From b784a464cb24baa00491f80d87aeabfe576888bc Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 6 Jan 2020 21:40:51 -0600
Subject: [PATCH 01/36] Create README.md

---
 README.md | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 README.md

diff --git a/README.md b/README.md
new file mode 100644
index 0000000..d7f31b5
--- /dev/null
+++ b/README.md
@@ -0,0 +1,66 @@
+# Welcome to University Of Chicago's Python For Analytics_ course
+This course introduces the python language of programming to students who are preparing to study analytics and data science
+
+Please follow the instructions below to get your computer ready for this class.
+
+_Note Mac users: Once software is downloaded, if you double click to launch it, you may get permission errors. Try to right click on the downloaded software, pick "open" and continue. (Apple is trying to protect you from accidentally starting malware/virus)_
+
+## Install Python (anaconda distribution)
+Please install Python 3.x from this website: https://www.anaconda.com/distribution/
+(do not install 2.7)
+
+Mac users:
+Accept all default prompts
+
+Windows users:
+Accept all default prompts, **except** "Add Anaconda to my PATH envrionment variable." Make sure this is checked.
+
+Anaconda's distribution of Python is widely used in the industry, particularly among data scientists. This distribution makes it easy to use many libraries and packages for data analysis, building models, visualization, etc.
+
+#### Aditional steps:
+Execute these statements at the terminal (Windows users should use Anaconda Prompt)
+  - `jupyter contrib nbextension install --user`
+  - `jupyter nbextensions_configurator enable --user`
+
+Once installed, please start jupyter notebook and execute code provided below
+1. Start `Anaconda Navigator` and click `Launch` on the panel labeled `Jupyter Notebook`
+2. Create new notebook from the web interface
+3. Execute this code:
+```
+%%timeit
+sum(range(1_000_000))
+```
+4. Execute this code:
+```
+from psutil import virtual_memory, disk_usage, cpu_count, os
+
+bytes_in_gb = 1024**3
+
+print("Memory:\t",round(virtual_memory().total/bytes_in_gb,4), "Gigabytes")
+print("Disk:\t",round(disk_usage(os.path.abspath(os.sep)).total/bytes_in_gb,4), "Gigabytes")
+print("CPUs:\t", cpu_count())
+```
+
+## Install all packages in environment.yml
+Available at https://github.com/falconair/ProgrammingForAnalytics/blob/master/environment.yml
+Also available in the repository you cloned in the previous step.
+
+
+## Install Git and Git Bash [Optional on day 1]
+Please intall Git, a version control sotware, from this website: https://git-scm.com/downloads (you are ok to use default settings)
+
+Note that this is a command-line tool. Once installed, you may not see a new icon to click. We will install a Desktop client to remedy this.
+
+Although we don't make heavy use of version control, you will be introduced to the concept. Installing Git also installs "Git Bash," and comand line environment which simulates Unix/Linux. We will do several exercises which will require this environment.
+
+#### Additional steps:
+1. Install a _Graphical_ interface to Git from this website: https://desktop.github.com/
+2. [Windows users only] Create or edit a file called `.profile` in your home directory (c:\Users\your_username\\.profile) and add a new line containing the word `cd`
+
+#### Additional steps:
+Install Python extensions from https://marketplace.visualstudio.com/items?itemName=ms-python.python (visit that page and click "Install")
+
+## Clone this repository [Optional on day 1]
+1. Visit this web page: https://github.com/falconair/ProgrammingForAnalytics
+2. Click "Clone or download" and pick the "Download ZIP" option (unless you already have a GitHub account)
+

From bf5b521b4615215671a5838a4d99cbd7d038d0cd Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 6 Jan 2020 21:41:45 -0600
Subject: [PATCH 02/36] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index d7f31b5..e982e48 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Welcome to University Of Chicago's Python For Analytics_ course
+# Welcome to University Of Chicago's Python For Analytics
 This course introduces the python language of programming to students who are preparing to study analytics and data science
 
 Please follow the instructions below to get your computer ready for this class.
@@ -7,7 +7,7 @@ _Note Mac users: Once software is downloaded, if you double click to launch it,
 
 ## Install Python (anaconda distribution)
 Please install Python 3.x from this website: https://www.anaconda.com/distribution/
-(do not install 2.7)
+(do not install 2.7, it will be discontinued soon.) 
 
 Mac users:
 Accept all default prompts

From cba34bf9b45dea1fb2e6d331fb25b536ddd50074 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 7 Jan 2020 14:22:58 -0600
Subject: [PATCH 03/36] Update README.md

---
 README.md | 13 ++-----------
 1 file changed, 2 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index e982e48..0b0ba1c 100644
--- a/README.md
+++ b/README.md
@@ -41,26 +41,17 @@ print("Disk:\t",round(disk_usage(os.path.abspath(os.sep)).total/bytes_in_gb,4),
 print("CPUs:\t", cpu_count())
 ```
 
-## Install all packages in environment.yml
-Available at https://github.com/falconair/ProgrammingForAnalytics/blob/master/environment.yml
-Also available in the repository you cloned in the previous step.
-
-
-## Install Git and Git Bash [Optional on day 1]
+## Install Git
 Please intall Git, a version control sotware, from this website: https://git-scm.com/downloads (you are ok to use default settings)
 
 Note that this is a command-line tool. Once installed, you may not see a new icon to click. We will install a Desktop client to remedy this.
 
 Although we don't make heavy use of version control, you will be introduced to the concept. Installing Git also installs "Git Bash," and comand line environment which simulates Unix/Linux. We will do several exercises which will require this environment.
 
-#### Additional steps:
-1. Install a _Graphical_ interface to Git from this website: https://desktop.github.com/
-2. [Windows users only] Create or edit a file called `.profile` in your home directory (c:\Users\your_username\\.profile) and add a new line containing the word `cd`
-
 #### Additional steps:
 Install Python extensions from https://marketplace.visualstudio.com/items?itemName=ms-python.python (visit that page and click "Install")
 
 ## Clone this repository [Optional on day 1]
-1. Visit this web page: https://github.com/falconair/ProgrammingForAnalytics
+1. Visit this web page: https://github.com/ravescovi/python_for_analytics
 2. Click "Clone or download" and pick the "Download ZIP" option (unless you already have a GitHub account)
 

From 91c4ebcbe89ef433e79edb6d470f562a60887eef Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Fri, 10 Jan 2020 13:34:50 -0600
Subject: [PATCH 04/36] Assigment1

---
 ...ontainer, Iteration Tools-checkpoint.ipynb | 2189 +++++++++++++++++
 ...cture 1 - Container, Iteration Tools.ipynb |  534 ++--
 presentation/MSCA_37014_v1.pptx               |  Bin 0 -> 1331506 bytes
 tests/NAME_SURNAME_ASSIGN_1.ipynb             |  509 ++++
 4 files changed, 3033 insertions(+), 199 deletions(-)
 create mode 100644 lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb
 create mode 100644 presentation/MSCA_37014_v1.pptx
 create mode 100644 tests/NAME_SURNAME_ASSIGN_1.ipynb

diff --git a/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb
new file mode 100644
index 0000000..7a89d5c
--- /dev/null
+++ b/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb	
@@ -0,0 +1,2189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note\n",
+    "* throughout the notebooks you will see blue font.  that is your chance to pause and play around with some of the concepts we've just learned\n",
+    "* some will be easier, some harder and the appropriate amount of time will be given\n",
+    "* use this as an example to test concepts\n",
+    "* don't worry about you're code being perfect or getting errors, that's how you'll learn the mechanics of it\n",
+    "* be adventorous so you can learn how powerful the concepts can be"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assumptions\n",
+    "* I will asssume you know some basic python\n",
+    "    * syntax\n",
+    "    * conditionals\n",
+    "    * iteration\n",
+    "    * user defined functions\n",
+    "* though in the first few lectures as they come up I will quickly review them"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lecture 1\n",
+    "* review the basics of python\n",
+    "* python containers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### IDEs\n",
+    "* Anaconda (https://www.anaconda.com/distribution/)\n",
+    "* Jupyter Notebooks\n",
+    "* Spyder\n",
+    "* Sublime (https://www.sublimetext.com/)\n",
+    "* PyCharm (https://www.jetbrains.com/pycharm/download/#section=mac)\n",
+    "* Atom (https://atom.io/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lists\n",
+    "* array, collection, of unordered items of any data type\n",
+    "* can be changed (mutable)\n",
+    "* can have duplicates\n",
+    "* can put anything in them\n",
+    "* created using brackets [ ]\n",
+    "* concat a list of dataframes in pandas\n",
+    "* https://docs.python.org/3/tutorial/datastructures.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = [\"A\", 1, 2, 2.4, \"B\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(a, list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__add__',\n",
+       " '__class__',\n",
+       " '__contains__',\n",
+       " '__delattr__',\n",
+       " '__delitem__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__iadd__',\n",
+       " '__imul__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__mul__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__reversed__',\n",
+       " '__rmul__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'append',\n",
+       " 'clear',\n",
+       " 'copy',\n",
+       " 'count',\n",
+       " 'extend',\n",
+       " 'index',\n",
+       " 'insert',\n",
+       " 'pop',\n",
+       " 'remove',\n",
+       " 'reverse',\n",
+       " 'sort']"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### adding items to a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a.append(5)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### adding item to specific position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['new_item', 'new_item', 'new_item', 'new_item', 1, 2, 3, 4, 5, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# list.insert(index, element)\n",
+    "a.insert(0, \"new_item\")\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### size of list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### accessing specific elements using bracket notation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "new_item\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# last item of list\n",
+    "print(a[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['new_item', 'new_item', 'new_item', 'new_item', 1, 2, 3, 4, 5, 5]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['new_item', 'new_item']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# return 4th and 5th indexed item, not inclusive of the 6th\n",
+    "a[4:6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a list and</h3>\n",
+    "<p style=\"color:blue\">- find the len</p>\n",
+    "<p style=\"color:blue\">- append an item</p>\n",
+    "<p style=\"color:blue\">- select an item using the index value</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a nested list</h3>\n",
+    "<p style=\"color:blue\">- access the first item of the first neseted list element"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### map\n",
+    "* map lets you apply a function to each element of a python list\n",
+    "* map(func, list)\n",
+    "* returns map object which we can instantly convert to a list\n",
+    "* https://docs.python.org/3/library/functions.html#map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[3, 3, 3]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [-3,-3,-3]\n",
+    "list(map(abs, a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### filter\n",
+    "* applies a function to each element of a list and returns a True or False, then subsets the list\n",
+    "* filter(function, list)\n",
+    "* https://docs.python.org/3/library/functions.html#filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def my_func(x):\n",
+    "    return x > 0\n",
+    "\n",
+    "a = [1,2,3,-4]\n",
+    "\n",
+    "list(filter(my_func, a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a list of random numbers</h3>\n",
+    "<p style=\"color:blue\">- filter for items above 2</p>\n",
+    "<p style=\"color:blue\">- map the sqrt function from math to it to each element</p>\n",
+    "<p style=\"color:blue\">- use \"from math import sqrt\"</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### find the index of a given item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [\"a\", \"b\"]\n",
+    "a.index(\"a\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [\"a\", \"b\", \"a\"]\n",
+    "a.index(\"a\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tuple\n",
+    "* ordered and unchangable array\n",
+    "* ( ) parenths signal tuple\n",
+    "* https://docs.python.org/2/tutorial/datastructures.html#tuples-and-sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = (1,2,3,\"a\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(a, tuple)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__add__',\n",
+       " '__class__',\n",
+       " '__contains__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getnewargs__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__mul__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rmul__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'count',\n",
+       " 'index']"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### access  elements using bracket notation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = (1,2,2,3,4,5,5)\n",
+    "a.count(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.index(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set\n",
+    "* unique elements\n",
+    "* unordered\n",
+    "* mixed types\n",
+    "* { } curly brackets indicate set\n",
+    "* could also use set()\n",
+    "* if we say x = { } this makes an empty dictionary not an empty set\n",
+    "* in such case we'd use set()\n",
+    "* https://docs.python.org/2/tutorial/datastructures.html#sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5}"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = {1,2,3,4,5,5,5,5,5}\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(a, set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__and__',\n",
+       " '__class__',\n",
+       " '__contains__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__iand__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__ior__',\n",
+       " '__isub__',\n",
+       " '__iter__',\n",
+       " '__ixor__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__or__',\n",
+       " '__rand__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__ror__',\n",
+       " '__rsub__',\n",
+       " '__rxor__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " '__xor__',\n",
+       " 'add',\n",
+       " 'clear',\n",
+       " 'copy',\n",
+       " 'difference',\n",
+       " 'difference_update',\n",
+       " 'discard',\n",
+       " 'intersection',\n",
+       " 'intersection_update',\n",
+       " 'isdisjoint',\n",
+       " 'issubset',\n",
+       " 'issuperset',\n",
+       " 'pop',\n",
+       " 'remove',\n",
+       " 'symmetric_difference',\n",
+       " 'symmetric_difference_update',\n",
+       " 'union',\n",
+       " 'update']"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 4}"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = {1,2,3,4}\n",
+    "b = (1,4,5,6)\n",
+    "a.intersection(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### lists can be converted to sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a', 'b'}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [\"a\", \"b\", \"a\"]\n",
+    "set(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_set = set([1,1,1,2,2,2,3,3,3])\n",
+    "my_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = {}\n",
+    "type(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a list with some repeating elements</h3>\n",
+    "<p style=\"color:blue\">- find the length of the list</p>\n",
+    "<p style=\"color:blue\">- convert it to a set</p>\n",
+    "<p style=\"color:blue\">- find the size of the set</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dict\n",
+    "* key value pairs\n",
+    "* unordered\n",
+    "* no repeating keys\n",
+    "* curly brakets, though you add key value pairs, seperated by a colon\n",
+    "* https://docs.python.org/2/tutorial/datastructures.html#dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dict = {\n",
+    "    \"a\": 1,\n",
+    "    \"b\": 2,\n",
+    "    \"c\": 3\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(my_dict, dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(my_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__contains__',\n",
+       " '__delattr__',\n",
+       " '__delitem__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'clear',\n",
+       " 'copy',\n",
+       " 'fromkeys',\n",
+       " 'get',\n",
+       " 'items',\n",
+       " 'keys',\n",
+       " 'pop',\n",
+       " 'popitem',\n",
+       " 'setdefault',\n",
+       " 'update',\n",
+       " 'values']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(my_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### grab the keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['a', 'b', 'c'])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### grab the values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_values([1, 2, 3])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dict.values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### iterate a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a\n",
+      "b\n",
+      "c\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in my_dict:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_items([('a', 1), ('b', 2), ('c', 3)])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list of tuples representing \n",
+    "my_dict.items()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a --> 1\n",
+      "b --> 2\n",
+      "c --> 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k,v in my_dict.items():\n",
+    "    print(k, \"-->\", v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### accessing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dict[\"a\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### don't repeat keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dict = {\n",
+    "    1:\"Brian\",\n",
+    "    2:\"Jane\",\n",
+    "    2:\"John\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'Brian', 2: 'John'}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### nested dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is basically json\n",
+    "my_dict = {\n",
+    "    \n",
+    "    1:{\"first_name\":\"Brian\", \"last_name\":\"Craft\"},\n",
+    "    2:{\"first_name\":\"Jane\", \"last_name\":\"Doe\"},\n",
+    "    3:{\"first_name\":\"John\", \"last_name\":\"Doe\"}\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'first_name': 'Brian', 'last_name': 'Craft'}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dict[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(my_dict[1], dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(my_dict[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Brian'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dict[1][\"first_name\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a dictionary</h3>\n",
+    "<p style=\"color:blue\">- access elements with the bracket notation</p>\n",
+    "<p style=\"color:blue\">- iterate over the dictionary using .items()</p>\n",
+    "<p style=\"color:blue\">- get the keys from the dictionary using .keys()</p>\n",
+    "<p style=\"color:blue\">- get the values from the dictionary using .values?()</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Eunumerate\n",
+    "* add an index value to your loops, or counter\n",
+    "* you can use the idx%1000 == 0 which returns the remainder of idx/1000 (modulus or clock math) which can be used as a way to monitor a time intensive loop\n",
+    "* https://docs.python.org/2/tutorial/datastructures.html#looping-techniques"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<enumerate at 0x7f9c682be5f0>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [\"a\", \"b\", \"c\"]\n",
+    "enumerate(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 --> a\n",
+      "1 --> b\n",
+      "2 --> c\n"
+     ]
+    }
+   ],
+   "source": [
+    "for idx, i in enumerate(a):\n",
+    "    print(idx,\"-->\", i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Zip\n",
+    "* Mash iterables together to make nested tuples\n",
+    "* you can use zip to mash together keys and values and put them into a dictionary\n",
+    "* https://docs.python.org/2/library/functions.html#zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3]\n",
+    "b = [\"a\", \"b\", \"c\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x7f9c682c3a50>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zip(a,b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 'a'), (2, 'b'), (3, 'c')]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[x for x in zip(a,b)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 'a')\n",
+      "(2, 'b')\n",
+      "(3, 'c')\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in zip(a,b):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'tuple'>\n",
+      "<class 'tuple'>\n",
+      "<class 'tuple'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in zip(a,b):\n",
+    "    print(type(i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 'a')\n",
+      "(2, 'b')\n",
+      "(3, 'c')\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in zip(a,b):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in zip(a,b):\n",
+    "    print(i[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a\n",
+      "b\n",
+      "c\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in zip(a,b):\n",
+    "    print(i[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 'a', 'A'), (2, 'b', 'B'), (3, 'c', 'C')]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [1,2,3]\n",
+    "b = [\"a\", \"b\", \"c\"]\n",
+    "c = [\"A\", \"B\", \"C\"]\n",
+    "\n",
+    "list(zip(a,b,c))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Continue, Break and Pass\n",
+    "* break: chance to exit a loop when a condition is met\n",
+    "* continue: continue to the next iteration of the loop\n",
+    "* pass: do nothing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 is not greater then 4\n",
+      "1 is not greater then 4\n",
+      "2 is not greater then 4\n",
+      "3 is not greater then 4\n",
+      "4 is not greater then 4\n",
+      "5 is greater then 4 so we will break out loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(range(10))\n",
+    "for x in lst:\n",
+    "    if x > 4:\n",
+    "        print(\"{} is greater then 4 so we will break out loop\".format(x))\n",
+    "        break\n",
+    "    else:\n",
+    "        print(\"{} is not greater then 4\".format(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 is greater then 4\n",
+      "6 is greater then 4\n",
+      "7 is greater then 4\n",
+      "8 is greater then 4\n",
+      "9 is greater then 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(range(10))\n",
+    "for x in lst:\n",
+    "    if x > 4:\n",
+    "        print(\"{} is greater then 4\".format(x))\n",
+    "    else:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 is not greater then 4\n",
+      "1 is not greater then 4\n",
+      "2 is not greater then 4\n",
+      "3 is not greater then 4\n",
+      "4 is not greater then 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(range(10))\n",
+    "for x in lst:\n",
+    "    if x > 4:\n",
+    "        continue\n",
+    "    else:\n",
+    "        print(\"{} is not greater then 4\".format(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 is not greater then 4\n",
+      "print statement below\n",
+      "1 is not greater then 4\n",
+      "print statement below\n",
+      "2 is not greater then 4\n",
+      "print statement below\n",
+      "3 is not greater then 4\n",
+      "print statement below\n",
+      "4 is not greater then 4\n",
+      "print statement below\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(range(10))\n",
+    "\n",
+    "for x in lst:\n",
+    "    if x > 4:\n",
+    "        continue\n",
+    "    else:\n",
+    "        print(\"{} is not greater then 4\".format(x))\n",
+    "       \n",
+    "    print(\"print statement below\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 is not greater then 4\n",
+      "print statement below\n",
+      "1 is not greater then 4\n",
+      "print statement below\n",
+      "2 is not greater then 4\n",
+      "print statement below\n",
+      "3 is not greater then 4\n",
+      "print statement below\n",
+      "4 is not greater then 4\n",
+      "print statement below\n",
+      "print statement below\n",
+      "print statement below\n",
+      "print statement below\n",
+      "print statement below\n",
+      "print statement below\n"
+     ]
+    }
+   ],
+   "source": [
+    "lst = list(range(10))\n",
+    "\n",
+    "for x in lst:\n",
+    "    if x > 4:\n",
+    "        pass\n",
+    "    else:\n",
+    "        print(\"{} is not greater then 4\".format(x))\n",
+    "       \n",
+    "    print(\"print statement below\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Itertools\n",
+    "* tools for working with iterables\n",
+    "* additional building blocks\n",
+    "* chain pushes lists together\n",
+    "* https://docs.python.org/2/library/itertools.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### chain mashes nested lists into one"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [\n",
+    "    [\"a\", \"b\", \"c\"],\n",
+    "    [\"d\", \"e\", \"f\", \"b\"]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = list(chain.from_iterable(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'd', 'e', 'f', 'b']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### permutation\n",
+    "* https://www.mathsisfun.com/combinatorics/combinations-permutations.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import permutations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('a', 'b'), ('a', 'c'), ('b', 'a'), ('b', 'c'), ('c', 'a'), ('c', 'b')]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(permutations([\"a\",\"b\",\"c\"], 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('a', 'b', 'c'),\n",
+       " ('a', 'c', 'b'),\n",
+       " ('b', 'a', 'c'),\n",
+       " ('b', 'c', 'a'),\n",
+       " ('c', 'a', 'b'),\n",
+       " ('c', 'b', 'a')]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(permutations([\"a\",\"b\",\"c\"], 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### combinations\n",
+    "* https://www.mathsisfun.com/combinatorics/combinations-permutations.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import combinations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('a', 'b'), ('a', 'c'), ('b', 'c')]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(combinations([\"a\",\"b\",\"c\"], 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('a', 'b', 'c')]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(combinations([\"a\",\"b\",\"c\"], 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [[\"a\", \"b\"], [\"c\", \"d\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd')]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(product(*a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd')]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(product([\"a\", \"b\"],[\"c\", \"d\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Collections\n",
+    "* use counter to get frequencies of items in a list\n",
+    "* https://docs.python.org/2/library/collections.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counter = Counter(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'a': 1, 'b': 2, 'c': 1, 'd': 1, 'e': 1, 'f': 1})"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.Counter"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(counter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a 1\n",
+      "b 2\n",
+      "c 1\n",
+      "d 1\n",
+      "e 1\n",
+      "f 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k,v in counter.items():\n",
+    "    print(k,v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a list</h3>\n",
+    "<p style=\"color:blue\">- find all the combinations</p>\n",
+    "<p style=\"color:blue\">- find all the permutations</p>\n",
+    "<p style=\"color:blue\">- create a counter for the list to get item frequencies</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lectures/Lecture 1 - Container, Iteration Tools.ipynb b/lectures/Lecture 1 - Container, Iteration Tools.ipynb
index 5b04089..672dd99 100644
--- a/lectures/Lecture 1 - Container, Iteration Tools.ipynb	
+++ b/lectures/Lecture 1 - Container, Iteration Tools.ipynb	
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,36 +72,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['A', 1, 2, 2.4, 'B']\n",
+      "A\n",
+      "A is a letter\n"
+     ]
+    }
+   ],
    "source": [
-    "b = [\"A\", 1, 2, 2.4, \"B\"]"
+    "b = [\"A\", 1, 2, 2.4, \"B\"]\n",
+    "print (b)\n",
+    "print (b[0])\n",
+    "print (b[0] + \" is a letter\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n"
+     ]
     }
    ],
    "source": [
-    "isinstance(a, list)"
+    "print(isinstance(a, list))\n",
+    "print(isinstance(a, int))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -110,7 +129,7 @@
        "list"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -121,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 96,
    "metadata": {
     "scrolled": true
    },
@@ -177,7 +196,7 @@
        " 'sort']"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -195,14 +214,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1, 2, 3, 4, 5, 5]\n"
+      "[-3, -3, -3, 5]\n"
      ]
     }
    ],
@@ -220,14 +239,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['new_item', 'new_item', 'new_item', 'new_item', 1, 2, 3, 4, 5, 5]\n"
+      "['new_item', -3, -3, -3, 5]\n"
      ]
     }
    ],
@@ -246,14 +265,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10\n"
+      "4\n"
      ]
     }
    ],
@@ -270,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -312,16 +331,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['new_item', 'new_item', 'new_item', 'new_item', 1, 2, 3, 4, 5, 5]"
+       "['new_item', -3, -3, -3, 5]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -332,36 +351,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['new_item', 'new_item']"
+       "['new_item', -3]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a[0:2]"
+    "a[0:-1:2]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[1, 2]"
+       "[4, 5]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -389,6 +408,26 @@
     "<p style=\"color:blue\">- access the first item of the first neseted list element"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 3, 5, [2, 4, 6, 8]]\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = [1,3,5,[2,4,6,8]]\n",
+    "print(a)\n",
+    "print (len(a))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -402,23 +441,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[3, 3, 3]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "3\n",
+      "3\n"
+     ]
     }
    ],
    "source": [
     "a = [-3,-3,-3]\n",
-    "list(map(abs, a))"
+    "list(map(abs, a))\n",
+    "\n",
+    "for k in a:\n",
+    "    print(abs(a[k]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<map object at 0x0000027082FF3608>\n",
+      "[1, 1, 2, 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = [1,-1,2,-2]\n",
+    "c = map(abs,b)\n",
+    "print(c)\n",
+    "d = list(c)\n",
+    "print(d)"
    ]
   },
   {
@@ -433,27 +496,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, -4]\n",
+      "<filter object at 0x0000027083074AC8>\n"
+     ]
     }
    ],
    "source": [
     "def my_func(x):\n",
-    "    return x > 0\n",
+    "    return abs(x)\n",
     "\n",
     "a = [1,2,3,-4]\n",
     "\n",
-    "list(filter(my_func, a))"
+    "b = filter(my_func, a)\n",
+    "print (a)\n",
+    "print (b)"
    ]
   },
   {
@@ -466,6 +529,25 @@
     "<p style=\"color:blue\">- use \"from math import sqrt\"</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[27, 40, 18, 1, 22, 29, 8]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random \n",
+    "res = [random.randrange(1, 50, 1) for i in range(7)] \n",
+    "print (res)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -475,28 +557,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4]\n",
+      "[1.0, 1.4142135623730951, 1.7320508075688772, 2.0]\n"
+     ]
     }
    ],
    "source": [
-    "a = [\"a\", \"b\"]\n",
-    "a.index(\"a\")"
+    "from math import sqrt\n",
+    "a = [1,2,3,-4]\n",
+    "\n",
+    "a = list(map(abs,a))\n",
+    "print(a)\n",
+    "b = list(map(sqrt,a))\n",
+    "print(b)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -505,7 +597,7 @@
        "0"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -527,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -536,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -545,7 +637,7 @@
        "True"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -565,7 +657,7 @@
        "tuple"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -576,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 23,
    "metadata": {
     "scrolled": true
    },
@@ -619,7 +711,7 @@
        " 'index']"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -637,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -646,7 +738,7 @@
        "1"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -657,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -666,7 +758,7 @@
        "(2, 3)"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -686,7 +778,7 @@
        "2"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -707,7 +799,7 @@
        "0"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -733,7 +825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -742,19 +834,19 @@
        "{1, 2, 3, 4, 5}"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a = {1,2,3,4,5,5,5,5,5}\n",
+    "a = {1,2,3,4,4,5,5,5,5,5}\n",
     "a"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -763,7 +855,7 @@
        "True"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -774,7 +866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -783,7 +875,7 @@
        "set"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -794,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 31,
    "metadata": {
     "scrolled": true
    },
@@ -859,7 +951,7 @@
        " 'update']"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -870,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -879,7 +971,7 @@
        "{1, 4}"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -899,7 +991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -908,7 +1000,7 @@
        "{'a', 'b'}"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -920,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
@@ -929,7 +1021,7 @@
        "{1, 2, 3}"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -941,7 +1033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -950,7 +1042,7 @@
        "dict"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -984,20 +1076,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
     "my_dict = {\n",
-    "    \"a\": 1,\n",
-    "    \"b\": 2,\n",
-    "    \"c\": 3\n",
+    "    \"F_name\": 'Rafael',\n",
+    "    \"L_name\": 'Vescovi',\n",
+    "    \"Like_potatos\": 'fried'\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
@@ -1006,7 +1098,7 @@
        "True"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,7 +1109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
@@ -1026,7 +1118,7 @@
        "dict"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1037,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 148,
    "metadata": {
     "scrolled": true
    },
@@ -1087,7 +1179,7 @@
        " 'values']"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 148,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1105,16 +1197,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['a', 'b', 'c'])"
+       "dict_keys(['F_name', 'L_name', 'Like_potatos'])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1132,16 +1224,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_values([1, 2, 3])"
+       "dict_values(['Rafael', 'Vescovi', 'fried'])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1159,16 +1251,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "a\n",
-      "b\n",
-      "c\n"
+      "F_name\n",
+      "L_name\n",
+      "Like_potatos\n"
      ]
     }
    ],
@@ -1179,16 +1271,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_items([('a', 1), ('b', 2), ('c', 3)])"
+       "dict_items([('F_name', 'Rafael'), ('L_name', 'Vescovi'), ('Like_potatos', 'fried')])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1200,16 +1292,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "a --> 1\n",
-      "b --> 2\n",
-      "c --> 3\n"
+      "F_name --> Rafael\n",
+      "L_name --> Vescovi\n",
+      "Like_potatos --> fried\n"
      ]
     }
    ],
@@ -1227,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1236,7 +1328,7 @@
        "1"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1254,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1267,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1276,7 +1368,7 @@
        "{1: 'Brian', 2: 'John'}"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1294,31 +1386,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [],
    "source": [
     "# this is basically json\n",
     "my_dict = {\n",
     "    \n",
-    "    1:{\"first_name\":\"Brian\", \"last_name\":\"Craft\"},\n",
-    "    2:{\"first_name\":\"Jane\", \"last_name\":\"Doe\"},\n",
-    "    3:{\"first_name\":\"John\", \"last_name\":\"Doe\"}\n",
+    "    0:{\"first_name\":\"Brian\", \"last_name\":\"Craft\"},\n",
+    "    1:{\"first_name\":\"Jane\", \"last_name\":\"Doe\"},\n",
+    "    2:{\"first_name\":\"John\", \"last_name\":\"Doe\"}\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'first_name': 'Brian', 'last_name': 'Craft'}"
+       "{'first_name': 'Jane', 'last_name': 'Doe'}"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1329,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [
     {
@@ -1338,7 +1430,7 @@
        "True"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1349,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [
     {
@@ -1358,7 +1450,7 @@
        "dict"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1369,16 +1461,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Brian'"
+       "'Jane'"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1410,16 +1502,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<enumerate at 0x7f9c682be5f0>"
+       "<enumerate at 0x27082fc9908>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1431,7 +1523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [
     {
@@ -1440,13 +1532,15 @@
      "text": [
       "0 --> a\n",
       "1 --> b\n",
-      "2 --> c\n"
+      "2 --> a\n",
+      "<enumerate object at 0x00000270830716D8>\n"
      ]
     }
    ],
    "source": [
     "for idx, i in enumerate(a):\n",
-    "    print(idx,\"-->\", i)"
+    "    print(idx,\"-->\", i)\n",
+    "    \n"
    ]
   },
   {
@@ -1461,7 +1555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1471,16 +1565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<zip at 0x7f9c682c3a50>"
+       "<zip at 0x27082fdb588>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1491,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1500,7 +1594,7 @@
        "[(1, 'a'), (2, 'b'), (3, 'c')]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1511,7 +1605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1531,7 +1625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1551,7 +1645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1571,7 +1665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1591,7 +1685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1611,7 +1705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1620,7 +1714,7 @@
        "[(1, 'a', 'A'), (2, 'b', 'B'), (3, 'c', 'C')]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1645,7 +1739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1673,7 +1767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1699,7 +1793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1725,7 +1819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1759,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -1816,7 +1910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1825,7 +1919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1837,7 +1931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1846,7 +1940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1855,7 +1949,7 @@
        "['a', 'b', 'c', 'd', 'e', 'f', 'b']"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1874,7 +1968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1883,7 +1977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 74,
    "metadata": {
     "scrolled": true
    },
@@ -1894,7 +1988,7 @@
        "[('a', 'b'), ('a', 'c'), ('b', 'a'), ('b', 'c'), ('c', 'a'), ('c', 'b')]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1905,7 +1999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -1919,7 +2013,7 @@
        " ('c', 'b', 'a')]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1938,7 +2032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1947,7 +2041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -1956,7 +2050,7 @@
        "[('a', 'b'), ('a', 'c'), ('b', 'c')]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1967,7 +2061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -1976,7 +2070,7 @@
        "[('a', 'b', 'c')]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1994,7 +2088,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2003,7 +2097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2012,7 +2106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -2021,7 +2115,7 @@
        "[('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd')]"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2032,7 +2126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -2041,7 +2135,7 @@
        "[('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd')]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2061,7 +2155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2070,7 +2164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2079,7 +2173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -2088,7 +2182,7 @@
        "Counter({'a': 1, 'b': 2, 'c': 1, 'd': 1, 'e': 1, 'f': 1})"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2099,7 +2193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -2108,7 +2202,7 @@
        "collections.Counter"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2119,7 +2213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -2150,6 +2244,48 @@
     "<p style=\"color:blue\">- create a counter for the list to get item frequencies</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/presentation/MSCA_37014_v1.pptx b/presentation/MSCA_37014_v1.pptx
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diff --git a/tests/NAME_SURNAME_ASSIGN_1.ipynb b/tests/NAME_SURNAME_ASSIGN_1.ipynb
new file mode 100644
index 0000000..8af326f
--- /dev/null
+++ b/tests/NAME_SURNAME_ASSIGN_1.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 1\n",
+    "* Due 1/31 by 5pm\n",
+    "* Topics: containers, udfs, comprehensions, error handling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instructions\n",
+    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_1\n",
+    "* Finish the test on this notebook\n",
+    "* Download your notebook as a PDF\n",
+    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_1.pdf and RAFAEL_VESCOVI_ASSIGN_1.ipynb)\n",
+    "* Show the output of your code working on the sample data, like below.  \n",
+    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
+    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
+    "* You have to show your work (code).\n",
+    "* You will be graded:\n",
+    "    * The functionality of your code (Does it do what it was meant to)\n",
+    "    * Showing the output on the sample data provided\n",
+    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
+    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
+    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
+    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample Question\n",
+    "* Create a UDF that adds 2 to a number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add_func(x):\n",
+    "    return(x+2)\n",
+    "\n",
+    "number = add_func(a)\n",
+    "print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1\n",
+    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
+    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
+    "    * make use of zip and comprehension to make the dictionary\n",
+    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [5,7]\n",
+    "# b = [\"x\", \"z\"]\n",
+    "# return {5:\"x\", 7:\"z\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7,8]\n",
+    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2\n",
+    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
+    "* The function should return 4 things"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3\n",
+    "* Map a function to the below list that\n",
+    "    * returns the remainder of each number divided by 3 (52/3 = 17 with a remainder of 1, we want the 1 to be returned)\n",
+    "    * use lambdas for the mapping\n",
+    "    * hint, look at modulus (%) in python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst = list(range(100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4\n",
+    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
+    "* example below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lst = [1,2]\n",
+    "# dict = {1:\"a\", 2:\"b\"}\n",
+    "# new_lst = [\"a\",\"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = list(range(5))\n",
+    "dct = {\n",
+    "    0:\"a\",\n",
+    "    1:\"b\",\n",
+    "    2:\"c\",\n",
+    "    3:\"d\",\n",
+    "    4:\"e\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 5\n",
+    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
+    "* use comprehension to perform the squaring of each item\n",
+    "* hint, this should be doable in one line using args"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_func(1,2,3)\n",
+    "    # do some stuff\n",
+    "    \n",
+    "# return_lst = [1,4,9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for this pass in the numberse 1,2,3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 6\n",
+    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [1,2]\n",
+    "# b = [2,3]\n",
+    "# return_lst = [2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7]\n",
+    "b = [2,3,4,6,8,10,11,21]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 7\n",
+    "* write a function that takes a list of numbers and filters for odds only\n",
+    "* use the filter method and a lambda as the function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_lst = [1,2]\n",
+    "# new_lst = [1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 8\n",
+    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
+    "* Use the function to map it to a list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_lst = [1,-2,1]\n",
+    "# using a comprehension return [3,4,3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,-2,1,8,-10,15,-12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 9\n",
+    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
+    "* note you may not use any third party modules.  use  comprehensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [1,2]\n",
+    "# b = [3,1]\n",
+    "# distance = ~2.23"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,1]\n",
+    "b = [3,1,2,4,3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 10\n",
+    "* write a function that takes a list, sorts it and finds the middle value.  \n",
+    "* use error handling to check the list is odd in length\n",
+    "* if it's even, return an error message\n",
+    "* feel free to use a try/except or assert\n",
+    "* show the output run on both lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [4,6,1]\n",
+    "# sort = [1,4,6]\n",
+    "# middle val = 4\n",
+    "# if even number in size, return error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
+    "b = [4,6,1,3,12,41,4,1,24,12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 11\n",
+    "* Make a generator that parses the list of transaction data\n",
+    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
+    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
+    "* make sure to skip the header somehow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transactions = [A, item_a], [A, item_b]\n",
+    "# dict = {A:[item_a, item_b]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transactions = [\n",
+    "    [\"user_id|item_id\"],\n",
+    "    [\"A|item_a\"],\n",
+    "    [\"B|item_a\"],\n",
+    "    [\"C|item_a\"],\n",
+    "    [\"C|item_b\"],\n",
+    "    [\"C|item_c\"],\n",
+    "    [\"B|item_c\"],\n",
+    "    [\"D|item_b\"],\n",
+    "    [\"D|item_b\"]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 12\n",
+    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
+    "* Use error handling of some kind if the lists are not of equal length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should return [True, True, True, False, False]\n",
+    "a = [1,2,3,4,5]\n",
+    "b = [1,2,3,5,6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 13\n",
+    "Briefly explain the use of the following words in python:\n",
+    "* assert\n",
+    "* raise\n",
+    "* yield\n",
+    "* def\n",
+    "* lambda\n",
+    "* return\n",
+    "* map\n",
+    "* filter\n",
+    "* try\n",
+    "* except\n",
+    "* continue\n",
+    "* pass\n",
+    "* break\n",
+    "* while\n",
+    "* do (in the context of a while loop)\n",
+    "* for"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 14\n",
+    "Briefly describe the below:\n",
+    "* list\n",
+    "* tuple\n",
+    "* dictionary\n",
+    "* set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 15\n",
+    "* What two datatypes can be declared with { }?\n",
+    "* What two datatypes can be declared with ( )?\n",
+    "* Provide examples of each of the 4 above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 16\n",
+    "Describe what a generator is and how it differs from a list or tuple."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 17 \n",
+    "Show three ways of attaching a list to itself (one of then should use the function append) . \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 9ffdcd9e7256e14b2ebeebd42582ac38a8e679fd Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 3 Feb 2020 13:18:53 -0600
Subject: [PATCH 05/36] updates

---
 tests/NAME_SURNAME_ASSIGN_1.ipynb | 509 ------------------------------
 1 file changed, 509 deletions(-)
 delete mode 100644 tests/NAME_SURNAME_ASSIGN_1.ipynb

diff --git a/tests/NAME_SURNAME_ASSIGN_1.ipynb b/tests/NAME_SURNAME_ASSIGN_1.ipynb
deleted file mode 100644
index 8af326f..0000000
--- a/tests/NAME_SURNAME_ASSIGN_1.ipynb
+++ /dev/null
@@ -1,509 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assignment 1\n",
-    "* Due 1/31 by 5pm\n",
-    "* Topics: containers, udfs, comprehensions, error handling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_1\n",
-    "* Finish the test on this notebook\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_1.pdf and RAFAEL_VESCOVI_ASSIGN_1.ipynb)\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
-    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
-    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sample Question\n",
-    "* Create a UDF that adds 2 to a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add_func(x):\n",
-    "    return(x+2)\n",
-    "\n",
-    "number = add_func(a)\n",
-    "print(number)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 1\n",
-    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
-    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
-    "    * make use of zip and comprehension to make the dictionary\n",
-    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [5,7]\n",
-    "# b = [\"x\", \"z\"]\n",
-    "# return {5:\"x\", 7:\"z\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8]\n",
-    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 2\n",
-    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
-    "* The function should return 4 things"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 3\n",
-    "* Map a function to the below list that\n",
-    "    * returns the remainder of each number divided by 3 (52/3 = 17 with a remainder of 1, we want the 1 to be returned)\n",
-    "    * use lambdas for the mapping\n",
-    "    * hint, look at modulus (%) in python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst = list(range(100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 4\n",
-    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
-    "* example below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lst = [1,2]\n",
-    "# dict = {1:\"a\", 2:\"b\"}\n",
-    "# new_lst = [\"a\",\"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = list(range(5))\n",
-    "dct = {\n",
-    "    0:\"a\",\n",
-    "    1:\"b\",\n",
-    "    2:\"c\",\n",
-    "    3:\"d\",\n",
-    "    4:\"e\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 5\n",
-    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
-    "* use comprehension to perform the squaring of each item\n",
-    "* hint, this should be doable in one line using args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_func(1,2,3)\n",
-    "    # do some stuff\n",
-    "    \n",
-    "# return_lst = [1,4,9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for this pass in the numberse 1,2,3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 6\n",
-    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [2,3]\n",
-    "# return_lst = [2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7]\n",
-    "b = [2,3,4,6,8,10,11,21]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 7\n",
-    "* write a function that takes a list of numbers and filters for odds only\n",
-    "* use the filter method and a lambda as the function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,2]\n",
-    "# new_lst = [1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 8\n",
-    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
-    "* Use the function to map it to a list comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,-2,1]\n",
-    "# using a comprehension return [3,4,3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,-2,1,8,-10,15,-12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 9\n",
-    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
-    "* note you may not use any third party modules.  use  comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [3,1]\n",
-    "# distance = ~2.23"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,1]\n",
-    "b = [3,1,2,4,3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 10\n",
-    "* write a function that takes a list, sorts it and finds the middle value.  \n",
-    "* use error handling to check the list is odd in length\n",
-    "* if it's even, return an error message\n",
-    "* feel free to use a try/except or assert\n",
-    "* show the output run on both lists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [4,6,1]\n",
-    "# sort = [1,4,6]\n",
-    "# middle val = 4\n",
-    "# if even number in size, return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
-    "b = [4,6,1,3,12,41,4,1,24,12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 11\n",
-    "* Make a generator that parses the list of transaction data\n",
-    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
-    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
-    "* make sure to skip the header somehow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# transactions = [A, item_a], [A, item_b]\n",
-    "# dict = {A:[item_a, item_b]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transactions = [\n",
-    "    [\"user_id|item_id\"],\n",
-    "    [\"A|item_a\"],\n",
-    "    [\"B|item_a\"],\n",
-    "    [\"C|item_a\"],\n",
-    "    [\"C|item_b\"],\n",
-    "    [\"C|item_c\"],\n",
-    "    [\"B|item_c\"],\n",
-    "    [\"D|item_b\"],\n",
-    "    [\"D|item_b\"]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 12\n",
-    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
-    "* Use error handling of some kind if the lists are not of equal length."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# should return [True, True, True, False, False]\n",
-    "a = [1,2,3,4,5]\n",
-    "b = [1,2,3,5,6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 13\n",
-    "Briefly explain the use of the following words in python:\n",
-    "* assert\n",
-    "* raise\n",
-    "* yield\n",
-    "* def\n",
-    "* lambda\n",
-    "* return\n",
-    "* map\n",
-    "* filter\n",
-    "* try\n",
-    "* except\n",
-    "* continue\n",
-    "* pass\n",
-    "* break\n",
-    "* while\n",
-    "* do (in the context of a while loop)\n",
-    "* for"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 14\n",
-    "Briefly describe the below:\n",
-    "* list\n",
-    "* tuple\n",
-    "* dictionary\n",
-    "* set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 15\n",
-    "* What two datatypes can be declared with { }?\n",
-    "* What two datatypes can be declared with ( )?\n",
-    "* Provide examples of each of the 4 above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 16\n",
-    "Describe what a generator is and how it differs from a list or tuple."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 17 \n",
-    "Show three ways of attaching a list to itself (one of then should use the function append) . \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 4a5da62a118f15a3ee11d448ca984ebd2d2cc859 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 3 Feb 2020 13:18:57 -0600
Subject: [PATCH 06/36] updates

---
 assigments/NAME_SURNAME_ASSIGN_1.ipynb        | 509 ++++++++++++++++++
 .../Challenge 1 - N Queens-checkpoint.ipynb   |   6 +
 ...enge 2 - Face Recognition-checkpoint.ipynb |   6 +
 .../Challenge 3 - ImageNet-checkpoint.ipynb   |   6 +
 challenges/Challenge 1 - N Queens.ipynb       |  55 ++
 .../Challenge 2 - Face Recognition.ipynb      |  37 ++
 challenges/Challenge 3 - ImageNet.ipynb       |  37 ++
 7 files changed, 656 insertions(+)
 create mode 100644 assigments/NAME_SURNAME_ASSIGN_1.ipynb
 create mode 100644 challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb
 create mode 100644 challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb
 create mode 100644 challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb
 create mode 100644 challenges/Challenge 1 - N Queens.ipynb
 create mode 100644 challenges/Challenge 2 - Face Recognition.ipynb
 create mode 100644 challenges/Challenge 3 - ImageNet.ipynb

diff --git a/assigments/NAME_SURNAME_ASSIGN_1.ipynb b/assigments/NAME_SURNAME_ASSIGN_1.ipynb
new file mode 100644
index 0000000..8af326f
--- /dev/null
+++ b/assigments/NAME_SURNAME_ASSIGN_1.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 1\n",
+    "* Due 1/31 by 5pm\n",
+    "* Topics: containers, udfs, comprehensions, error handling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instructions\n",
+    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_1\n",
+    "* Finish the test on this notebook\n",
+    "* Download your notebook as a PDF\n",
+    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_1.pdf and RAFAEL_VESCOVI_ASSIGN_1.ipynb)\n",
+    "* Show the output of your code working on the sample data, like below.  \n",
+    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
+    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
+    "* You have to show your work (code).\n",
+    "* You will be graded:\n",
+    "    * The functionality of your code (Does it do what it was meant to)\n",
+    "    * Showing the output on the sample data provided\n",
+    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
+    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
+    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
+    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample Question\n",
+    "* Create a UDF that adds 2 to a number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add_func(x):\n",
+    "    return(x+2)\n",
+    "\n",
+    "number = add_func(a)\n",
+    "print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1\n",
+    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
+    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
+    "    * make use of zip and comprehension to make the dictionary\n",
+    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [5,7]\n",
+    "# b = [\"x\", \"z\"]\n",
+    "# return {5:\"x\", 7:\"z\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7,8]\n",
+    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2\n",
+    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
+    "* The function should return 4 things"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3\n",
+    "* Map a function to the below list that\n",
+    "    * returns the remainder of each number divided by 3 (52/3 = 17 with a remainder of 1, we want the 1 to be returned)\n",
+    "    * use lambdas for the mapping\n",
+    "    * hint, look at modulus (%) in python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst = list(range(100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4\n",
+    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
+    "* example below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lst = [1,2]\n",
+    "# dict = {1:\"a\", 2:\"b\"}\n",
+    "# new_lst = [\"a\",\"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = list(range(5))\n",
+    "dct = {\n",
+    "    0:\"a\",\n",
+    "    1:\"b\",\n",
+    "    2:\"c\",\n",
+    "    3:\"d\",\n",
+    "    4:\"e\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 5\n",
+    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
+    "* use comprehension to perform the squaring of each item\n",
+    "* hint, this should be doable in one line using args"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_func(1,2,3)\n",
+    "    # do some stuff\n",
+    "    \n",
+    "# return_lst = [1,4,9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for this pass in the numberse 1,2,3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 6\n",
+    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [1,2]\n",
+    "# b = [2,3]\n",
+    "# return_lst = [2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7]\n",
+    "b = [2,3,4,6,8,10,11,21]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 7\n",
+    "* write a function that takes a list of numbers and filters for odds only\n",
+    "* use the filter method and a lambda as the function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_lst = [1,2]\n",
+    "# new_lst = [1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 8\n",
+    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
+    "* Use the function to map it to a list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_lst = [1,-2,1]\n",
+    "# using a comprehension return [3,4,3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,-2,1,8,-10,15,-12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 9\n",
+    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
+    "* note you may not use any third party modules.  use  comprehensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [1,2]\n",
+    "# b = [3,1]\n",
+    "# distance = ~2.23"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,1]\n",
+    "b = [3,1,2,4,3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 10\n",
+    "* write a function that takes a list, sorts it and finds the middle value.  \n",
+    "* use error handling to check the list is odd in length\n",
+    "* if it's even, return an error message\n",
+    "* feel free to use a try/except or assert\n",
+    "* show the output run on both lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [4,6,1]\n",
+    "# sort = [1,4,6]\n",
+    "# middle val = 4\n",
+    "# if even number in size, return error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
+    "b = [4,6,1,3,12,41,4,1,24,12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 11\n",
+    "* Make a generator that parses the list of transaction data\n",
+    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
+    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
+    "* make sure to skip the header somehow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transactions = [A, item_a], [A, item_b]\n",
+    "# dict = {A:[item_a, item_b]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transactions = [\n",
+    "    [\"user_id|item_id\"],\n",
+    "    [\"A|item_a\"],\n",
+    "    [\"B|item_a\"],\n",
+    "    [\"C|item_a\"],\n",
+    "    [\"C|item_b\"],\n",
+    "    [\"C|item_c\"],\n",
+    "    [\"B|item_c\"],\n",
+    "    [\"D|item_b\"],\n",
+    "    [\"D|item_b\"]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 12\n",
+    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
+    "* Use error handling of some kind if the lists are not of equal length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should return [True, True, True, False, False]\n",
+    "a = [1,2,3,4,5]\n",
+    "b = [1,2,3,5,6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 13\n",
+    "Briefly explain the use of the following words in python:\n",
+    "* assert\n",
+    "* raise\n",
+    "* yield\n",
+    "* def\n",
+    "* lambda\n",
+    "* return\n",
+    "* map\n",
+    "* filter\n",
+    "* try\n",
+    "* except\n",
+    "* continue\n",
+    "* pass\n",
+    "* break\n",
+    "* while\n",
+    "* do (in the context of a while loop)\n",
+    "* for"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 14\n",
+    "Briefly describe the below:\n",
+    "* list\n",
+    "* tuple\n",
+    "* dictionary\n",
+    "* set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 15\n",
+    "* What two datatypes can be declared with { }?\n",
+    "* What two datatypes can be declared with ( )?\n",
+    "* Provide examples of each of the 4 above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 16\n",
+    "Describe what a generator is and how it differs from a list or tuple."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 17 \n",
+    "Show three ways of attaching a list to itself (one of then should use the function append) . \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/Challenge 1 - N Queens.ipynb b/challenges/Challenge 1 - N Queens.ipynb
new file mode 100644
index 0000000..dde9b97
--- /dev/null
+++ b/challenges/Challenge 1 - N Queens.ipynb	
@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# N-Queens Challenge\n",
+    "\n",
+    "From wikipedia:\n",
+    "    \n",
+    "    The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard so that no two queens threaten each other; thus, a solution requires that no two queens share the same row, column, or diagonal. The eight queens puzzle is an example of the more general n queens problem of placing n non-attacking queens on an n×n chessboard, for which solutions exist for all natural numbers n with the exception of n = 2 and n = 3.\n",
+    "    Chess composer Max Bezzel published the eight queens puzzle in 1848. Franz Nauck published the first solutions in 1850.[2] Nauck also extended the puzzle to the n queens problem, with n queens on a chessboard of n×n squares.\n",
+    "    Since then, many mathematicians, including Carl Friedrich Gauss, have worked on both the eight queens puzzle and its generalized n-queens version. In 1874, S. Gunther proposed a method using determinants to find solutions.[2] J.W.L. Glaisher refined Gunther's approach.\n",
+    "    In 1972, Edsger Dijkstra used this problem to illustrate the power of what he called structured programming. He published a highly detailed description of a depth-first backtracking algorithm.2\n",
+    "\n",
+    "## Build a code that calculates the ratio of solutions vs the possible arrangements for N queens in a chess board of NxN size.\n",
+    "\n",
+    "\n",
+    "1 - Build an UDF that calculates the total ammount of arragements of N queens\n",
+    "2 - Build an UDF that if a Queen can be place on a given position and returns true or false\n",
+    "3 - Apply the 2nd UDF for N queens on an NxN board\n",
+    "4 - Repeat #3 until it exhausts the possibilities\n",
+    "5 - Build an UDF thew\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/Challenge 2 - Face Recognition.ipynb b/challenges/Challenge 2 - Face Recognition.ipynb
new file mode 100644
index 0000000..a9c5bde
--- /dev/null
+++ b/challenges/Challenge 2 - Face Recognition.ipynb	
@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/Challenge 3 - ImageNet.ipynb b/challenges/Challenge 3 - ImageNet.ipynb
new file mode 100644
index 0000000..a9c5bde
--- /dev/null
+++ b/challenges/Challenge 3 - ImageNet.ipynb	
@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From f52fe34dfdef6ea09ed8821d44940b8a16624bd3 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 3 Feb 2020 13:19:54 -0600
Subject: [PATCH 07/36] updates

---
 .../Challenge 1 - N Queens-checkpoint.ipynb   |    6 -
 ...enge 2 - Face Recognition-checkpoint.ipynb |    6 -
 .../Challenge 3 - ImageNet-checkpoint.ipynb   |    6 -
 challenges/Challenge 1 - N Queens.ipynb       |    2 +-
 ...ontainer, Iteration Tools-checkpoint.ipynb | 2189 -----------------
 5 files changed, 1 insertion(+), 2208 deletions(-)
 delete mode 100644 challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb
 delete mode 100644 challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb
 delete mode 100644 challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb
 delete mode 100644 lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb

diff --git a/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb
deleted file mode 100644
index 2fd6442..0000000
--- a/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb
deleted file mode 100644
index 2fd6442..0000000
--- a/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb
deleted file mode 100644
index 2fd6442..0000000
--- a/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/challenges/Challenge 1 - N Queens.ipynb b/challenges/Challenge 1 - N Queens.ipynb
index dde9b97..7c9a134 100644
--- a/challenges/Challenge 1 - N Queens.ipynb	
+++ b/challenges/Challenge 1 - N Queens.ipynb	
@@ -20,7 +20,7 @@
     "2 - Build an UDF that if a Queen can be place on a given position and returns true or false\n",
     "3 - Apply the 2nd UDF for N queens on an NxN board\n",
     "4 - Repeat #3 until it exhausts the possibilities\n",
-    "5 - Build an UDF thew\n"
+    "5 - Build an UDF that manages the results in a table? or tuple? build an assesment of this object that returns the compiled results.\n"
    ]
   },
   {
diff --git a/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb
deleted file mode 100644
index 7a89d5c..0000000
--- a/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb	
+++ /dev/null
@@ -1,2189 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note\n",
-    "* throughout the notebooks you will see blue font.  that is your chance to pause and play around with some of the concepts we've just learned\n",
-    "* some will be easier, some harder and the appropriate amount of time will be given\n",
-    "* use this as an example to test concepts\n",
-    "* don't worry about you're code being perfect or getting errors, that's how you'll learn the mechanics of it\n",
-    "* be adventorous so you can learn how powerful the concepts can be"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assumptions\n",
-    "* I will asssume you know some basic python\n",
-    "    * syntax\n",
-    "    * conditionals\n",
-    "    * iteration\n",
-    "    * user defined functions\n",
-    "* though in the first few lectures as they come up I will quickly review them"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lecture 1\n",
-    "* review the basics of python\n",
-    "* python containers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### IDEs\n",
-    "* Anaconda (https://www.anaconda.com/distribution/)\n",
-    "* Jupyter Notebooks\n",
-    "* Spyder\n",
-    "* Sublime (https://www.sublimetext.com/)\n",
-    "* PyCharm (https://www.jetbrains.com/pycharm/download/#section=mac)\n",
-    "* Atom (https://atom.io/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lists\n",
-    "* array, collection, of unordered items of any data type\n",
-    "* can be changed (mutable)\n",
-    "* can have duplicates\n",
-    "* can put anything in them\n",
-    "* created using brackets [ ]\n",
-    "* concat a list of dataframes in pandas\n",
-    "* https://docs.python.org/3/tutorial/datastructures.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = [\"A\", 1, 2, 2.4, \"B\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(a, list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__delitem__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__getitem__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__iadd__',\n",
-       " '__imul__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__iter__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__mul__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__reversed__',\n",
-       " '__rmul__',\n",
-       " '__setattr__',\n",
-       " '__setitem__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'append',\n",
-       " 'clear',\n",
-       " 'copy',\n",
-       " 'count',\n",
-       " 'extend',\n",
-       " 'index',\n",
-       " 'insert',\n",
-       " 'pop',\n",
-       " 'remove',\n",
-       " 'reverse',\n",
-       " 'sort']"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### adding items to a list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 3, 4, 5, 5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "a.append(5)\n",
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### adding item to specific position"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['new_item', 'new_item', 'new_item', 'new_item', 1, 2, 3, 4, 5, 5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# list.insert(index, element)\n",
-    "a.insert(0, \"new_item\")\n",
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### size of list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(len(a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### accessing specific elements using bracket notation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "new_item\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(a[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "# last item of list\n",
-    "print(a[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### slicing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['new_item', 'new_item', 'new_item', 'new_item', 1, 2, 3, 4, 5, 5]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['new_item', 'new_item']"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# return 4th and 5th indexed item, not inclusive of the 6th\n",
-    "a[4:6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a list and</h3>\n",
-    "<p style=\"color:blue\">- find the len</p>\n",
-    "<p style=\"color:blue\">- append an item</p>\n",
-    "<p style=\"color:blue\">- select an item using the index value</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a nested list</h3>\n",
-    "<p style=\"color:blue\">- access the first item of the first neseted list element"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### map\n",
-    "* map lets you apply a function to each element of a python list\n",
-    "* map(func, list)\n",
-    "* returns map object which we can instantly convert to a list\n",
-    "* https://docs.python.org/3/library/functions.html#map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 3, 3]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [-3,-3,-3]\n",
-    "list(map(abs, a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### filter\n",
-    "* applies a function to each element of a list and returns a True or False, then subsets the list\n",
-    "* filter(function, list)\n",
-    "* https://docs.python.org/3/library/functions.html#filter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def my_func(x):\n",
-    "    return x > 0\n",
-    "\n",
-    "a = [1,2,3,-4]\n",
-    "\n",
-    "list(filter(my_func, a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a list of random numbers</h3>\n",
-    "<p style=\"color:blue\">- filter for items above 2</p>\n",
-    "<p style=\"color:blue\">- map the sqrt function from math to it to each element</p>\n",
-    "<p style=\"color:blue\">- use \"from math import sqrt\"</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### find the index of a given item"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"a\", \"b\"]\n",
-    "a.index(\"a\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"a\", \"b\", \"a\"]\n",
-    "a.index(\"a\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Tuple\n",
-    "* ordered and unchangable array\n",
-    "* ( ) parenths signal tuple\n",
-    "* https://docs.python.org/2/tutorial/datastructures.html#tuples-and-sequences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = (1,2,3,\"a\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(a, tuple)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tuple"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__getitem__',\n",
-       " '__getnewargs__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__iter__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__mul__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__rmul__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'count',\n",
-       " 'index']"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### access  elements using bracket notation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = (1,2,2,3,4,5,5)\n",
-    "a.count(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.index(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set\n",
-    "* unique elements\n",
-    "* unordered\n",
-    "* mixed types\n",
-    "* { } curly brackets indicate set\n",
-    "* could also use set()\n",
-    "* if we say x = { } this makes an empty dictionary not an empty set\n",
-    "* in such case we'd use set()\n",
-    "* https://docs.python.org/2/tutorial/datastructures.html#sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5}"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = {1,2,3,4,5,5,5,5,5}\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(a, set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__and__',\n",
-       " '__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__iand__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__ior__',\n",
-       " '__isub__',\n",
-       " '__iter__',\n",
-       " '__ixor__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__or__',\n",
-       " '__rand__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__ror__',\n",
-       " '__rsub__',\n",
-       " '__rxor__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__sub__',\n",
-       " '__subclasshook__',\n",
-       " '__xor__',\n",
-       " 'add',\n",
-       " 'clear',\n",
-       " 'copy',\n",
-       " 'difference',\n",
-       " 'difference_update',\n",
-       " 'discard',\n",
-       " 'intersection',\n",
-       " 'intersection_update',\n",
-       " 'isdisjoint',\n",
-       " 'issubset',\n",
-       " 'issuperset',\n",
-       " 'pop',\n",
-       " 'remove',\n",
-       " 'symmetric_difference',\n",
-       " 'symmetric_difference_update',\n",
-       " 'union',\n",
-       " 'update']"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 4}"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = {1,2,3,4}\n",
-    "b = (1,4,5,6)\n",
-    "a.intersection(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### lists can be converted to sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'a', 'b'}"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"a\", \"b\", \"a\"]\n",
-    "set(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3}"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_set = set([1,1,1,2,2,2,3,3,3])\n",
-    "my_set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = {}\n",
-    "type(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a list with some repeating elements</h3>\n",
-    "<p style=\"color:blue\">- find the length of the list</p>\n",
-    "<p style=\"color:blue\">- convert it to a set</p>\n",
-    "<p style=\"color:blue\">- find the size of the set</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dict\n",
-    "* key value pairs\n",
-    "* unordered\n",
-    "* no repeating keys\n",
-    "* curly brakets, though you add key value pairs, seperated by a colon\n",
-    "* https://docs.python.org/2/tutorial/datastructures.html#dictionaries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dict = {\n",
-    "    \"a\": 1,\n",
-    "    \"b\": 2,\n",
-    "    \"c\": 3\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(my_dict, dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(my_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__delitem__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__getitem__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__iter__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__setitem__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'clear',\n",
-       " 'copy',\n",
-       " 'fromkeys',\n",
-       " 'get',\n",
-       " 'items',\n",
-       " 'keys',\n",
-       " 'pop',\n",
-       " 'popitem',\n",
-       " 'setdefault',\n",
-       " 'update',\n",
-       " 'values']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(my_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### grab the keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['a', 'b', 'c'])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### grab the values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_values([1, 2, 3])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict.values()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### iterate a dictionary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a\n",
-      "b\n",
-      "c\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in my_dict:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_items([('a', 1), ('b', 2), ('c', 3)])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# list of tuples representing \n",
-    "my_dict.items()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a --> 1\n",
-      "b --> 2\n",
-      "c --> 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k,v in my_dict.items():\n",
-    "    print(k, \"-->\", v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### accessing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict[\"a\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### don't repeat keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dict = {\n",
-    "    1:\"Brian\",\n",
-    "    2:\"Jane\",\n",
-    "    2:\"John\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: 'Brian', 2: 'John'}"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### nested dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this is basically json\n",
-    "my_dict = {\n",
-    "    \n",
-    "    1:{\"first_name\":\"Brian\", \"last_name\":\"Craft\"},\n",
-    "    2:{\"first_name\":\"Jane\", \"last_name\":\"Doe\"},\n",
-    "    3:{\"first_name\":\"John\", \"last_name\":\"Doe\"}\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'first_name': 'Brian', 'last_name': 'Craft'}"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(my_dict[1], dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(my_dict[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Brian'"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict[1][\"first_name\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a dictionary</h3>\n",
-    "<p style=\"color:blue\">- access elements with the bracket notation</p>\n",
-    "<p style=\"color:blue\">- iterate over the dictionary using .items()</p>\n",
-    "<p style=\"color:blue\">- get the keys from the dictionary using .keys()</p>\n",
-    "<p style=\"color:blue\">- get the values from the dictionary using .values?()</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Eunumerate\n",
-    "* add an index value to your loops, or counter\n",
-    "* you can use the idx%1000 == 0 which returns the remainder of idx/1000 (modulus or clock math) which can be used as a way to monitor a time intensive loop\n",
-    "* https://docs.python.org/2/tutorial/datastructures.html#looping-techniques"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<enumerate at 0x7f9c682be5f0>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"a\", \"b\", \"c\"]\n",
-    "enumerate(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 --> a\n",
-      "1 --> b\n",
-      "2 --> c\n"
-     ]
-    }
-   ],
-   "source": [
-    "for idx, i in enumerate(a):\n",
-    "    print(idx,\"-->\", i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Zip\n",
-    "* Mash iterables together to make nested tuples\n",
-    "* you can use zip to mash together keys and values and put them into a dictionary\n",
-    "* https://docs.python.org/2/library/functions.html#zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3]\n",
-    "b = [\"a\", \"b\", \"c\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<zip at 0x7f9c682c3a50>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zip(a,b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 'a'), (2, 'b'), (3, 'c')]"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[x for x in zip(a,b)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1, 'a')\n",
-      "(2, 'b')\n",
-      "(3, 'c')\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'tuple'>\n",
-      "<class 'tuple'>\n",
-      "<class 'tuple'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(type(i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1, 'a')\n",
-      "(2, 'b')\n",
-      "(3, 'c')\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(i[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a\n",
-      "b\n",
-      "c\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(i[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 'a', 'A'), (2, 'b', 'B'), (3, 'c', 'C')]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2,3]\n",
-    "b = [\"a\", \"b\", \"c\"]\n",
-    "c = [\"A\", \"B\", \"C\"]\n",
-    "\n",
-    "list(zip(a,b,c))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Continue, Break and Pass\n",
-    "* break: chance to exit a loop when a condition is met\n",
-    "* continue: continue to the next iteration of the loop\n",
-    "* pass: do nothing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 is not greater then 4\n",
-      "1 is not greater then 4\n",
-      "2 is not greater then 4\n",
-      "3 is not greater then 4\n",
-      "4 is not greater then 4\n",
-      "5 is greater then 4 so we will break out loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        print(\"{} is greater then 4 so we will break out loop\".format(x))\n",
-    "        break\n",
-    "    else:\n",
-    "        print(\"{} is not greater then 4\".format(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5 is greater then 4\n",
-      "6 is greater then 4\n",
-      "7 is greater then 4\n",
-      "8 is greater then 4\n",
-      "9 is greater then 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        print(\"{} is greater then 4\".format(x))\n",
-    "    else:\n",
-    "        pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 is not greater then 4\n",
-      "1 is not greater then 4\n",
-      "2 is not greater then 4\n",
-      "3 is not greater then 4\n",
-      "4 is not greater then 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        continue\n",
-    "    else:\n",
-    "        print(\"{} is not greater then 4\".format(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 is not greater then 4\n",
-      "print statement below\n",
-      "1 is not greater then 4\n",
-      "print statement below\n",
-      "2 is not greater then 4\n",
-      "print statement below\n",
-      "3 is not greater then 4\n",
-      "print statement below\n",
-      "4 is not greater then 4\n",
-      "print statement below\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        continue\n",
-    "    else:\n",
-    "        print(\"{} is not greater then 4\".format(x))\n",
-    "       \n",
-    "    print(\"print statement below\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 is not greater then 4\n",
-      "print statement below\n",
-      "1 is not greater then 4\n",
-      "print statement below\n",
-      "2 is not greater then 4\n",
-      "print statement below\n",
-      "3 is not greater then 4\n",
-      "print statement below\n",
-      "4 is not greater then 4\n",
-      "print statement below\n",
-      "print statement below\n",
-      "print statement below\n",
-      "print statement below\n",
-      "print statement below\n",
-      "print statement below\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        pass\n",
-    "    else:\n",
-    "        print(\"{} is not greater then 4\".format(x))\n",
-    "       \n",
-    "    print(\"print statement below\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Itertools\n",
-    "* tools for working with iterables\n",
-    "* additional building blocks\n",
-    "* chain pushes lists together\n",
-    "* https://docs.python.org/2/library/itertools.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### chain mashes nested lists into one"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [\n",
-    "    [\"a\", \"b\", \"c\"],\n",
-    "    [\"d\", \"e\", \"f\", \"b\"]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = list(chain.from_iterable(a))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c', 'd', 'e', 'f', 'b']"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### permutation\n",
-    "* https://www.mathsisfun.com/combinatorics/combinations-permutations.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import permutations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'b'), ('a', 'c'), ('b', 'a'), ('b', 'c'), ('c', 'a'), ('c', 'b')]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(permutations([\"a\",\"b\",\"c\"], 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'b', 'c'),\n",
-       " ('a', 'c', 'b'),\n",
-       " ('b', 'a', 'c'),\n",
-       " ('b', 'c', 'a'),\n",
-       " ('c', 'a', 'b'),\n",
-       " ('c', 'b', 'a')]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(permutations([\"a\",\"b\",\"c\"], 3))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### combinations\n",
-    "* https://www.mathsisfun.com/combinatorics/combinations-permutations.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'b'), ('a', 'c'), ('b', 'c')]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(combinations([\"a\",\"b\",\"c\"], 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'b', 'c')]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(combinations([\"a\",\"b\",\"c\"], 3))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [[\"a\", \"b\"], [\"c\", \"d\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd')]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(product(*a))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd')]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(product([\"a\", \"b\"],[\"c\", \"d\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Collections\n",
-    "* use counter to get frequencies of items in a list\n",
-    "* https://docs.python.org/2/library/collections.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counter = Counter(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({'a': 1, 'b': 2, 'c': 1, 'd': 1, 'e': 1, 'f': 1})"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "collections.Counter"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(counter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a 1\n",
-      "b 2\n",
-      "c 1\n",
-      "d 1\n",
-      "e 1\n",
-      "f 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k,v in counter.items():\n",
-    "    print(k,v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a list</h3>\n",
-    "<p style=\"color:blue\">- find all the combinations</p>\n",
-    "<p style=\"color:blue\">- find all the permutations</p>\n",
-    "<p style=\"color:blue\">- create a counter for the list to get item frequencies</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From de76581717e061feefe3dbf96698294f02083703 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 3 Feb 2020 14:53:05 -0600
Subject: [PATCH 08/36] setup

---
 challenges/Challenge 1 - N Queens.ipynb | 70 +++++++++++++++++++++++++
 setup.py                                | 22 ++++++++
 2 files changed, 92 insertions(+)
 create mode 100644 setup.py

diff --git a/challenges/Challenge 1 - N Queens.ipynb b/challenges/Challenge 1 - N Queens.ipynb
index 7c9a134..bf4f3d9 100644
--- a/challenges/Challenge 1 - N Queens.ipynb	
+++ b/challenges/Challenge 1 - N Queens.ipynb	
@@ -23,6 +23,76 @@
     "5 - Build an UDF that manages the results in a table? or tuple? build an assesment of this object that returns the compiled results.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For N = 1 \n",
+      "Found 1 solutions.\n",
+      "1\n",
+      "For N = 2 \n",
+      "Found 0 solutions.\n",
+      "0\n",
+      "For N = 3 \n",
+      "Found 0 solutions.\n",
+      "0\n",
+      "For N = 4 \n",
+      "Found 2 solutions.\n",
+      "2\n",
+      "For N = 5 \n",
+      "Found 10 solutions.\n",
+      "10\n",
+      "For N = 6 \n",
+      "Found 4 solutions.\n",
+      "4\n",
+      "For N = 7 \n",
+      "Found 40 solutions.\n",
+      "40\n",
+      "For N = 8 \n",
+      "Found 92 solutions.\n",
+      "92\n",
+      "For N = 9 \n",
+      "Found 352 solutions.\n",
+      "352\n",
+      "For N = 10 \n",
+      "Found 724 solutions.\n",
+      "724\n",
+      "For N = 11 \n",
+      "Found 2680 solutions.\n",
+      "2680\n",
+      "For N = 12 \n",
+      "Found 14200 solutions.\n",
+      "14200\n",
+      "For N = 13 \n",
+      "Found 73712 solutions.\n",
+      "73712\n",
+      "For N = 14 \n",
+      "Found 365596 solutions.\n",
+      "365596\n",
+      "For N = 15 \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "for k in range(1,21):\n",
+    "    print(\"For N = {} \".format(k))\n",
+    "    print(NQueens(k).solutions)\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..527506b
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,22 @@
+import setuptools
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+setuptools.setup(
+    name="MSCA07013", # Replace with your own username
+    version="0.0.1",
+    author="Rafael Vescovi",
+    author_email="ravescovi@gmail.com",
+    description="A small example package",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/ravescovi/python_for_analitycs",
+    packages=setuptools.find_packages(),
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+    python_requires='>=3.6',
+)
\ No newline at end of file

From 495a85ae6bc8972736affb71aa606f4d12baf728 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 3 Feb 2020 19:54:39 -0600
Subject: [PATCH 09/36] Updates

---
 README.md                         |   3 --
 lectures/mashme3.txt              | Bin 0 -> 25 bytes
 tests/NAME_SURNAME_ASSIGN_1.ipynb |  61 ++++++++++++++++++++++++++++++
 3 files changed, 61 insertions(+), 3 deletions(-)
 create mode 100644 lectures/mashme3.txt

diff --git a/README.md b/README.md
index 0b0ba1c..186f013 100644
--- a/README.md
+++ b/README.md
@@ -48,9 +48,6 @@ Note that this is a command-line tool. Once installed, you may not see a new ico
 
 Although we don't make heavy use of version control, you will be introduced to the concept. Installing Git also installs "Git Bash," and comand line environment which simulates Unix/Linux. We will do several exercises which will require this environment.
 
-#### Additional steps:
-Install Python extensions from https://marketplace.visualstudio.com/items?itemName=ms-python.python (visit that page and click "Install")
-
 ## Clone this repository [Optional on day 1]
 1. Visit this web page: https://github.com/ravescovi/python_for_analytics
 2. Click "Clone or download" and pick the "Download ZIP" option (unless you already have a GitHub account)
diff --git a/lectures/mashme3.txt b/lectures/mashme3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c4ccfeb75659b78a15f34c8ee0854820be1e719a
GIT binary patch
literal 25
gcmZo*j^JltU<fWrtjx?yS8(*nEK5~XEM(9F08<PGoB#j-

literal 0
HcmV?d00001

diff --git a/tests/NAME_SURNAME_ASSIGN_1.ipynb b/tests/NAME_SURNAME_ASSIGN_1.ipynb
index 8af326f..e118edf 100644
--- a/tests/NAME_SURNAME_ASSIGN_1.ipynb
+++ b/tests/NAME_SURNAME_ASSIGN_1.ipynb
@@ -477,6 +477,67 @@
     "Show three ways of attaching a list to itself (one of then should use the function append) . \n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'chain'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-11-449d8e80d3a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'chain'"
+     ]
+    }
+   ],
+   "source": [
+    "a = [1,2,3]\n",
+    "a.append(a)\n",
+    "\n",
+    "print(a.chain())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, [1, 2, 3, [...]], 1, 2, 3, [1, 2, 3, [...]]]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a+a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, [...]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From f6927d22c190f58e17072bdead5e08bcd4f0da5a Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 4 Feb 2020 14:15:06 -0600
Subject: [PATCH 10/36] updates

---
 .gitignore                                    |    2 +
 .../NAME_SURNAME_ASSIGN_2-checkpoint.ipynb    |  570 ++
 assigments/NAME_SURNAME_ASSIGN_2.ipynb        |  257 +
 .../Challenge 1 - N Queens-checkpoint.ipynb   |  125 +
 ...enge 2 - Face Recognition-checkpoint.ipynb |   55 +
 .../Challenge 3 - ImageNet-checkpoint.ipynb   |   37 +
 challenges/Challenge 1 - N Queens.ipynb       |   18 +-
 .../Challenge 2 - Face Recognition.ipynb      |   19 +-
 challenges/Challenge 3 - ImageNet.ipynb       |   15 +-
 ...ators, and Error Handling-checkpoint.ipynb | 1849 ++++
 ...tetime and Saving Objects-checkpoint.ipynb | 1876 ++++
 ... and 5 - Numpy and Pandas-checkpoint.ipynb | 7817 +++++++++++++++++
 ...bdas, Generators, and Error Handling.ipynb |  446 +-
 ...3 - OOO, Datetime and Saving Objects.ipynb |  664 +-
 .../Lecture 4 and 5 - Numpy and Pandas.ipynb  | 1386 +--
 15 files changed, 14165 insertions(+), 971 deletions(-)
 create mode 100644 .gitignore
 create mode 100644 assigments/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_2-checkpoint.ipynb
 create mode 100644 assigments/NAME_SURNAME_ASSIGN_2.ipynb
 create mode 100644 challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb
 create mode 100644 challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb
 create mode 100644 challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb
 create mode 100644 lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb
 create mode 100644 lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb
 create mode 100644 lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..bc8c467
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+
+tests/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_1-checkpoint.ipynb
diff --git a/assigments/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_2-checkpoint.ipynb b/assigments/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_2-checkpoint.ipynb
new file mode 100644
index 0000000..51aa0de
--- /dev/null
+++ b/assigments/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_2-checkpoint.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 2\n",
+    "* Due 2/25 by 9pm\n",
+    "* Topics: Numpy and Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instructions\n",
+    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_2\n",
+    "* Finish the test on this notebook\n",
+    "* Download your notebook as a PDF\n",
+    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_2.pdf and RAFAEL_VESCOVI_ASSIGN_2.ipynb)\n",
+    "* Show the output of your code working on the sample data, like below.  \n",
+    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
+    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
+    "* You have to show your work (code).\n",
+    "* You will be graded:\n",
+    "    * The functionality of your code (Does it do what it was meant to)\n",
+    "    * Showing the output on the sample data provided\n",
+    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
+    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
+    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
+    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample Question\n",
+    "* Create a UDF that adds 2 to a number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add_func(x):\n",
+    "    return(x+2)\n",
+    "\n",
+    "number = add_func(a)\n",
+    "print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1\n",
+    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
+    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
+    "    * make use of zip and comprehension to make the dictionary\n",
+    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [5,7]\n",
+    "# b = [\"x\", \"z\"]\n",
+    "# return {5:\"x\", 7:\"z\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7,8]\n",
+    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2\n",
+    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
+    "* The function should return 4 things"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3\n",
+    "* Map a function to the below list that\n",
+    "    * returns the remainder of each number divided by 3 (52/3 = 17 with a remainder of 1, we want the 1 to be returned)\n",
+    "    * use lambdas for the mapping\n",
+    "    * hint, look at modulus (%) in python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst = list(range(100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4\n",
+    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
+    "* example below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lst = [1,2]\n",
+    "# dict = {1:\"a\", 2:\"b\"}\n",
+    "# new_lst = [\"a\",\"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = list(range(5))\n",
+    "dct = {\n",
+    "    0:\"a\",\n",
+    "    1:\"b\",\n",
+    "    2:\"c\",\n",
+    "    3:\"d\",\n",
+    "    4:\"e\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 5\n",
+    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
+    "* use comprehension to perform the squaring of each item\n",
+    "* hint, this should be doable in one line using args"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_func(1,2,3)\n",
+    "    # do some stuff\n",
+    "    \n",
+    "# return_lst = [1,4,9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for this pass in the numberse 1,2,3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 6\n",
+    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [1,2]\n",
+    "# b = [2,3]\n",
+    "# return_lst = [2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7]\n",
+    "b = [2,3,4,6,8,10,11,21]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 7\n",
+    "* write a function that takes a list of numbers and filters for odds only\n",
+    "* use the filter method and a lambda as the function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_lst = [1,2]\n",
+    "# new_lst = [1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 8\n",
+    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
+    "* Use the function to map it to a list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_lst = [1,-2,1]\n",
+    "# using a comprehension return [3,4,3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,-2,1,8,-10,15,-12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 9\n",
+    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
+    "* note you may not use any third party modules.  use  comprehensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [1,2]\n",
+    "# b = [3,1]\n",
+    "# distance = ~2.23"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,1]\n",
+    "b = [3,1,2,4,3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 10\n",
+    "* write a function that takes a list, sorts it and finds the middle value.  \n",
+    "* use error handling to check the list is odd in length\n",
+    "* if it's even, return an error message\n",
+    "* feel free to use a try/except or assert\n",
+    "* show the output run on both lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [4,6,1]\n",
+    "# sort = [1,4,6]\n",
+    "# middle val = 4\n",
+    "# if even number in size, return error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
+    "b = [4,6,1,3,12,41,4,1,24,12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 11\n",
+    "* Make a generator that parses the list of transaction data\n",
+    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
+    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
+    "* make sure to skip the header somehow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transactions = [A, item_a], [A, item_b]\n",
+    "# dict = {A:[item_a, item_b]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transactions = [\n",
+    "    [\"user_id|item_id\"],\n",
+    "    [\"A|item_a\"],\n",
+    "    [\"B|item_a\"],\n",
+    "    [\"C|item_a\"],\n",
+    "    [\"C|item_b\"],\n",
+    "    [\"C|item_c\"],\n",
+    "    [\"B|item_c\"],\n",
+    "    [\"D|item_b\"],\n",
+    "    [\"D|item_b\"]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 12\n",
+    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
+    "* Use error handling of some kind if the lists are not of equal length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should return [True, True, True, False, False]\n",
+    "a = [1,2,3,4,5]\n",
+    "b = [1,2,3,5,6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 13\n",
+    "Briefly explain the use of the following words in python:\n",
+    "* assert\n",
+    "* raise\n",
+    "* yield\n",
+    "* def\n",
+    "* lambda\n",
+    "* return\n",
+    "* map\n",
+    "* filter\n",
+    "* try\n",
+    "* except\n",
+    "* continue\n",
+    "* pass\n",
+    "* break\n",
+    "* while\n",
+    "* do (in the context of a while loop)\n",
+    "* for"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 14\n",
+    "Briefly describe the below:\n",
+    "* list\n",
+    "* tuple\n",
+    "* dictionary\n",
+    "* set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 15\n",
+    "* What two datatypes can be declared with { }?\n",
+    "* What two datatypes can be declared with ( )?\n",
+    "* Provide examples of each of the 4 above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 16\n",
+    "Describe what a generator is and how it differs from a list or tuple."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 17 \n",
+    "Show three ways of attaching a list to itself (one of then should use the function append) . \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'chain'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-11-449d8e80d3a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'chain'"
+     ]
+    }
+   ],
+   "source": [
+    "a = [1,2,3]\n",
+    "a.append(a)\n",
+    "\n",
+    "print(a.chain())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, [1, 2, 3, [...]], 1, 2, 3, [1, 2, 3, [...]]]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a+a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, [...]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assigments/NAME_SURNAME_ASSIGN_2.ipynb b/assigments/NAME_SURNAME_ASSIGN_2.ipynb
new file mode 100644
index 0000000..9a76633
--- /dev/null
+++ b/assigments/NAME_SURNAME_ASSIGN_2.ipynb
@@ -0,0 +1,257 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 2\n",
+    "* Due: Tuesday 2/25/2020 by  9 PM \n",
+    "* Topics: pandas and numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instructions\n",
+    "* Download your notebook as a PDF\n",
+    "* Turn in both the notebook and PDF\n",
+    "* Show the output of your code working on the sample data, like below.  \n",
+    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
+    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
+    "* You have to show your work (code).\n",
+    "* You will be graded:\n",
+    "    * The functionality of your code (Does it do what it was meant to)\n",
+    "    * Showing the output on the sample data provided\n",
+    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
+    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 1\n",
+    "* sklearn has the iris dataset built in\n",
+    "* this is what we will be working with\n",
+    "* put the iris data into a dataframe where we have 5 columns (the 4 features and the target)\n",
+    "* but the target shouldn't be numerical, it should be the actual label (so put setosa not 0 in the target column) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "iris = load_iris()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 2\n",
+    "* using the data array from the iris variable (should be a numpy array of size 150x4), for each row and column, find the sum, min and max\n",
+    "* print out the results, but for the rows, show only the first 5 elements of the array and display the length (otherwise it will be large arrays since each row has a value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 3\n",
+    "* write a function to return the multiplication of two matrices\n",
+    "* use some sort of error handling to make sure the dimensions are compatible\n",
+    "* use the function to multiply the iris data (don't use the label column) by it's transpose\n",
+    "* what are the dimensions of the result\n",
+    "* put the sum of each row of the resulting matrix into a vector, printing out the length and first 5 elements.\n",
+    "* put the sum of each column of the resulting matrix in a vector and print it out."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 4\n",
+    "* create a subset of the iris dataframe that doesn't have the label column\n",
+    "* for each row, find the index of the min and max feature value\n",
+    "    * if we have a row with values [10,2,3,4] we want to return 1 for min and 0 for max, as these are the indicies that align to the min and max values\n",
+    "* the result should be two arrays of length 150 (one for each row) with the indices of the min and max value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 5\n",
+    "* Use the above two arrays to make a dataframe with 2 columns, the first being the feature of minimum value and the second the feature name of the maximum value\n",
+    "* note, don't have the cell values be the index value of the min/max value, have it be the feature name\n",
+    "    * a row should be [min_val = sepal width (cm), max_val = petal length (cm)]\n",
+    "* show the distributions for max and min features (how many times is each feature the max and min value for a row)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 6\n",
+    "* Describe a situation the functionality from question 4 and 5 could be of use?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 7\n",
+    "* sort the feature values for each row and replace the indices with the feature names\n",
+    "    * so each row will have 4 columns, the first column being the feature name that is the highest value, the second column being the feature name that is the second highest value, etc.\n",
+    "* note, watch out how argsort in numpy works.  you will need to reverse the order somehow.  the sorted(reverse = True) functionality might be of help.\n",
+    "* make sure to replace the index values with the feature name, as we did above\n",
+    "* put the resulting 2-d array into a pandas dataframe and print the first 5 rows.\n",
+    "* hint, look at the apply_along_axis() method for numpy arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 8\n",
+    "* apply z-score normalization to each column in the iris data (note you do not need the target/label column)\n",
+    "* column wise meaning, treat each column as an array, and find the standard deviations and means of that column\n",
+    "* note, you can use zscore from scipy.stats\n",
+    "* results should be a pandas dataframe, printing out 5 rows and running the describe() method on the dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 9\n",
+    "* make a function that takes in a 2d numpy array X, a 1d numpy array Y and the size the training data, test data and valiation datasets.  \n",
+    "* return 6 items\n",
+    "    * train_x, train_y, test_x, test_y, val_x, val_y\n",
+    "* do not use any modules.  this can be solved using bracket notation to subset.  Note // will take care of decimals in doing division. you could also use int() to convert the float to a whole number\n",
+    "* make the params for the training, test and val size be decimals, that repsent percentages of the data.  For instance .8, .1, .1 means an 80% training, 10% test and 10% validation split\n",
+    "* use an assert to make sure the numbers add to 1\n",
+    "* print out the dimensions of all 6 elements, using iris as a test case with an 80-10-10 split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 10\n",
+    "* using pandas, find the sum of each feature by species type (label)\n",
+    "* do the same, but find the min, max and median as well"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 11\n",
+    "* mean center each column of the iris dataframe (excluding the label column of course)\n",
+    "* this means, for each column, the mean should be zero.  to accomplish this we can subtract the column mean from each element of the column\n",
+    "* note, broadcasting, which if we have say a 150 row and 4 column dataframe, can  take a row vector of size 4 and apply it to each element\n",
+    "*  thus, we can answer this questions doing something like df - df_col_means\n",
+    "* run the describe() method at the end to show the data has been mean center"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quesiton 12\n",
+    "* Explain what the axis mean in regards to a pandas dataframe and numpy array\n",
+    "* How would you groupby two columns in pandas?\n",
+    "* What functions are used to read in csvs and excel files in pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 13\n",
+    "* using Pandas, subset the dataset for only species setosa and petal length > 1.3\n",
+    "* sum the feature columns and display the results for each (excluding the label column)\n",
+    "* do the same, but change the and to an or, and repeat the same calculation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 14\n",
+    "* write a lambda that subtracts 2 from all elements of a pandas dataframe\n",
+    "* display the top 5 rows\n",
+    "* use the same lambda,  but applying it to the sepal length  column only\n",
+    "* so the output should be the top 5 rows of a dataframe where all cells have had 2 subtracted and then another dataframe where only sepal length has 2 subtracted from it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 15\n",
+    "* normalize each column of pandas dataframe to 0-1 scale.\n",
+    "* hint\n",
+    "    * 0-1 is done by using (X - xmin)/(xmax - xmin)\n",
+    "    * the approach should be similar to when we meean centered the dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 16\n",
+    "* assume the below 2 matrices, the first is observations and the second is cluster centers\n",
+    "* using cdist from scipy, create a matrix where the rows represent our 3 observations and the columns represent our 2 clusters and the cells values are the euclidean distances between a given observation and cluster\n",
+    "* when would this be of use?  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = np.array([\n",
+    "    [1,2,3],\n",
+    "    [4,3,1],\n",
+    "    [2,3,4]\n",
+    "])\n",
+    "\n",
+    "cluster_centers = np.array([\n",
+    "    [2,3,1],\n",
+    "    [2,1,3]\n",
+    "])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb
new file mode 100644
index 0000000..def6c73
--- /dev/null
+++ b/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb	
@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# N-Queens Challenge\n",
+    "\n",
+    "From wikipedia:\n",
+    "    \n",
+    ">The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard so that no two queens threaten each other; thus, a solution requires that no two queens share the same row, column, or diagonal. The eight queens puzzle is an example of the more general n queens problem of placing n non-attacking queens on an n×n chessboard, for which solutions exist for all natural numbers n with the exception of n = 2 and n = 3.\n",
+    "Chess composer Max Bezzel published the eight queens puzzle in 1848. Franz Nauck published the first solutions in 1850. Nauck also extended the puzzle to the n queens problem, with n queens on a chessboard of n×n squares.\n",
+    "    Since then, many mathematicians, including Carl Friedrich Gauss, have worked on both the eight queens puzzle and its generalized n-queens version. In 1874, S. Gunther proposed a method using determinants to find solutions. J.W.L. Glaisher refined Gunther's approach.\n",
+    "    In 1972, Edsger Dijkstra used this problem to illustrate the power of what he called structured programming. He published a highly detailed description of a depth-first backtracking algorithm.2\n",
+    "\n",
+    "## Build a code that calculates the ratio of solutions vs the possible arrangements for N queens in a chess board of NxN size.\n",
+    "\n",
+    "\n",
+    "1. Build an UDF that calculates the total ammount of arragements of N queens\n",
+    "2. Build an UDF that if a Queen can be place on a given position and returns true or false\n",
+    "3. Apply the 2nd UDF for N queens on an NxN board\n",
+    "4. Repeat #3 until it exhausts the possibilities\n",
+    "5. Build an UDF that manages the results in a table? or tuple? build an assesment of this object that returns the compiled results.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For N = 1 \n",
+      "Found 1 solutions.\n",
+      "1\n",
+      "For N = 2 \n",
+      "Found 0 solutions.\n",
+      "0\n",
+      "For N = 3 \n",
+      "Found 0 solutions.\n",
+      "0\n",
+      "For N = 4 \n",
+      "Found 2 solutions.\n",
+      "2\n",
+      "For N = 5 \n",
+      "Found 10 solutions.\n",
+      "10\n",
+      "For N = 6 \n",
+      "Found 4 solutions.\n",
+      "4\n",
+      "For N = 7 \n",
+      "Found 40 solutions.\n",
+      "40\n",
+      "For N = 8 \n",
+      "Found 92 solutions.\n",
+      "92\n",
+      "For N = 9 \n",
+      "Found 352 solutions.\n",
+      "352\n",
+      "For N = 10 \n",
+      "Found 724 solutions.\n",
+      "724\n",
+      "For N = 11 \n",
+      "Found 2680 solutions.\n",
+      "2680\n",
+      "For N = 12 \n",
+      "Found 14200 solutions.\n",
+      "14200\n",
+      "For N = 13 \n",
+      "Found 73712 solutions.\n",
+      "73712\n",
+      "For N = 14 \n",
+      "Found 365596 solutions.\n",
+      "365596\n",
+      "For N = 15 \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "for k in range(1,21):\n",
+    "    print(\"For N = {} \".format(k))\n",
+    "    print(NQueens(k).solutions)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb
new file mode 100644
index 0000000..37f12ea
--- /dev/null
+++ b/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb	
@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# N-Queens Challenge\n",
+    "\n",
+    "From wikipedia:\n",
+    "    \n",
+    ">The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard so that no two queens threaten each other; thus, a solution requires that no two queens share the same row, column, or diagonal. The eight queens puzzle is an example of the more general n queens problem of placing n non-attacking queens on an n×n chessboard, for which solutions exist for all natural numbers n with the exception of n = 2 and n = 3.\n",
+    "Chess composer Max Bezzel published the eight queens puzzle in 1848. Franz Nauck published the first solutions in 1850. Nauck also extended the puzzle to the n queens problem, with n queens on a chessboard of n×n squares.\n",
+    "    Since then, many mathematicians, including Carl Friedrich Gauss, have worked on both the eight queens puzzle and its generalized n-queens version. In 1874, S. Gunther proposed a method using determinants to find solutions. J.W.L. Glaisher refined Gunther's approach.\n",
+    "    In 1972, Edsger Dijkstra used this problem to illustrate the power of what he called structured programming. He published a highly detailed description of a depth-first backtracking algorithm.2\n",
+    "\n",
+    "## Build a code that calculates the ratio of solutions vs the possible arrangements for N queens in a chess board of NxN size.\n",
+    "\n",
+    "\n",
+    "1. Build an UDF that calculates the total ammount of arragements of N queens\n",
+    "2. Build an UDF that if a Queen can be place on a given position and returns true or false\n",
+    "3. Apply the 2nd UDF for N queens on an NxN board\n",
+    "4. Repeat #3 until it exhausts the possibilities\n",
+    "5. Build an UDF that manages the results in a table? or tuple? build an assesment of this object that returns the compiled results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb
new file mode 100644
index 0000000..f5197cd
--- /dev/null
+++ b/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb	
@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/Challenge 1 - N Queens.ipynb b/challenges/Challenge 1 - N Queens.ipynb
index bf4f3d9..def6c73 100644
--- a/challenges/Challenge 1 - N Queens.ipynb	
+++ b/challenges/Challenge 1 - N Queens.ipynb	
@@ -8,19 +8,19 @@
     "\n",
     "From wikipedia:\n",
     "    \n",
-    "    The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard so that no two queens threaten each other; thus, a solution requires that no two queens share the same row, column, or diagonal. The eight queens puzzle is an example of the more general n queens problem of placing n non-attacking queens on an n×n chessboard, for which solutions exist for all natural numbers n with the exception of n = 2 and n = 3.\n",
-    "    Chess composer Max Bezzel published the eight queens puzzle in 1848. Franz Nauck published the first solutions in 1850.[2] Nauck also extended the puzzle to the n queens problem, with n queens on a chessboard of n×n squares.\n",
-    "    Since then, many mathematicians, including Carl Friedrich Gauss, have worked on both the eight queens puzzle and its generalized n-queens version. In 1874, S. Gunther proposed a method using determinants to find solutions.[2] J.W.L. Glaisher refined Gunther's approach.\n",
+    ">The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard so that no two queens threaten each other; thus, a solution requires that no two queens share the same row, column, or diagonal. The eight queens puzzle is an example of the more general n queens problem of placing n non-attacking queens on an n×n chessboard, for which solutions exist for all natural numbers n with the exception of n = 2 and n = 3.\n",
+    "Chess composer Max Bezzel published the eight queens puzzle in 1848. Franz Nauck published the first solutions in 1850. Nauck also extended the puzzle to the n queens problem, with n queens on a chessboard of n×n squares.\n",
+    "    Since then, many mathematicians, including Carl Friedrich Gauss, have worked on both the eight queens puzzle and its generalized n-queens version. In 1874, S. Gunther proposed a method using determinants to find solutions. J.W.L. Glaisher refined Gunther's approach.\n",
     "    In 1972, Edsger Dijkstra used this problem to illustrate the power of what he called structured programming. He published a highly detailed description of a depth-first backtracking algorithm.2\n",
     "\n",
     "## Build a code that calculates the ratio of solutions vs the possible arrangements for N queens in a chess board of NxN size.\n",
     "\n",
     "\n",
-    "1 - Build an UDF that calculates the total ammount of arragements of N queens\n",
-    "2 - Build an UDF that if a Queen can be place on a given position and returns true or false\n",
-    "3 - Apply the 2nd UDF for N queens on an NxN board\n",
-    "4 - Repeat #3 until it exhausts the possibilities\n",
-    "5 - Build an UDF that manages the results in a table? or tuple? build an assesment of this object that returns the compiled results.\n"
+    "1. Build an UDF that calculates the total ammount of arragements of N queens\n",
+    "2. Build an UDF that if a Queen can be place on a given position and returns true or false\n",
+    "3. Apply the 2nd UDF for N queens on an NxN board\n",
+    "4. Repeat #3 until it exhausts the possibilities\n",
+    "5. Build an UDF that manages the results in a table? or tuple? build an assesment of this object that returns the compiled results.\n"
    ]
   },
   {
@@ -117,7 +117,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/challenges/Challenge 2 - Face Recognition.ipynb b/challenges/Challenge 2 - Face Recognition.ipynb
index a9c5bde..323cbbd 100644
--- a/challenges/Challenge 2 - Face Recognition.ipynb	
+++ b/challenges/Challenge 2 - Face Recognition.ipynb	
@@ -3,7 +3,22 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "# Face Recognition Challenge\n",
+    "\n",
+    "From wikipedia:\n",
+    "    \n",
+    "> Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars.\n",
+    "Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process.\n",
+    "A reliable face-detection approach based on the genetic algorithm and the eigen-face technique:\n",
+    "Firstly, the possible human eye regions are detected by testing all the valley regions in the gray-level image. Then the genetic algorithm is used to generate all the possible face regions which include the eyebrows, the iris, the nostril and the mouth corners.\n",
+    "Each possible face candidate is normalized to reduce both the lighting effect, which is caused by uneven illumination; and the shirring effect, which is due to head movement. The fitness value of each candidate is measured based on its projection on the eigen-faces. After a number of iterations, all the face candidates with a high fitness value are selected for further verification. At this stage, the face symmetry is measured and the existence of the different facial features is verified for each face candidate.\n",
+    "\n",
+    "## Build a code that finds faces in a given picture.\n",
+    "\n",
+    "1. Build an UDF that finds faces in a given picture.\n",
+    "2. Apply to 3 different pictures ."
+   ]
   },
   {
    "cell_type": "code",
@@ -29,7 +44,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/challenges/Challenge 3 - ImageNet.ipynb b/challenges/Challenge 3 - ImageNet.ipynb
index a9c5bde..9c87bc9 100644
--- a/challenges/Challenge 3 - ImageNet.ipynb	
+++ b/challenges/Challenge 3 - ImageNet.ipynb	
@@ -3,7 +3,18 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "# ImageNet Challenge\n",
+    "\n",
+    "From wikipedia:\n",
+    "    \n",
+    "> The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. ImageNet contains more than 20,000 categories with a typical category, such as \"balloon\" or \"strawberry\", consisting of several hundred images. The database of annotations of third-party image URLs is freely available directly from ImageNet, though the actual images are not owned by ImageNet. Since 2010, the ImageNet project runs an annual software contest, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), where software programs compete to correctly classify and detect objects and scenes. The challenge uses a \"trimmed\" list of one thousand non-overlapping classes.\n",
+    "\n",
+    "## Build a code that finds faces in a given picture.\n",
+    "\n",
+    "1. Build an UDF that trains a ImageNet\n",
+    "2. Classify a given set of pictures"
+   ]
   },
   {
    "cell_type": "code",
@@ -29,7 +40,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb
new file mode 100644
index 0000000..40f91ab
--- /dev/null
+++ b/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb	
@@ -0,0 +1,1849 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lecture 2\n",
+    "* user defined functions\n",
+    "* comprehensions\n",
+    "* lambda functions\n",
+    "* generators\n",
+    "* exception handling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### User Defined Functions\n",
+    "* def means we are defining a function\n",
+    "* def function_name(param1, param2)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "def my_function(param1, param2):\n",
+    "   do some stuff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(my_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### return will let you store output from a function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x):\n",
+    "    return x+2 # note you can just add on the fly here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "y = my_func(2)\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### multiple params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x,y):\n",
+    "    return x+y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "y = my_func(5,2)\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### a bit more complicated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x,y):\n",
+    "    \n",
+    "    output = None\n",
+    "    z = x+y\n",
+    "    \n",
+    "    if z < 5:\n",
+    "        output = True\n",
+    "    else:\n",
+    "        output = False\n",
+    "        \n",
+    "    return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output = my_func(2,1)\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output = my_func(2,5)\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a function that</h3>\n",
+    "<p style=\"color:blue\">- takes as an input a list of integers</p>\n",
+    "<p style=\"color:blue\">- adds together all the tiems less than 10</p>\n",
+    "<p style=\"color:blue\">- returns the final sum</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### note python will assume the positioning of params\n",
+    "* consider the below\n",
+    "* the first param is a string\n",
+    "* the second is a number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x,y):\n",
+    "    x = x.lower()\n",
+    "    y = y + 2\n",
+    "    print(x)\n",
+    "    print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'int' object has no attribute 'lower'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-24-d492ee7d6bfe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"str\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-23-20206ed1ff95>\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'lower'"
+     ]
+    }
+   ],
+   "source": [
+    "#my_func(5,\"str\") # won't work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "str\n",
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(\"str\", 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### we could explicitly use the keyword, even if things are out of order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "str\n",
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(y = 5, x = \"str\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### but we can't do a keyword followed by a positional argument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "positional argument follows keyword argument (<ipython-input-3-f73cfd3c8222>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-f73cfd3c8222>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    my_func(y = 5, \"str\") # won't work\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(y = 5, \"str\") # won't work"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### we can use args to have a variable amount of arguments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(*args):\n",
+    "    for arg in args:\n",
+    "        print(arg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(1,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "5\n",
+      "6\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(1,2,5,6,2,3,4,5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a function that</h3>\n",
+    "<p style=\"color:blue\">- takes an inifinte amount of params</p>\n",
+    "<p style=\"color:blue\">- puts them all in a list</p>\n",
+    "<p style=\"color:blue\">- returns the list</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### we can use kwargs to have keyword arguments that are optional\n",
+    "* arguments are put into a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(**kwargs):\n",
+    "    print(type(kwargs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'dict'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(x = 2, y = 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(**kwargs):\n",
+    "    print(kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'x': 2, 'y': 3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(x = 2, y = 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x,y, **kwargs):\n",
+    "    val = x + y\n",
+    "    if \"z\" in kwargs:\n",
+    "        # remember, we can't access the value simply using\n",
+    "        # z, we have to grab it form the kwargs dictionary\n",
+    "        val+=kwargs[\"z\"]\n",
+    "    print(val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(5,7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "my_func() takes 2 positional arguments but 3 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-8751ad374ad8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: my_func() takes 2 positional arguments but 3 were given"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(5,2,4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(5,2,z=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a function that takes two params</h3>\n",
+    "<p style=\"color:blue\">- divids the first param by the second</p>\n",
+    "<p style=\"color:blue\">- searches for a third named \"third\"</p>\n",
+    "<p style=\"color:blue\">- if it exists add that to the division of the first two params</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### we can also set defaults for params\n",
+    "* the default can be overriden by passing in a value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x, y = 2):\n",
+    "    print(x + y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(2,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(x = 2, y = 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(x = 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### use of stars\n",
+    "* the star unpacks the tuple, saying this is a list of arguments, vs treat this list as one argument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(*args):\n",
+    "    for i in args:\n",
+    "        print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func([1,2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func(*[1,2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lambda Functions\n",
+    "* annonymous\n",
+    "* defined on the fly with one line\n",
+    "* multiple params but one expression\n",
+    "* commonly used in mapping, applying and filtering\n",
+    "* lambda params: expression\n",
+    "* https://docs.python.org/3/reference/expressions.html#lambda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[3, 4, 5]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_lambda = lambda x: x + 2\n",
+    "\n",
+    "a = [1,2,3]\n",
+    "\n",
+    "list(map(my_lambda, a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### but we don't even need to store the function in my_lambda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[3, 4, 5]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(map(lambda x: x+2, a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 5]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [2,3,4,5]\n",
+    "\n",
+    "list(filter(lambda x: x > 3, a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">write a lambda that</h3>\n",
+    "<p style=\"color:blue\">- maps to a list of strings and</p>\n",
+    "<p style=\"color:blue\">- replaces all the letter o with e</p>\n",
+    "<p style=\"color:blue\">- converts to upper case</p>\n",
+    "<p style=\"color:blue\">- .replace(item_replace, replace_with)</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## list comprehension\n",
+    "* memory efficient way to run a for loop and put the results in a list or iterable\n",
+    "* on the fly apply functions to the elements of a list\n",
+    "* similar to filtering and mapping\n",
+    "* https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [1,2,3,4,5]\n",
+    "b = []\n",
+    "for i in a:\n",
+    "    b.append(i)\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = [i for i in a]\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('a', 'b', 'c')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [\"A\", \"B\", \"C\"]\n",
+    "b = tuple(x.lower() for x in a)\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{(1, 'a'), (2, 'b'), (3, 'c')}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [1,2,3]\n",
+    "b = [\"a\",\"b\",\"c\"]\n",
+    "\n",
+    "my_dict = {(x[0],x[1]) for x in zip(a,b)}\n",
+    "my_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 5]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = [i for i in a if i > 3]\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1, -2, -3, -4, -5]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = [i*-1 for i in a]\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [\"A\", \"B\", \"C\"]\n",
+    "b = [x.lower() for x in a]\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_func = lambda x: x.lower()\n",
+    "a = [\"A\", \"B\", \"C\"]\n",
+    "b = [my_func(x) for x in a]\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">write a lambda that</h3>\n",
+    "<p style=\"color:blue\">- use comprehension to apply to a list of numbers</p>\n",
+    "<p style=\"color:blue\">- the lambda should return the remainder of the number divided by 2</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "51%2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "50%2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generators\n",
+    "* special function that returns a lazy iterator.\n",
+    "* do not store contents in memory.\n",
+    "* Instead, the state of the function is remembered.\n",
+    "* Keep track of where we are in the iterator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst = [num for num in range(5)]\n",
+    "gen = (num for num in range(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7fcd988c25d0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in gen:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## use next() to grab the next item in the generator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gen = (num for num in range(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### can use yield instead of return, to return the next item of the generator.  yield automatically tells python this is a generator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def infinite_sequence():\n",
+    "    for i in [num for num in range(5)]:\n",
+    "        yield i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gen = infinite_sequence()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object infinite_sequence at 0x7fcd988c2350>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in gen:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gen = infinite_sequence()\n",
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### very memory efficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "81528064"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nums_squared_lc = [i * 2 for i in range(10000000)]\n",
+    "sys.getsizeof(nums_squared_lc)#/ 1e+9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "128"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# returns bytes\n",
+    "nums_squared_lc = (i * 2 for i in range(10000000))\n",
+    "sys.getsizeof(nums_squared_lc)#/ 1e+9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### could use generator for dealing with text files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "csv = [\n",
+    "    \"permalink,company,numEmps,category,city,state,fundedDate,raisedAmt,raisedCurrency,round\",\n",
+    "    \"digg,Digg,60,web,San Francisco,CA,1-Dec-06,8500000,USD,b\",\n",
+    "    \"digg,Digg,60,web,San Francisco,CA,1-Oct-05,2800000,USD,a\",\n",
+    "    \"facebook,Facebook,450,web,Palo Alto,CA,1-Sep-04,500000,USD,angel\",\n",
+    "    \"facebook,Facebook,450,web,Palo Alto,CA,1-May-05,12700000,USD,a\",\n",
+    "    \"photobucket,Photobucket,60,web,Palo Alto,CA,1-Mar-05,3000000,USD,a\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "file_name = \"techcrunch.csv\"\n",
+    "\n",
+    "# declares a generator\n",
+    "lines = (line for line in open(file_name))\n",
+    "\n",
+    "# split the lines on the comma\n",
+    "list_line = (s.rstrip().split(\",\") for s in lines)\n",
+    "\n",
+    "# gives us the next line\n",
+    "cols = next(list_line)\n",
+    "\n",
+    "# this will be helpful for filtering, keys are column names\n",
+    "# \n",
+    "company_dicts = (dict(zip(cols, data)) for data in list_line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7fcd68774450>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lines = (line for line in csv)\n",
+    "lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7fcd687745d0>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_line = (s.rstrip().split(\",\") for s in lines)\n",
+    "list_line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['permalink',\n",
+       " 'company',\n",
+       " 'numEmps',\n",
+       " 'category',\n",
+       " 'city',\n",
+       " 'state',\n",
+       " 'fundedDate',\n",
+       " 'raisedAmt',\n",
+       " 'raisedCurrency',\n",
+       " 'round']"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(list_line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['digg',\n",
+       " 'Digg',\n",
+       " '60',\n",
+       " 'web',\n",
+       " 'San Francisco',\n",
+       " 'CA',\n",
+       " '1-Dec-06',\n",
+       " '8500000',\n",
+       " 'USD',\n",
+       " 'b']"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(list_line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exception Handling and Errors\n",
+    "* https://docs.python.org/3/tutorial/errors.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### syntax errors\n",
+    "* missing parenth or bracket maybe, some formatting issue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-76-35c08edd0df2>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-76-35c08edd0df2>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print((2)))\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### exception errors:\n",
+    "* you've tried to do something that can't be done"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-78-fad870a50e27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "print(5/0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "can only concatenate str (not \"int\") to str",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-79-89309f5042e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"string\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"string\" + 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### assert\n",
+    "* logical test\n",
+    "* if the test isn't met we raise an assertion error\n",
+    "* assert logical, error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "The length of x is not greater than 3",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-36-523a2a17546a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The length of x is not greater than 3\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m: The length of x is not greater than 3"
+     ]
+    }
+   ],
+   "source": [
+    "x = [2,3]\n",
+    "\n",
+    "assert len(x) > 3, \"The length of x is not greater than 3\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "x is empty",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-37-9bd7d8a13151>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"x is empty\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m: x is empty"
+     ]
+    }
+   ],
+   "source": [
+    "x = []\n",
+    "\n",
+    "assert len(x) != 0, \"x is empty\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### raise\n",
+    "* we could also raise an exception"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "Exception",
+     "evalue": "x should not exceed 5.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-39-f2f500004ca1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x should not exceed 5.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mException\u001b[0m: x should not exceed 5."
+     ]
+    }
+   ],
+   "source": [
+    "x = 10\n",
+    "if x > 5:\n",
+    "    raise Exception('x should not exceed 5.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### try and except\n",
+    "* tell python to try a block of code, if there is an error, revert to except"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found an error.  Item String isn't a number or float\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = [1,2,3,4,\"String\"]\n",
+    "\n",
+    "for i in a:\n",
+    "    \n",
+    "    try:\n",
+    "        i = i+2\n",
+    "    except:\n",
+    "        print(\"Found an error.  Item {} isn't a number or float\".format(i))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### plenty of built in exception/errors classese in python\n",
+    "* for instance, IndexError\n",
+    "* NameError\n",
+    "* https://docs.python.org/3/library/exceptions.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-83-070992024e9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "a = [2]\n",
+    "a[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'z' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-84-c01ac3f50fdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'z' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### excepting the error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "An error occurred\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = [1, 2, 3] \n",
+    "try:  \n",
+    "    print(a[1]) \n",
+    "  \n",
+    "    # Throws error since there are only 3 elements in array \n",
+    "    print(a[3])\n",
+    "    \n",
+    "except IndexError: \n",
+    "    print(\"An error occurred\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### raise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-97-b6c41dcfb79b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"string\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0;31m# raises the error that was caught\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
+     ]
+    }
+   ],
+   "source": [
+    "a = 5\n",
+    "\n",
+    "try:\n",
+    "    print(a + \"string\")\n",
+    "except TypeError:\n",
+    "    raise # raises the error that was caught"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb
new file mode 100644
index 0000000..7fdd6a9
--- /dev/null
+++ b/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb	
@@ -0,0 +1,1876 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### OOO\n",
+    "* simple OOO example\n",
+    "* classes - template of objects\n",
+    "* objects - member or instance of class\n",
+    "* classes can have\n",
+    "    * characteristics/attribuets\n",
+    "    * methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple framework for a dog\n",
+    "class dog:\n",
+    "    \n",
+    "    # these are params here, things we set when\n",
+    "    # we initialize our instance of our class\n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'blue'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'sheperd'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.breed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'breed',\n",
+       " 'color',\n",
+       " 'name']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(my_dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.dog"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(my_dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple framework for a dog\n",
+    "class dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        \n",
+    "    def speak(self):\n",
+    "        xxx = self.color\n",
+    "        print(\"My color is \" + xxx)\n",
+    "        \n",
+    "    def change_color(self, carrot):\n",
+    "        self.color = carrot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method dog.speak of <__main__.dog object at 0x000001F1CE85A9C8>>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.speak"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My color is blue\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_dog.speak()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'blue'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'red'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.change_color(\"red\")\n",
+    "my_dog.color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple framework for a dog\n",
+    "class dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name, distance=0):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.distance += distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")\n",
+    "my_dog.distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.walk(5)\n",
+    "my_dog.distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.walk(12)\n",
+    "my_dog.distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.walk(18)\n",
+    "my_dog.distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inheritence\n",
+    "* Inherit properties and functions from other objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.distance += distance\n",
+    "        \n",
+    "class Retreiver(Dog):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'blue'"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'breed',\n",
+       " 'color',\n",
+       " 'distance',\n",
+       " 'name',\n",
+       " 'walk']"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.distance += distance\n",
+    "        \n",
+    "class Retreiver(Dog):\n",
+    "\n",
+    "    def speak(self):\n",
+    "        print(\"My name is {}\".format(self.name))\n",
+    "        \n",
+    "    def blink182(self):\n",
+    "        print(\"My age is {}\".format(self.age))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() got an unexpected keyword argument 'color'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-62-6f94a433d382>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mRetreiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"blue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"retreiver\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Rex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblink182\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'color'"
+     ]
+    }
+   ],
+   "source": [
+    "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
+    "r.blink182()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Dog' object has no attribute 'speak'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-56-800ba7303882>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mDog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"blue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"retreiver\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Rex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspeak\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Dog' object has no attribute 'speak'"
+     ]
+    }
+   ],
+   "source": [
+    "r =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
+    "r.speak()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Class Methods\n",
+    "* takes cls as the first input\n",
+    "* bound to the class, not the instance of the object\n",
+    "* can access class variables though not instance variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    class_atr = 0\n",
+    "    \n",
+    "    @classmethod\n",
+    "    def speak(cls, speak):\n",
+    "        print(speak)\n",
+    "        \n",
+    "    @classmethod\n",
+    "    def add(cls,x,y):\n",
+    "        return x+y\n",
+    "    \n",
+    "    @classmethod\n",
+    "    def printer(cls):\n",
+    "        print(cls.class_atr)\n",
+    "        \n",
+    "    @classmethod\n",
+    "    def class_add(cls):\n",
+    "        cls.class_atr = cls.class_atr + 10\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.distance = 0\n",
+    "        class_atr=+1\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.distance = Dog.add(self.distance, distance)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rex =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "rex.printer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n"
+     ]
+    }
+   ],
+   "source": [
+    "rex.class_add()\n",
+    "rex.printer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "say something\n"
+     ]
+    }
+   ],
+   "source": [
+    "r.speak(\"say something\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "say something\n"
+     ]
+    }
+   ],
+   "source": [
+    "Dog.speak(\"say something\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.walk(5)\n",
+    "r.walk(10)\n",
+    "r.distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Static Method\n",
+    "* neither bound to the instance or class\n",
+    "* no access to the instance or class attribuets\n",
+    "* could be a standalone function but we want to keep it in the class\n",
+    "* for instance, distance measures inside a KNN class.  then we can reference the functions when we have a fit method\n",
+    "* aren't super useful due to limited access of information\n",
+    "* utility type funciton"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Distance:\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def add(a,b):\n",
+    "        print(a+b)\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def sub(a,b):\n",
+    "        print(a-b)\n",
+    "    \n",
+    "    def __init__(self,x,y):\n",
+    "        self.x = x\n",
+    "        self.y = y\n",
+    "        \n",
+    "    def operate(self, how):\n",
+    "        if how == \"add\":\n",
+    "            Distance.add(self.x, self.y)\n",
+    "        else:\n",
+    "            Distance.sub(self.x, self.y)\n",
+    "        \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = Distance(10,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n"
+     ]
+    }
+   ],
+   "source": [
+    "d.operate(\"add\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "d.operate(\"sub\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Characteristics/Properties/Attributes\n",
+    "* accessed using dot notation\n",
+    "* methods are used using dot notation and parentheses ()\n",
+    "* we can now read the sklearn documentation that much better\n",
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
+    "* since sklearn documentation is consistent, we can easily look at any if the page and disect how the data structure works\n",
+    "    * what methods we can use\n",
+    "    * what characteristics we can access\n",
+    "    * what params we can pass\n",
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DateTimes in Python\n",
+    "* https://docs.python.org/3/library/datetime.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "from datetime import date"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get Todays Date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-11-24\n"
+     ]
+    }
+   ],
+   "source": [
+    "today = date.today()\n",
+    "print(today)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "today.day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "today.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2019"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "today.year"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Weekdays start at monday and index at 0\n",
+    "* 0:Monday\n",
+    "* 1:Tuesday\n",
+    "* 2:Wednesday\n",
+    "* 3:Thursday\n",
+    "* 4:Friday\n",
+    "* 5:Saturday\n",
+    "* 6:Sunday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "today.weekday()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__add__',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__radd__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rsub__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " 'ctime',\n",
+       " 'day',\n",
+       " 'fromisoformat',\n",
+       " 'fromordinal',\n",
+       " 'fromtimestamp',\n",
+       " 'isocalendar',\n",
+       " 'isoformat',\n",
+       " 'isoweekday',\n",
+       " 'max',\n",
+       " 'min',\n",
+       " 'month',\n",
+       " 'replace',\n",
+       " 'resolution',\n",
+       " 'strftime',\n",
+       " 'timetuple',\n",
+       " 'today',\n",
+       " 'toordinal',\n",
+       " 'weekday',\n",
+       " 'year']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(today)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get Time and Date Now"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-11-24 12:06:33.330468\n"
+     ]
+    }
+   ],
+   "source": [
+    "now = datetime.now()\n",
+    "print(now)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__add__',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__radd__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rsub__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " 'astimezone',\n",
+       " 'combine',\n",
+       " 'ctime',\n",
+       " 'date',\n",
+       " 'day',\n",
+       " 'dst',\n",
+       " 'fold',\n",
+       " 'fromisoformat',\n",
+       " 'fromordinal',\n",
+       " 'fromtimestamp',\n",
+       " 'hour',\n",
+       " 'isocalendar',\n",
+       " 'isoformat',\n",
+       " 'isoweekday',\n",
+       " 'max',\n",
+       " 'microsecond',\n",
+       " 'min',\n",
+       " 'minute',\n",
+       " 'month',\n",
+       " 'now',\n",
+       " 'replace',\n",
+       " 'resolution',\n",
+       " 'second',\n",
+       " 'strftime',\n",
+       " 'strptime',\n",
+       " 'time',\n",
+       " 'timestamp',\n",
+       " 'timetuple',\n",
+       " 'timetz',\n",
+       " 'today',\n",
+       " 'toordinal',\n",
+       " 'tzinfo',\n",
+       " 'tzname',\n",
+       " 'utcfromtimestamp',\n",
+       " 'utcnow',\n",
+       " 'utcoffset',\n",
+       " 'utctimetuple',\n",
+       " 'weekday',\n",
+       " 'year']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(now)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "now.second"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "now.minute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "now.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "now.day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "now.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2019"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "now.year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "now.weekday()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a variable of the current date</h3>\n",
+    "<p style=\"color:blue\">- make a list with the weekdays</p>\n",
+    "<p style=\"color:blue\">- convert the weekday() integer to the actual date name</p>\n",
+    "<p style=\"color:blue\">- print the below, without hard coding it</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Today is a Friday\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Today is a Friday\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Format Datetime output using strftime\n",
+    "* (%y/%Y – Year)\n",
+    "* (%a/%A- weekday)\n",
+    "* (%b/%B- month)\n",
+    "* (%d - day of month)\n",
+    "* %c- indicates the local date and time\n",
+    "* 12 hours time is declared [print now.strftime(\"%I:%M:%S %P) ]\n",
+    "* 24 hours time is declared [print now.strftime(\"%H:%M\")] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "now = datetime.now()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019\n",
+      "19\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.strftime(\"%Y\"))\n",
+    "print(now.strftime(\"%y\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sunday\n",
+      "Sun\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.strftime(\"%A\"))\n",
+    "print(now.strftime(\"%a\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "November\n",
+      "Nov\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.strftime(\"%B\"))\n",
+    "print(now.strftime(\"%b\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/24/19\n",
+      "24\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.strftime(\"%D\"))\n",
+    "print(now.strftime(\"%d\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sun Nov 24 12:10:24 2019\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.strftime(\"%c\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### we can string these together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-November-24\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.strftime(\"%Y-%B-%d\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019,November,24\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.strftime(\"%Y,%B,%d\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19-Nov-24\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.strftime(\"%y-%b-%d\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12:10:24\n"
+     ]
+    }
+   ],
+   "source": [
+    "# I is the hour, # M is minutes, #S is seconds\n",
+    "# army time\n",
+    "print(now.strftime(\"%I:%M:%S\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12:10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 24 hour clock\n",
+    "print(now.strftime(\"%H:%M\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Use timedelta to add and subtract dates\n",
+    "* does not support years, because the duration of a year depends on which year (for example, leap years have Feb 29)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import timedelta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-11-24 12:19:01.737490\n",
+      "2019-12-04 17:34:01.737781\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(datetime.now())\n",
+    "print(datetime.now() + timedelta(days=10, hours = 5, minutes = 15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-11-24 12:19:09.017928\n",
+      "2019-11-14 07:04:09.018214\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(datetime.now())\n",
+    "print(datetime.now() - timedelta(days=10, hours = 5, minutes = 15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-11-24 12:19:44.676823\n",
+      "2019-12-08 12:19:44.677028\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(datetime.now())\n",
+    "print(datetime.now() + timedelta(weeks=2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019-11-24 12:19:54.454511\n",
+      "2019-11-10 12:19:54.454799\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(datetime.now())\n",
+    "print(datetime.now() - timedelta(weeks=2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.datetime"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# returns a datetime so we can extract info\n",
+    "x = datetime.now() - timedelta(weeks=2)\n",
+    "type(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2019"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.weekday()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a variable with the current date</h3>\n",
+    "<p style=\"color:blue\">- add/subtract a year</p>\n",
+    "<p style=\"color:blue\">- add subtract a year and 2 weeks</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving Objects in Python\n",
+    "* serialization, marshaling, and flattening. \n",
+    "* same—to save an object to a file for later retrieval\n",
+    "* accomplishes this by writing the object as one long stream of bytes. \n",
+    "* pickling also used to save models in sklearn\n",
+    "* https://docs.python.org/3/library/pickle.html\n",
+    "* https://scikit-learn.org/stable/modules/model_persistence.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = [\"a\", \"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open('test_pickle.pkl', 'wb') \n",
+    "pickle.dump(test, file)\n",
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delete from memory\n",
+    "del test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open('test_pickle.pkl', 'rb') \n",
+    "test = pickle.load(file)\n",
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b']"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### using with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "item = \"some string I made\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test_pickle.pkl\", \"wb\") as file:\n",
+    "    pickle.dump(item, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#del item\n",
+    "#item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test_pickle.pkl\", \"rb\") as file:\n",
+    "    item = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'some string I made'"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "item"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a python object</h3>\n",
+    "<p style=\"color:blue\">- save the object</p>\n",
+    "<p style=\"color:blue\">- delete the object from memory</p>\n",
+    "<p style=\"color:blue\">- reload it</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb
new file mode 100644
index 0000000..8966296
--- /dev/null
+++ b/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb	
@@ -0,0 +1,7817 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numpy Arrays\n",
+    "* multi dimensional arrays or data\n",
+    "* can be 1d, 2d, 3d\n",
+    "* very computationally efficient to perform linear algebra operations over\n",
+    "* elementwise operations\n",
+    "* row and column wise calculations\n",
+    "* https://docs.scipy.org/doc/numpy-1.11.0/numpy-user-1.11.0.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Arrays vs. Matrix Class\n",
+    "* \"Short Answer: Use Arrays.\"\n",
+    "* They are the standard vector/matrix/tensor type of numpy. Many numpy functions return arrays, not matrices.\n",
+    "* The array class is intended to be a general-purposen-dimensional array for many kinds of numerical computing, whilematrixis intended to facilitate linear algebracomputations specifically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1-D arrays\n",
+    "* note his only has one demension\n",
+    "* we could use a method called reshape to change this (we will get into this more later on)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1,2,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5,)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = np.reshape(a, (5,1))\n",
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[1,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### indexing and slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 3])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in a:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### looping\n",
+    "* numpy loops through the left most axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,4]\n",
+    "])\n",
+    "\n",
+    "array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "[2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# since we have 2 items in the first axis we will end up with 2 items in our iteration\n",
+    "for i in array:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3,4],\n",
+    "    [2,3,4,4],\n",
+    "    [2,3,4,4]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,7,4],\n",
+    "    [4,6,9,4],\n",
+    "    [2,3,4,4]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3, 4)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# depth 2\n",
+    "# rows 3\n",
+    "# columns 4\n",
+    "tensor = np.array([array, array1])\n",
+    "tensor.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* remember numpy will iterate left most index\n",
+    "* in this 3-d cube, it's iterating the dpeth\n",
+    "* so we end up printing our 2 initial matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3 4]\n",
+      " [2 3 4 4]\n",
+      " [2 3 4 4]]\n",
+      "[[5 6 7 4]\n",
+      " [4 6 9 4]\n",
+      " [2 3 4 4]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tensor:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### performing matrix operations\n",
+    "* axis 0 is the y axis\n",
+    "* the up and down axis\n",
+    "* the number of rows\n",
+    "* so any operation done on axis 0 is going to be columns\n",
+    "* done up and down\n",
+    "* axis 1 is the length of our array\n",
+    "* any summing or anything is done across our dataframe\n",
+    "* doing some operation to our rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3,4],\n",
+    "    [2,3,4,4],\n",
+    "    [2,3,4,4]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 4)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 4],\n",
+       "       [2, 3, 4, 4],\n",
+       "       [2, 3, 4, 4]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5,  8, 11, 12])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 13, 13])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(array, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2., 3., 4., 4.])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.median(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* the other option would be to loop which is far more costly of an operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "13\n",
+      "13\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in array:\n",
+    "    print(sum(i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n",
+      "8\n",
+      "11\n",
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in array.T:\n",
+    "    print(sum(i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 4],\n",
+       "       [2, 3, 4, 4],\n",
+       "       [2, 3, 4, 4]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### tranpose, turn rows into columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 2],\n",
+       "       [2, 3, 3],\n",
+       "       [3, 4, 4],\n",
+       "       [4, 4, 4]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">Make a 2-d array in numpy:</h3>\n",
+    "<p style=\"color:blue\">- find the row wise mean</p>\n",
+    "<p style=\"color:blue\">- find the column wise mean</p>\n",
+    "<p style=\"color:blue\">- do the same for the transpose of the array you've made</p>\n",
+    "<p style=\"color:blue\">- check if matrix square</p>\n",
+    "<p style=\"color:blue\">- multiple the matrix by transpose</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3. 3. 3. 3.]\n",
+      "[4. 4. 4.]\n",
+      "[4. 4. 4.]\n",
+      "[3. 3. 3. 3.]\n",
+      "Not a square!\n",
+      "[12. 12. 12.]\n",
+      "[12. 12. 12.]\n",
+      "Square as me!\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.ones([3,4])\n",
+    "\n",
+    "\n",
+    "print(a.sum(0))\n",
+    "print(a.sum(1))\n",
+    "\n",
+    "b = a.T\n",
+    "\n",
+    "print(b.sum(0))\n",
+    "print(b.sum(1))\n",
+    "\n",
+    "\n",
+    "if(a.shape[0]!=a.shape[1]):\n",
+    "    print(\"Not a square!\")\n",
+    "\n",
+    "\n",
+    "c = np.dot(a,b)\n",
+    "\n",
+    "print(c.sum(0))\n",
+    "print(c.sum(1))\n",
+    "\n",
+    "\n",
+    "if(c.shape[0]!=c.shape[1]):\n",
+    "    print(\"Not a square!\")\n",
+    "else:\n",
+    "    print(\"Square as me!\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### other useful metrics\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.quantile.html\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1101, 2020, 3212],\n",
+       "       [2212, 3112, 4121]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([555.5, 546. , 454.5])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.std(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1656.5, 2566. , 3666.5])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1101, 2020, 3212])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.min(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2212, 3112, 4121])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1656.5, 2566. , 3666.5])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.quantile(array,.5, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1106.555, 2025.46 , 3216.545])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.percentile(array,.5, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### reshape\n",
+    "* change the shape of your data\n",
+    "* adding indices\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this is indexed by one element\n",
+    "array = np.array([1,2,3,4])\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4,)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 1)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.reshape(array, (4,1))\n",
+    "array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array[0][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we've now added two dimension using reshape\n",
+    "array1 = np.reshape(array,(2,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2],\n",
+       "       [3, 4]])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([1,2,3,4,5,6,7,8])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5, 6, 7, 8])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array1 = np.reshape(array, (2,2,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[1, 2],\n",
+       "        [3, 4]],\n",
+       "\n",
+       "       [[5, 6],\n",
+       "        [7, 8]]])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 2, 2)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2]\n",
+      " [3 4]] \n",
+      "\n",
+      "[[5 6]\n",
+      " [7 8]] \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in array1:\n",
+    "    print(i, \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### norm of a vector\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.norm.html\n",
+    "* defaults to the l2 norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.7416573867739413"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([1,2,3])\n",
+    "np.linalg.norm(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.7416573867739413"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### matrix multiplication/dot product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2],\n",
+    "    [2,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6],\n",
+    "    [4,6]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[13, 18],\n",
+       "       [22, 30]])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# shortcut\n",
+    "array@array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[13, 18],\n",
+       "       [22, 30]])"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.matmul(array, array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[13, 18],\n",
+       "       [22, 30]])"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dot(array, array1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### remember the dimension constraints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,2],\n",
+    "    [4,6,1]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3) (2, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(array.shape, array1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "The dimensions aren't aligned",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-57-fe46837ad8c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0marray1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0marray1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
+     ]
+    }
+   ],
+   "source": [
+    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
+    "\n",
+    "array@array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3) (3, 6)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[19, 27, 10, 17, 14, 21],\n",
+       "       [28, 39, 13, 25, 21, 28]])"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5],\n",
+    "    [2,3,2,2,1,3]\n",
+    "])\n",
+    "\n",
+    "print(array.shape, array1.shape)\n",
+    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
+    "\n",
+    "array@array1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
+    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
+    "<p style=\"color:blue\">- tests to make sure they can be multipled using assert</p>\n",
+    "<p style=\"color:blue\">- return the dot product of the two matrices</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[19 27 10 17 14 21]\n",
+      " [28 39 13 25 21 28]]\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "The dimensions aren't aligned",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0marray2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36mrafdot\u001b[1;34m(m1, m2)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0mm2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m array = np.array([\n",
+      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
+     ]
+    }
+   ],
+   "source": [
+    "def rafdot(m1, m2):\n",
+    "    assert m1.shape[1] == m2.shape[0], \"The dimensions aren't aligned\"\n",
+    "    return m1@m2\n",
+    "\n",
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5],\n",
+    "    [2,3,2,2,1,3]\n",
+    "])\n",
+    "\n",
+    "array2 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5]])\n",
+    "\n",
+    "c = rafdot(array,array1)\n",
+    "print(c)\n",
+    "\n",
+    "c = rafdot(array,array2)\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### broadcasting\n",
+    "* expand the dimension to add to a matrix\n",
+    "* https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([1,1])\n",
+    "array1 = np.reshape(array1, (2,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [2, 3, 3]])"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1],\n",
+       "       [1]])"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3, 4],\n",
+       "       [3, 4, 4]])"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.add(array, array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [1, 2, 2]])"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.subtract(array, array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3, 4],\n",
+       "       [3, 4, 4]])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array + array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [1, 2, 2]])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array - array1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### elementwise squaring\n",
+    "* this is the elementwise operations arrays that make arrays so powerful"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1,  32, 243],\n",
+       "       [ 32, 243, 243]])"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array**5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1,  32, 243],\n",
+       "       [ 32, 243, 243]])"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.power(array, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
+    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
+    "<p style=\"color:blue\">- iterates the rows and columns and squares each element</p>\n",
+    "<p style=\"color:blue\">- puts the squared items in a list</p>\n",
+    "<p style=\"color:blue\">- returns the list</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[1, 4, 9],\n",
+       "        [4, 9, 9]]), array([[25, 36,  4, 25,  9,  4],\n",
+       "        [16, 36,  1,  9, 16, 25],\n",
+       "        [ 4,  9,  4,  4,  1,  9]])]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def raf_square(m1,m2):\n",
+    "    flist = []\n",
+    "    for k in m1, m2:\n",
+    "        c = k*k\n",
+    "        flist.append(c)\n",
+    "    return flist\n",
+    "\n",
+    "raf_square(array,array1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### argmax and min\n",
+    "* returns the index of the max/min value\n",
+    "* In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([1,2,3,4,5])\n",
+    "np.argmax(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmin(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,6,3],\n",
+    "    [3,10,5],\n",
+    "    [15,8,9]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(array, axis = 0) # column level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(array, axis = 1) # row level"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### let's say these are distances\n",
+    "* each row is an item\n",
+    "* and each column is the distance to a cluster\n",
+    "array = np.array(["
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,6,3],\n",
+    "    [3,10,5],\n",
+    "    [15,8,9]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this could then give us the index of the min distance\n",
+    "np.argmin(array, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "items = [\"item_A\", \"item_B\", \"item_C\"]\n",
+    "clusters = [\"A\", \"B\", \"C\"]\n",
+    "closest_cluster = np.argmin(array, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A', 'A', 'B']"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "closest_cluster_labels = [clusters[idx] for idx in closest_cluster]\n",
+    "closest_cluster_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('item_A', 'A'), ('item_B', 'A'), ('item_C', 'B')]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(zip(items, closest_cluster_labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['item_A', 'A'],\n",
+       "       ['item_B', 'A'],\n",
+       "       ['item_C', 'B']], dtype='<U6')"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([\n",
+    "    items,\n",
+    "    closest_cluster_labels\n",
+    "]).T\n",
+    "\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['item_A', 'B'],\n",
+       "       ['item_B', 'B'],\n",
+       "       ['item_C', 'B']], dtype='<U6')"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([\n",
+    "    items,\n",
+    "    [clusters[idx] for idx in np.argmin(array, axis = 1)]\n",
+    "]).T\n",
+    "\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(np.array([1,1,2,2,5,5,5]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### argsort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([1,3,6,0,51,12])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_args = np.argsort(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 3, 6, 12, 51]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[array[idx] for idx in sorted_args]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,6,3],\n",
+    "    [3,10,5],\n",
+    "    [15,8,9]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "items = [\"item_A\", \"item_B\", \"item_C\"]\n",
+    "clusters = [\"A\", \"B\", \"C\"]\n",
+    "closest_cluster = np.argmin(array, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  6,  3],\n",
+       "       [ 3, 10,  5],\n",
+       "       [15,  8,  9]])"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 2, 1],\n",
+       "       [0, 2, 1],\n",
+       "       [1, 2, 0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argsort(array, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def myfunc(x):\n",
+    "    items = [\"item_A\", \"item_B\", \"item_C\"]\n",
+    "    return items[x]\n",
+    "\n",
+    "vectorized_func = np.vectorize(myfunc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-84-86bed837003f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvectorized_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2089\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2091\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2092\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2093\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[1;34m(self, func, args)\u001b[0m\n\u001b[0;32m   2165\u001b[0m                       for a in args]\n\u001b[0;32m   2166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2167\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2169\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-83-df07eceacbb6>\u001b[0m in \u001b[0;36mmyfunc\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmyfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"item_A\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_B\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_C\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mvectorized_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvectorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmyfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "vectorized_func(array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distance Measures\n",
+    "* used in many ML algorithms\n",
+    "* rec systems, kmeans, knn, decision trees\n",
+    "* https://docs.scipy.org/doc/scipy/reference/spatial.distance.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### euclidean distance\n",
+    "* magnitude makes a difference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1,2,3])\n",
+    "b = np.array([4,2,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.605551275463989"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(sum((a-b)**2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### cosine similarity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6415330278717848"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dot(a, b)/(np.linalg.norm(a)*np.linalg.norm(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### jaccard coefficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [\"the\", \"old\", \"man\", \"by\", \"the\", \"sea\"]\n",
+    "b = [\"the\", \"old\", \"man\", \"by\", \"the\", \"chair\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def jaccard_similarity(a, b):\n",
+    "    return len(set(a).intersection(set(b))) / len(set(a).union(set(b)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6666666666666666"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jaccard_similarity(a,b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### distance matrix\n",
+    "* pdist returns a reduced distance matrix, since they are symetrical\n",
+    "* squareform makes this a dense form"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.spatial.distance import pdist, squareform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3,4],\n",
+    "    [2,4,2,1],\n",
+    "    [2,3,4,5]\n",
+    "    \n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.        , 3.87298335, 2.        ],\n",
+       "       [3.87298335, 0.        , 4.58257569],\n",
+       "       [2.        , 4.58257569, 0.        ]])"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "squareform(pdist(array, \"euclidean\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.1 0.9]\n",
+      "[0. 1.]\n",
+      "[[0. 1.]\n",
+      " [1. 1.]]\n",
+      "(array([0.8, 0.1]), array([], dtype=float64), 2, array([1.61803399, 0.61803399]))\n",
+      "y = 0.09999999999999992x + 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
+      "  \n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "xdata = np.array([0.0,1.0])\n",
+    "ydata = np.array([0.1,0.9])\n",
+    "\n",
+    "data_pred = xdata.T * xdata.T\n",
+    "\n",
+    "A = np.vstack([xdata,np.ones(len(xdata))]).T\n",
+    "\n",
+    "\n",
+    "print(ydata)\n",
+    "print(xdata)\n",
+    "print(A)\n",
+    "\n",
+    "\n",
+    "print (np.linalg.lstsq(A, ydata))\n",
+    "\n",
+    "# the model has the following face y = a + bx\n",
+    "b, a = np.linalg.lstsq(A, ydata)[0]\n",
+    "print(\"y = \"+str(a)+'x + '+str(b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#find if a number is prime\n",
+    "#find if it is a prime of goldbach and the primes that build it\n",
+    "\n",
+    "\n",
+    "def isprime(number):\n",
+    "    if(number%2):\n",
+    "        if(number!=2):\n",
+    "            return 0\n",
+    "        else:\n",
+    "            return 1\n",
+    "    dividers = np.arange(number,1,2)\n",
+    "    print(dividers)\n",
+    "    for k in dividers:\n",
+    "        if number%divider:\n",
+    "            return 0\n",
+    "    return 1 \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pandas\n",
+    "* data wrangling\n",
+    "* groupby\n",
+    "* extension of numpys to add a dataframe capability\n",
+    "* similar to R dataframes\n",
+    "* integrates with numpy\n",
+    "* https://pandas.pydata.org/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series\n",
+    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html\n",
+    "* One-dimensional ndarray with axis labels (including time series)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vals = [1,2,3,4,5]\n",
+    "idxs = [\"a\",\"b\",\"c\",\"d\",\"e\"]\n",
+    "\n",
+    "my_series = pd.Series(vals, idxs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1\n",
+       "b    2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_series[[\"a\", \"b\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dataframe\n",
+    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\n",
+    "* core object in pandas\n",
+    "* 2 dimensional\n",
+    "* index and columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array1 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5],\n",
+    "    [2,3,2,2,1,3]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 6)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0  1  2  3  4  5\n",
+       "0  5  6  2  5  3  2"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0  1  2  3  4  5\n",
+       "0  5  6  2  5  3  2\n",
+       "1  4  6  1  3  4  5\n",
+       "2  2  3  2  2  1  3"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=3, step=1)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=6, step=1)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### change headers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>e</th>\n",
+       "      <th>f</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b  c  d  e  f\n",
+       "0  5  6  2  5  3  2\n",
+       "1  4  6  1  3  4  5\n",
+       "2  2  3  2  2  1  3"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    int32\n",
+       "b    int32\n",
+       "c    int32\n",
+       "d    int32\n",
+       "e    int32\n",
+       "f    int32\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### accessing columns\n",
+    "* returns series\n",
+    "* index still attached to the series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    5\n",
+       "1    4\n",
+       "2    2\n",
+       "Name: a, dtype: int32"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"a\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df[\"a\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 4, 2]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"a\"].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=3, step=1)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"a\"].index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "0  5  6\n",
+       "1  4  6\n",
+       "2  2  3"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"a\", \"b\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### index subsetting\n",
+    "* iloc[] is position selecting\n",
+    "* iloc[rows, columns]\n",
+    "* rows/columns can be a list of indicies (integers) or use : to seperate a range\n",
+    "* [2:] means give me 2 and everything after for slicing\n",
+    "* [1] means give me index 1\n",
+    "* [1,2,3] means give me index 1,2,3\n",
+    "* [1],[0,1] means give me row 1 and columns 0 and 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    4\n",
+       "b    6\n",
+       "c    1\n",
+       "d    3\n",
+       "e    4\n",
+       "f    5\n",
+       "Name: 1, dtype: int32"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    4\n",
+       "b    6\n",
+       "c    1\n",
+       "Name: 1, dtype: int32"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# need integer based subsetting\n",
+    "df.iloc[1,[0,1,2]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>e</th>\n",
+       "      <th>f</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b  c  d  e  f\n",
+       "1  4  6  1  3  4  5\n",
+       "2  2  3  2  2  1  3"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[[1,2]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>e</th>\n",
+       "      <th>f</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <td>2</td>\n",
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+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
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+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
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+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
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+      ],
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+       "   a  b  c  d  e  f\n",
+       "0  5  6  2  5  3  2\n",
+       "2  2  3  2  2  1  3\n",
+       "1  4  6  1  3  4  5"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[[0,2,1]]"
+   ]
+  },
+  {
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+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <td>2</td>\n",
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+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
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+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b  c  d  e  f\n",
+       "0  5  6  2  5  3  2\n",
+       "1  4  6  1  3  4  5\n",
+       "2  2  3  2  2  1  3"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[0:,0:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th>a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
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+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
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+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a\n",
+       "0  5\n",
+       "1  4\n",
+       "2  2"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[0:,0:1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "    <tr style=\"text-align: right;\">\n",
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+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "1  4  6"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[[1],[0,1]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### loc\n",
+    "* used if we have labels and not integer indec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=3, step=1)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.index = [\"aa\", \"bb\", \"cc\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>e</th>\n",
+       "      <th>f</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>aa</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>bb</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cc</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a  b  c  d  e  f\n",
+       "aa  5  6  2  5  3  2\n",
+       "bb  4  6  1  3  4  5\n",
+       "cc  2  3  2  2  1  3"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    5\n",
+       "b    6\n",
+       "c    2\n",
+       "d    5\n",
+       "e    3\n",
+       "f    2\n",
+       "Name: aa, dtype: int32"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[\"aa\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>e</th>\n",
+       "      <th>f</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>aa</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>bb</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a  b  c  d  e  f\n",
+       "aa  5  6  2  5  3  2\n",
+       "bb  4  6  1  3  4  5"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[[\"aa\", \"bb\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>aa</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a  b\n",
+       "aa  5  6"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[[\"aa\"], [\"a\",\"b\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">how might we</h3>\n",
+    "<p style=\"color:blue\">- filter if we have a large number of indices?</p>\n",
+    "<p style=\"color:blue\">- we don't want to type out a list of 1000s of index labels</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>aa</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>bb</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a  c\n",
+       "aa  5  2\n",
+       "bb  4  1"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows = [\"aa\", \"bb\"]\n",
+    "cols = [\"a\", \"c\"]\n",
+    "\n",
+    "df.loc[rows,cols]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### subset for columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = df[[\"a\", \"b\", \"c\", \"d\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>aa</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>bb</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cc</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a  b  c  d\n",
+       "aa  5  6  2  5\n",
+       "bb  4  6  1  3\n",
+       "cc  2  3  2  2"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### describe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>count</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>mean</td>\n",
+       "      <td>3.666667</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.666667</td>\n",
+       "      <td>3.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>std</td>\n",
+       "      <td>1.527525</td>\n",
+       "      <td>1.732051</td>\n",
+       "      <td>0.577350</td>\n",
+       "      <td>1.527525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>min</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25%</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.500000</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>50%</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>75%</td>\n",
+       "      <td>4.500000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>max</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              a         b         c         d\n",
+       "count  3.000000  3.000000  3.000000  3.000000\n",
+       "mean   3.666667  5.000000  1.666667  3.333333\n",
+       "std    1.527525  1.732051  0.577350  1.527525\n",
+       "min    2.000000  3.000000  1.000000  2.000000\n",
+       "25%    3.000000  4.500000  1.500000  2.500000\n",
+       "50%    4.000000  6.000000  2.000000  3.000000\n",
+       "75%    4.500000  6.000000  2.000000  4.000000\n",
+       "max    5.000000  6.000000  2.000000  5.000000"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### correlate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>a</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.944911</td>\n",
+       "      <td>-0.188982</td>\n",
+       "      <td>0.928571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>b</td>\n",
+       "      <td>0.944911</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>0.755929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>c</td>\n",
+       "      <td>-0.188982</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.188982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>d</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.755929</td>\n",
+       "      <td>0.188982</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          a         b         c         d\n",
+       "a  1.000000  0.944911 -0.188982  0.928571\n",
+       "b  0.944911  1.000000 -0.500000  0.755929\n",
+       "c -0.188982 -0.500000  1.000000  0.188982\n",
+       "d  0.928571  0.755929  0.188982  1.000000"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### summations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    11\n",
+       "b    15\n",
+       "c     5\n",
+       "d    10\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.sum(axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "aa    18\n",
+       "bb    14\n",
+       "cc     9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.sum(axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    4.0\n",
+       "b    6.0\n",
+       "c    2.0\n",
+       "d    3.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.median(axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### new column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "df1[\"class\"] = [\"AA\", \"AA\", \"BB\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>aa</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>AA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>bb</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>AA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>cc</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>BB</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a  b  c  d class\n",
+       "aa  5  6  2  5    AA\n",
+       "bb  4  6  1  3    AA\n",
+       "cc  2  3  2  2    BB"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>AA</td>\n",
+       "      <td>9</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>BB</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a   b  c  d\n",
+       "class             \n",
+       "AA     9  12  3  8\n",
+       "BB     2   3  2  2"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby(\"class\").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>AA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>BB</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a  b  c  d\n",
+       "class            \n",
+       "AA     2  2  2  2\n",
+       "BB     1  1  1  1"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby(\"class\").count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>AA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>BB</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a  b  c  d  class\n",
+       "class                   \n",
+       "AA     2  1  2  2      1\n",
+       "BB     1  1  1  1      1"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# count distinct\n",
+    "df1.groupby(\"class\").nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### reset index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['AA', 'BB'], dtype='object', name='class')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby(\"class\").count().index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>class</th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>AA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>BB</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  class  a  b  c  d\n",
+       "0    AA  2  2  2  2\n",
+       "1    BB  1  1  1  1"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby(\"class\").count().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=2, step=1)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby(\"class\").count().reset_index().index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### reading in csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"../data/iris.csv\"\n",
+    "df = pd.read_csv(path, sep = \",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "      <th>variety</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
+       "0           5.1          3.5           1.4          0.2  Setosa\n",
+       "1           4.9          3.0           1.4          0.2  Setosa\n",
+       "2           4.7          3.2           1.3          0.2  Setosa\n",
+       "3           4.6          3.1           1.5          0.2  Setosa\n",
+       "4           5.0          3.6           1.4          0.2  Setosa"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">read in the iris csv:</h3>\n",
+    "<p style=\"color:blue\">- find the descriptive statistics</p>\n",
+    "<p style=\"color:blue\">- create a correlation matrix </p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### subset on rules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(61, 5)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = df[df[\"sepal.length\"] > 6]\n",
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(23, 5)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = df[(df[\"sepal.length\"] > 6) & (df[\"sepal.width\"] > 3)]\n",
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 5)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = df[df[\"variety\"] == \"Setosa\"]\n",
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### isin()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Setosa', 'Versicolor', 'Virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"variety\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100, 5)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst = [\"Setosa\", \"Versicolor\"]\n",
+    "\n",
+    "df1 = df[df[\"variety\"].isin(lst)]\n",
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 5)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = df[df[\"variety\"].str.contains(\"Versi\")]\n",
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### apply"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x):\n",
+    "    return -x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "      <th>variety</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
+       "0           5.1          3.5           1.4          0.2  Setosa"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(my_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "      <th>variety</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>-5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>-4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>-4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>-4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
+       "0          -5.1          3.5           1.4          0.2  Setosa\n",
+       "1          -4.9          3.0           1.4          0.2  Setosa\n",
+       "2          -4.7          3.2           1.3          0.2  Setosa\n",
+       "3          -4.6          3.1           1.5          0.2  Setosa\n",
+       "4          -5.0          3.6           1.4          0.2  Setosa"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['sepal.length', 'sepal.width', 'petal.length', 'petal.width',\n",
+       "       'variety'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['sepal.length', 'sepal.width', 'petal.length', 'petal.width']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in cols:\n",
+    "    df[col] = df[col].apply(my_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "      <th>variety</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>-3.5</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>-3.2</td>\n",
+       "      <td>-1.3</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>-3.1</td>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>-3.6</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
+       "0           5.1         -3.5          -1.4         -0.2  Setosa\n",
+       "1           4.9         -3.0          -1.4         -0.2  Setosa\n",
+       "2           4.7         -3.2          -1.3         -0.2  Setosa\n",
+       "3           4.6         -3.1          -1.5         -0.2  Setosa\n",
+       "4           5.0         -3.6          -1.4         -0.2  Setosa"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(lambda x: -x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "      <th>variety</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>-5.1</td>\n",
+       "      <td>-3.5</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>-4.9</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>-4.7</td>\n",
+       "      <td>-3.2</td>\n",
+       "      <td>-1.3</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>-4.6</td>\n",
+       "      <td>-3.1</td>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>-3.6</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
+       "0          -5.1         -3.5          -1.4         -0.2  Setosa\n",
+       "1          -4.9         -3.0          -1.4         -0.2  Setosa\n",
+       "2          -4.7         -3.2          -1.3         -0.2  Setosa\n",
+       "3          -4.6         -3.1          -1.5         -0.2  Setosa\n",
+       "4          -5.0         -3.6          -1.4         -0.2  Setosa"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal.length    float64\n",
+       "sepal.width     float64\n",
+       "petal.length    float64\n",
+       "petal.width     float64\n",
+       "variety          object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal.length     object\n",
+       "sepal.width     float64\n",
+       "petal.length    float64\n",
+       "petal.width     float64\n",
+       "variety          object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### get dummies\n",
+    "* for string or object variables\n",
+    "* can specify specific columns\n",
+    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">what might we need dummies for?</h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.get_dummies(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "      <th>sepal.length_-4.3</th>\n",
+       "      <th>sepal.length_-4.4</th>\n",
+       "      <th>sepal.length_-4.5</th>\n",
+       "      <th>sepal.length_-4.6</th>\n",
+       "      <th>sepal.length_-4.7</th>\n",
+       "      <th>sepal.length_-4.8</th>\n",
+       "      <th>sepal.length_-4.9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sepal.length_-7.1</th>\n",
+       "      <th>sepal.length_-7.2</th>\n",
+       "      <th>sepal.length_-7.3</th>\n",
+       "      <th>sepal.length_-7.4</th>\n",
+       "      <th>sepal.length_-7.6</th>\n",
+       "      <th>sepal.length_-7.7</th>\n",
+       "      <th>sepal.length_-7.9</th>\n",
+       "      <th>variety_Setosa</th>\n",
+       "      <th>variety_Versicolor</th>\n",
+       "      <th>variety_Virginica</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>-3.5</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>-3.2</td>\n",
+       "      <td>-1.3</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>-3.1</td>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>-3.6</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal.width  petal.length  petal.width  sepal.length_-4.3  \\\n",
+       "0         -3.5          -1.4         -0.2                  0   \n",
+       "1         -3.0          -1.4         -0.2                  0   \n",
+       "2         -3.2          -1.3         -0.2                  0   \n",
+       "3         -3.1          -1.5         -0.2                  0   \n",
+       "4         -3.6          -1.4         -0.2                  0   \n",
+       "\n",
+       "   sepal.length_-4.4  sepal.length_-4.5  sepal.length_-4.6  sepal.length_-4.7  \\\n",
+       "0                  0                  0                  0                  0   \n",
+       "1                  0                  0                  0                  0   \n",
+       "2                  0                  0                  0                  1   \n",
+       "3                  0                  0                  1                  0   \n",
+       "4                  0                  0                  0                  0   \n",
+       "\n",
+       "   sepal.length_-4.8  sepal.length_-4.9  ...  sepal.length_-7.1  \\\n",
+       "0                  0                  0  ...                  0   \n",
+       "1                  0                  1  ...                  0   \n",
+       "2                  0                  0  ...                  0   \n",
+       "3                  0                  0  ...                  0   \n",
+       "4                  0                  0  ...                  0   \n",
+       "\n",
+       "   sepal.length_-7.2  sepal.length_-7.3  sepal.length_-7.4  sepal.length_-7.6  \\\n",
+       "0                  0                  0                  0                  0   \n",
+       "1                  0                  0                  0                  0   \n",
+       "2                  0                  0                  0                  0   \n",
+       "3                  0                  0                  0                  0   \n",
+       "4                  0                  0                  0                  0   \n",
+       "\n",
+       "   sepal.length_-7.7  sepal.length_-7.9  variety_Setosa  variety_Versicolor  \\\n",
+       "0                  0                  0               1                   0   \n",
+       "1                  0                  0               1                   0   \n",
+       "2                  0                  0               1                   0   \n",
+       "3                  0                  0               1                   0   \n",
+       "4                  0                  0               1                   0   \n",
+       "\n",
+       "   variety_Virginica  \n",
+       "0                  0  \n",
+       "1                  0  \n",
+       "2                  0  \n",
+       "3                  0  \n",
+       "4                  0  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>AA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>BB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>CC</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col_1 col_2\n",
+       "0     A    AA\n",
+       "1     B    BB\n",
+       "2     C    CC"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col_1 = np.array([\"A\", \"B\", \"C\"])\n",
+    "col_2 = np.array([\"AA\", \"BB\", \"CC\"])\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    \"col_1\":col_1,\n",
+    "    \"col_2\":col_2\n",
+    "})\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">perform get dummies on col_1 only?</h3>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### iterate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 col_1     A\n",
+      "col_2    AA\n",
+      "Name: 0, dtype: object\n",
+      "1 col_1     B\n",
+      "col_2    BB\n",
+      "Name: 1, dtype: object\n",
+      "2 col_1     C\n",
+      "col_2    CC\n",
+      "Name: 2, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "for idx, r in df.head(5).iterrows():\n",
+    "    print(idx, r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'petal.length'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4735\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4736\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4737\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.get_value_at\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.validate_indexer\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-79-99dacd2ca267>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"petal.length\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1066\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1067\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1068\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1069\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1070\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4742\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4743\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4744\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4745\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4746\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4728\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4729\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4730\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4731\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4732\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'petal.length'"
+     ]
+    }
+   ],
+   "source": [
+    "for idx, r in df.head(5).iterrows():\n",
+    "    print(r[\"petal.length\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'petal.width'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'petal.width'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-80-4e0306d0c85d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"petal.width\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'petal.width'"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"petal.width\"].tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">Using iris, group by variety and find the count of the other columns?</h3>\n",
+    "<h3 style=\"color:blue\">Using iris, group by variety and find the sum of the other columns?</h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_1_ind</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>D</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col_1  col_1_ind\n",
+       "0     A          1\n",
+       "1     B          1\n",
+       "2     C          1\n",
+       "3     D          1\n",
+       "4     E          1"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col_1 = np.array([\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
+    "col_2 = np.array([\"A\", \"B\", \"C\"])\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    \"col_1\":col_1,\n",
+    "    \"col_1_ind\":1\n",
+    "})\n",
+    "\n",
+    "df1 = pd.DataFrame({\n",
+    "    \"col_1\":col_2,\n",
+    "    \"col_2_ind\":1\n",
+    "})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_2_ind</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col_1  col_2_ind\n",
+       "0     A          1\n",
+       "1     B          1\n",
+       "2     C          1"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### concat\n",
+    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_1_ind</th>\n",
+       "      <th>col_2_ind</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>D</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col_1  col_1_ind  col_2_ind\n",
+       "0     A        1.0        NaN\n",
+       "1     B        1.0        NaN\n",
+       "2     C        1.0        NaN\n",
+       "3     D        1.0        NaN\n",
+       "4     E        1.0        NaN\n",
+       "0     A        NaN        1.0\n",
+       "1     B        NaN        1.0\n",
+       "2     C        NaN        1.0"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df, df1], axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### merge\n",
+    "* https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.merge.html\n",
+    "* assumes inner join"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "select *\n",
+    "from df inner join df2\n",
+    "    on df.col_1 = df2.col_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_1_ind</th>\n",
+       "      <th>col_2_ind</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col_1  col_1_ind  col_2_ind\n",
+       "0     A          1          1\n",
+       "1     B          1          1\n",
+       "2     C          1          1"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.merge(df1, how = \"inner\", left_on = \"col_1\", right_on = \"col_1\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "select *\n",
+    "from df left join df2\n",
+    "    on df.col_1 = df2.col_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_1</th>\n",
+       "      <th>col_1_ind</th>\n",
+       "      <th>col_2_ind</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>D</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col_1  col_1_ind  col_2_ind\n",
+       "0     A          1        1.0\n",
+       "1     B          1        1.0\n",
+       "2     C          1        1.0\n",
+       "3     D          1        NaN\n",
+       "4     E          1        NaN"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.merge(df1, how = \"left\", left_on = \"col_1\", right_on = \"col_1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Imputation\n",
+    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html\n",
+    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.interpolate.html#pandas.DataFrame.interpolate\n",
+    "* missing values\n",
+    "* When summing data, NA (missing) values will be treated as zero.\n",
+    "* If the data are all NA, the result will be 0.\n",
+    "* Cumulative methods like cumsum() and cumprod() ignore NA values by default, but preserve them in the resulting arrays. To override this behaviour and include NA values, use skipna=False.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "data = [1,2,3,4,None,5,2,1]\n",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0\n",
+       "0  1.0\n",
+       "1  2.0\n",
+       "2  3.0\n",
+       "3  4.0\n",
+       "4  NaN\n",
+       "5  5.0\n",
+       "6  2.0\n",
+       "7  1.0"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0\n",
+       "0  False\n",
+       "1  False\n",
+       "2  False\n",
+       "3  False\n",
+       "4   True\n",
+       "5  False\n",
+       "6  False\n",
+       "7  False"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "      <td>2</td>\n",
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+       "      <td>True</td>\n",
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+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
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+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0\n",
+       "0   True\n",
+       "1   True\n",
+       "2   True\n",
+       "3   True\n",
+       "4  False\n",
+       "5   True\n",
+       "6   True\n",
+       "7   True"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.notna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0\n",
+       "0  1.0\n",
+       "1  2.0\n",
+       "2  3.0\n",
+       "3  4.0\n",
+       "4  0.0\n",
+       "5  5.0\n",
+       "6  2.0\n",
+       "7  1.0"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
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+       "      <th></th>\n",
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+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
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+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
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+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
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+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
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+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0\n",
+       "0    1.0\n",
+       "1    2.0\n",
+       "2    3.0\n",
+       "3    4.0\n",
+       "4  100.0\n",
+       "5    5.0\n",
+       "6    2.0\n",
+       "7    1.0"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
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+       "      <td>3.0</td>\n",
+       "    </tr>\n",
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+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0\n",
+       "0  1.0\n",
+       "1  2.0\n",
+       "2  3.0\n",
+       "3  4.0\n",
+       "5  5.0\n",
+       "6  2.0\n",
+       "7  1.0"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "      <th></th>\n",
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+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [0, 1, 2, 3, 4, 5, 6, 7]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
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+       "      <td>3.000000</td>\n",
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+       "      <td>3</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0\n",
+       "0  1.000000\n",
+       "1  2.000000\n",
+       "2  3.000000\n",
+       "3  4.000000\n",
+       "4  2.571429\n",
+       "5  5.000000\n",
+       "6  2.000000\n",
+       "7  1.000000"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(df.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0\n",
+       "0  1.0\n",
+       "1  2.0\n",
+       "2  3.0\n",
+       "3  4.0\n",
+       "4  2.0\n",
+       "5  5.0\n",
+       "6  2.0\n",
+       "7  1.0"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(df.median())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index         0\n",
+       "0      0  1.000000\n",
+       "1      1  2.000000\n",
+       "2      2  3.000000\n",
+       "3      3  4.000000\n",
+       "4      4  2.571429\n",
+       "5      5  5.000000\n",
+       "6      6  2.000000\n",
+       "7      7  1.000000"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[0].fillna(df[0].mean()).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Json data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"../data/ca.json\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "with open(path, \"r\") as file:\n",
+    "    line = file.readlines()\n",
+    "    for l in line[0:50]:\n",
+    "        print(l)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "{\n",
+    "    \"brian\" {\n",
+    "        \"age\":31,\n",
+    "        \"job\":data scientist,\n",
+    "        \"city\"chicago\n",
+    "        },\n",
+    "        \n",
+    "   \"james\":{\n",
+    "        \"age\":28,\n",
+    "        \"job\":data scientist,\n",
+    "        \"city\"new york\n",
+    "        }\n",
+    "        \n",
+    "     \"james\":{\n",
+    "        \"age\":28,\n",
+    "        }\n",
+    "    \n",
+    "    \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "columns = [\"age\", \"job\", \"city\"]\n",
+    "data = [\n",
+    "    [31, \"data scientist\", \"chicago\"],\n",
+    "    [28, \"data scientist\", \"new york\"],\n",
+    "    [28,None,None]\n",
+    "]\n",
+    "\n",
+    "df = pd.DataFrame(data, columns = columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Expected object or value",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-102-1ce8f6ab196d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    593\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mshould_close\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    715\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 717\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    718\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[1;34m(self, json)\u001b[0m\n\u001b[0;32m    737\u001b[0m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"frame\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 739\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    741\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"series\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    847\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    848\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 849\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_no_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    850\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    851\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1091\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m             self.obj = DataFrame(\n\u001b[1;32m-> 1093\u001b[1;33m                 \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprecise_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprecise_float\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1094\u001b[0m             )\n\u001b[0;32m   1095\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"split\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Expected object or value"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_json(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html\n",
+    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.json.json_normalize.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Nested JSON files can be time consuming and difficult process to flatten and load into Pandas.\n",
+    "* Let’s unpack the works column into a standalone dataframe. We’ll also grab the flat columns.\n",
+    "* nycphil = json_normalize(d['programs'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "        {'id': 1, 'name': \"Cole Volk\",'fitness': \n",
+    "             {'height': 130, 'weight': 60}},\n",
+    "    \n",
+    "        {'name': \"Mose Reg\",'fitness': \n",
+    "             {'height': 130, 'weight': 60}},\n",
+    "    \n",
+    "        {'id': 2, 'name': 'Faye Raker','fitness': \n",
+    "             {'height': 130, 'weight': 60}}\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.io.json import json_normalize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>fitness.height</th>\n",
+       "      <th>fitness.weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Cole Volk</td>\n",
+       "      <td>130</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mose Reg</td>\n",
+       "      <td>130</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Faye Raker</td>\n",
+       "      <td>130</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    id        name  fitness.height  fitness.weight\n",
+       "0  1.0   Cole Volk             130              60\n",
+       "1  NaN    Mose Reg             130              60\n",
+       "2  2.0  Faye Raker             130              60"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_normalize(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>fitness</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Cole Volk</td>\n",
+       "      <td>{'height': 130, 'weight': 60}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mose Reg</td>\n",
+       "      <td>{'height': 130, 'weight': 60}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Faye Raker</td>\n",
+       "      <td>{'height': 130, 'weight': 60}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    id        name                        fitness\n",
+       "0  1.0   Cole Volk  {'height': 130, 'weight': 60}\n",
+       "1  NaN    Mose Reg  {'height': 130, 'weight': 60}\n",
+       "2  2.0  Faye Raker  {'height': 130, 'weight': 60}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_normalize(data, max_level = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "        {'state': 'Florida','shortname': 'FL',\n",
+    "            'info': {'governor': 'Rick Scott', \"gender\":\"m\"},\n",
+    "            'counties': [{'name': 'Dade', 'population': 12345},\n",
+    "                         {'name': 'Broward', 'population': 40000},\n",
+    "                         {'name': 'Palm Beach', 'population': 60000}]},\n",
+    "    \n",
+    "         {'state': 'Ohio', 'shortname': 'OH',\n",
+    "          'info': {'governor': 'John Kasich', \"gender\":\"m\"},\n",
+    "          'counties': [{'name': 'Summit', 'population': 1234},\n",
+    "                       {'name': 'Cuyahoga', 'population': 1337}]}\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>shortname</th>\n",
+       "      <th>info</th>\n",
+       "      <th>counties</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Florida</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>{'governor': 'Rick Scott', 'gender': 'm'}</td>\n",
+       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>OH</td>\n",
+       "      <td>{'governor': 'John Kasich', 'gender': 'm'}</td>\n",
+       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state shortname                                        info  \\\n",
+       "0  Florida        FL   {'governor': 'Rick Scott', 'gender': 'm'}   \n",
+       "1     Ohio        OH  {'governor': 'John Kasich', 'gender': 'm'}   \n",
+       "\n",
+       "                                            counties  \n",
+       "0  [{'name': 'Dade', 'population': 12345}, {'name...  \n",
+       "1  [{'name': 'Summit', 'population': 1234}, {'nam...  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_normalize(data, max_level = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>shortname</th>\n",
+       "      <th>counties</th>\n",
+       "      <th>info.governor</th>\n",
+       "      <th>info.gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Florida</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
+       "      <td>Rick Scott</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>OH</td>\n",
+       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
+       "      <td>John Kasich</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state shortname                                           counties  \\\n",
+       "0  Florida        FL  [{'name': 'Dade', 'population': 12345}, {'name...   \n",
+       "1     Ohio        OH  [{'name': 'Summit', 'population': 1234}, {'nam...   \n",
+       "\n",
+       "  info.governor info.gender  \n",
+       "0    Rick Scott           m  \n",
+       "1   John Kasich           m  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_normalize(data, max_level = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>shortname</th>\n",
+       "      <th>counties</th>\n",
+       "      <th>info.governor</th>\n",
+       "      <th>info.gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Florida</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
+       "      <td>Rick Scott</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>OH</td>\n",
+       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
+       "      <td>John Kasich</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state shortname                                           counties  \\\n",
+       "0  Florida        FL  [{'name': 'Dade', 'population': 12345}, {'name...   \n",
+       "1     Ohio        OH  [{'name': 'Summit', 'population': 1234}, {'nam...   \n",
+       "\n",
+       "  info.governor info.gender  \n",
+       "0    Rick Scott           m  \n",
+       "1   John Kasich           m  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_normalize(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>population</th>\n",
+       "      <th>info.governor</th>\n",
+       "      <th>info.gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Dade</td>\n",
+       "      <td>12345</td>\n",
+       "      <td>Rick Scott</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Broward</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Rick Scott</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>Palm Beach</td>\n",
+       "      <td>60000</td>\n",
+       "      <td>Rick Scott</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>Summit</td>\n",
+       "      <td>1234</td>\n",
+       "      <td>John Kasich</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>Cuyahoga</td>\n",
+       "      <td>1337</td>\n",
+       "      <td>John Kasich</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         name  population info.governor info.gender\n",
+       "0        Dade       12345    Rick Scott           m\n",
+       "1     Broward       40000    Rick Scott           m\n",
+       "2  Palm Beach       60000    Rick Scott           m\n",
+       "3      Summit        1234   John Kasich           m\n",
+       "4    Cuyahoga        1337   John Kasich           m"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make each row a county, then start parsing data as such\n",
+    "json_normalize(data, \"counties\", [[\"info\",\"governor\"], [\"info\", \"gender\"]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chunks\n",
+    "* Can use chunks to process pieces of a dataframe at a time if it won't fit into memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chunk_size = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"../data/iris.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 5)\n",
+      "(50, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in pd.read_csv(path, chunksize = chunk_size):\n",
+    "    print(c.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 4)\n",
+      "(1, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in pd.read_csv(path, chunksize = chunk_size):\n",
+    "    df = c.groupby(\"variety\").sum()\n",
+    "    print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lectures/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling.ipynb b/lectures/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling.ipynb
index f7e15dc..1f49e8c 100644
--- a/lectures/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling.ipynb	
+++ b/lectures/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling.ipynb	
@@ -26,7 +26,7 @@
    "metadata": {},
    "source": [
     "def my_function(param1, param2):\n",
-    "    do some stuff"
+    "   do some stuff"
    ]
   },
   {
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +50,7 @@
        "function"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -61,19 +61,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5\n"
+      "<built-in function print>\n"
      ]
     }
    ],
    "source": [
-    "my_func(5)"
+    "my_func(print)"
    ]
   },
   {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,18 +112,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "4\n",
       "4\n"
      ]
     }
    ],
    "source": [
+    "print(my_func(2))\n",
+    "\n",
     "y = my_func(2)\n",
     "print(y)"
    ]
@@ -172,26 +175,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "def my_func(x,y):\n",
-    "    \n",
-    "    output = None\n",
-    "    z = x+y\n",
-    "    \n",
-    "    if z < 5:\n",
-    "        output = True\n",
-    "    else:\n",
-    "        output = False\n",
-    "        \n",
-    "    return output"
+    "\n",
+    "  output = None \n",
+    "  z = x+y\n",
+    "\n",
+    "  if z < 5:\n",
+    "    output = True\n",
+    "  else:\n",
+    "    output = False\n",
+    "\n",
+    "  return output"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -200,7 +203,7 @@
        "True"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -212,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -221,7 +224,7 @@
        "False"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -253,7 +256,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "[0, 1, 3, 5, 7, 9, 11, 13, 15]\n",
+      "0\n",
+      "CoolBeans!\n",
+      "1\n",
+      "CoolBeans!\n",
+      "3\n",
+      "CoolBeans!\n",
+      "5\n",
+      "CoolBeans!\n",
+      "7\n",
+      "CoolBeans!\n",
+      "9\n",
+      "CoolBeans!\n",
+      "11\n",
+      "NotMySauce!\n",
+      "13\n",
+      "NotMySauce!\n",
+      "15\n",
+      "NotMySauce!\n",
+      "The sum of test is 25\n",
+      "raf_list is still 35\n"
+     ]
+    }
+   ],
+   "source": [
+    "raf_list = 35\n",
+    "\n",
+    "def raf_func_x(raf_list):\n",
+    "    \n",
+    "    #Lets test the types \n",
+    "    print(type(raf_list))\n",
+    "    print(raf_list)\n",
+    "\n",
+    "    #define the inner variables\n",
+    "    full_sum = 0\n",
+    "    \n",
+    "    #test for the ones bellow value\n",
+    "    #k is a value on raf_list\n",
+    "    for k in raf_list:\n",
+    "        print(k)\n",
+    "        if k < 10:\n",
+    "            print('CoolBeans!')\n",
+    "            full_sum= full_sum + k #add to the return variable\n",
+    "        else:\n",
+    "            print(\"NotMySauce!\")\n",
+    "    \n",
+    "    #finito!\n",
+    "    return full_sum \n",
+    "\n",
+    "test = [0,1,3,5,7,9,11,13,15]\n",
+    "\n",
+    "test_result = raf_func_x(test)\n",
+    "\n",
+    "print('The sum of test is ' + str(test_result))\n",
+    "print('raf_list is still ' + str(raf_list))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,24 +336,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'int' object has no attribute 'lower'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-24-d492ee7d6bfe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"str\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-23-20206ed1ff95>\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'lower'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "potatto\n",
+      "4\n"
      ]
     }
    ],
    "source": [
-    "#my_func(5,\"str\") # won't work"
+    "my_func(\"PoTatTO\",2) # won't work"
    ]
   },
   {
@@ -313,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -338,15 +404,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-3-f73cfd3c8222>, line 1)",
+     "evalue": "positional argument follows keyword argument (<ipython-input-45-f73cfd3c8222>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-f73cfd3c8222>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    my_func(y = 5, \"str\") # won't work\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-45-f73cfd3c8222>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    my_func(y = 5, \"str\") # won't work\u001b[0m\n\u001b[1;37m                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n"
      ]
     }
    ],
@@ -354,6 +420,29 @@
     "my_func(y = 5, \"str\") # won't work"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "coolmethod\n"
+     ]
+    }
+   ],
+   "source": [
+    "def test(x,y,z, method = \"c\", execute=False):\n",
+    "    print(x+y+z)\n",
+    "    if execute:\n",
+    "        print(method) \n",
+    "\n",
+    "test(0,1,2, execute=True,method='coolmethod')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -363,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -392,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -406,12 +495,13 @@
       "2\n",
       "3\n",
       "4\n",
-      "5\n"
+      "5\n",
+      "str\n"
      ]
     }
    ],
    "source": [
-    "my_func(1,2,5,6,2,3,4,5)"
+    "my_func(1,2,5,6,2,3,4,5,'str')"
    ]
   },
   {
@@ -424,6 +514,49 @@
     "<p style=\"color:blue\">- returns the list</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'function'>\n",
+      "<class 'list'>\n",
+      "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def uncle_sam(*enlisted):\n",
+    "    army = []\n",
+    "    for soldier in enlisted:\n",
+    "        army.append(soldier)\n",
+    "    \n",
+    "    return army\n",
+    "\n",
+    "micro_army = ('raf',0,\"mR Pots\",3, 17, 'hitman')\n",
+    "micro_army2 = list(range(0,1000,1))\n",
+    "\n",
+    "#micro_army3 = micro_army + micro_army2\n",
+    "usaf = uncle_sam(micro_army2)\n",
+    "print(type(uncle_sam))\n",
+    "print(type(usaf))\n",
+    "print(usaf)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys, shutil"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -434,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -444,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -471,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -488,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -520,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -528,10 +661,10 @@
      "evalue": "my_func() takes 2 positional arguments but 3 were given",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-8751ad374ad8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: my_func() takes 2 positional arguments but 3 were given"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-73-8751ad374ad8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: my_func() takes 2 positional arguments but 3 were given"
      ]
     }
    ],
@@ -541,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -566,6 +699,33 @@
     "<p style=\"color:blue\">- if it exists add that to the division of the first two params</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5\n",
+      "None\n",
+      "3.5\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "def copied_from_2_cells_above(x,y, **kwargs):\n",
+    "    val = x / y\n",
+    "    if \"third\" in kwargs:\n",
+    "        val+=kwargs[\"third\"]\n",
+    "    print(val)\n",
+    "    \n",
+    "print(copied_from_2_cells_above(1,2))\n",
+    "print(copied_from_2_cells_above(1,2,third=3))\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -721,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -730,7 +890,7 @@
        "[3, 4, 5]"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -772,7 +932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -781,7 +941,7 @@
        "[4, 5]"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -803,6 +963,27 @@
     "<p style=\"color:blue\">- .replace(item_replace, replace_with)</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['AEA', 'EEE']"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = ['aea', 'oeo']\n",
+    "list(map(lambda x: x.replace('o','e').upper(),test))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -816,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -825,7 +1006,7 @@
        "[1, 2, 3, 4, 5]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -840,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -849,7 +1030,7 @@
        "[1, 2, 3, 4, 5]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -861,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -870,7 +1051,7 @@
        "('a', 'b', 'c')"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -883,16 +1064,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{(1, 'a'), (2, 'b'), (3, 'c')}"
+       "'a'"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -901,22 +1082,22 @@
     "a = [1,2,3]\n",
     "b = [\"a\",\"b\",\"c\"]\n",
     "\n",
-    "my_dict = {(x[0],x[1]) for x in zip(a,b)}\n",
-    "my_dict"
+    "my_dict = dict((x[0],x[1]) for x in zip(a,b)}\n",
+    "print()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[4, 5]"
+       "[]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -928,16 +1109,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[-1, -2, -3, -4, -5]"
+       "[-1, -2, -3]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1003,22 +1184,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1"
+       "[1, 0, 1, 0, 1, 0]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "51%2"
+    "test = [1,2,3,4,5,6]\n",
+    "list(map(lambda x:x%2,test))"
    ]
   },
   {
@@ -1054,7 +1236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1064,7 +1246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
@@ -1073,7 +1255,7 @@
        "[0, 1, 2, 3, 4]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1084,16 +1266,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7fcd988c25d0>"
+       "<generator object <genexpr> at 0x000001A597BF4848>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1104,7 +1286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -1210,7 +1392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1221,7 +1403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1230,16 +1412,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<generator object infinite_sequence at 0x7fcd988c2350>"
+       "<generator object infinite_sequence at 0x000001A597BF46C8>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1250,7 +1432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -1300,7 +1482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1309,16 +1491,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "81528064"
+       "81528056"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1330,16 +1512,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "128"
+       "120"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1359,7 +1541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1395,16 +1577,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7fcd68774450>"
+       "<generator object <genexpr> at 0x000001A597C350C8>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1416,16 +1598,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7fcd687745d0>"
+       "<generator object <genexpr> at 0x000001A597C35148>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,7 +1619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 135,
    "metadata": {
     "scrolled": true
    },
@@ -1457,7 +1639,7 @@
        " 'round']"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1468,7 +1650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -1486,7 +1668,7 @@
        " 'b']"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1513,15 +1695,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-76-35c08edd0df2>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-137-35c08edd0df2>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-76-35c08edd0df2>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print((2)))\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-137-35c08edd0df2>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    print((2)))\u001b[0m\n\u001b[1;37m              ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1614,7 +1796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -1622,10 +1804,10 @@
      "evalue": "x is empty",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-37-9bd7d8a13151>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"x is empty\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m: x is empty"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-138-9bd7d8a13151>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"x is empty\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m: x is empty"
      ]
     }
    ],
@@ -1676,7 +1858,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,\"String\"]\n",
+    "\n",
+    "for i in a:\n",
+    "    \n",
+    "    try:\n",
+    "        i = i+2\n",
+    "    except:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -1688,13 +1885,6 @@
     }
    ],
    "source": [
-    "a = [1,2,3,4,\"String\"]\n",
-    "\n",
-    "for i in a:\n",
-    "    \n",
-    "    try:\n",
-    "        i = i+2\n",
-    "    except:\n",
     "        print(\"Found an error.  Item {} isn't a number or float\".format(i))"
    ]
   },
diff --git a/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb b/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb
index 770940f..6f1b674 100644
--- a/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb	
+++ b/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb	
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,43 +41,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'blue'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog.color"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'sheperd'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog.breed"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'breed',\n",
+       " 'color',\n",
+       " 'name']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "dir(my_dog)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.dog"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "type(my_dog)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,15 +162,16 @@
     "        self.name = name\n",
     "        \n",
     "    def speak(self):\n",
-    "        print(\"this is speaking\")\n",
+    "        xxx = self.color\n",
+    "        print(\"My color is \" + xxx)\n",
     "        \n",
-    "    def change_color(self, color):\n",
-    "        self.color = color"
+    "    def change_color(self, carrot):\n",
+    "        self.color = carrot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,36 +180,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method dog.speak of <__main__.dog object at 0x000001F1CE85A9C8>>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog.speak"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My color is blue\n"
+     ]
+    }
+   ],
    "source": [
     "my_dog.speak()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'blue'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog.color"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'red'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog.change_color(\"red\")\n",
     "my_dog.color"
@@ -144,14 +258,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
     "# simple framework for a dog\n",
     "class dog:\n",
     "    \n",
-    "    def __init__(self, color, breed, name):\n",
+    "    def __init__(self, color, breed, name, distance=0):\n",
     "        self.color = color\n",
     "        self.breed = breed\n",
     "        self.name = name\n",
@@ -163,9 +277,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")\n",
     "my_dog.distance"
@@ -173,9 +298,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog.walk(5)\n",
     "my_dog.distance"
@@ -183,9 +319,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog.walk(12)\n",
     "my_dog.distance"
@@ -193,9 +340,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "my_dog.walk(18)\n",
     "my_dog.distance"
@@ -211,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,16 +390,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
-    "r =  retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
+    "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -250,7 +408,7 @@
        "'blue'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -261,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 51,
    "metadata": {
     "scrolled": true
    },
@@ -302,7 +460,7 @@
        " 'walk']"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -313,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,32 +487,39 @@
     "        self.distance += distance\n",
     "        \n",
     "class Retreiver(Dog):\n",
-    "    \n",
+    "\n",
     "    def speak(self):\n",
-    "        print(\"My name is {}\".format(self.name))"
+    "        print(\"My name is {}\".format(self.name))\n",
+    "        \n",
+    "    def blink182(self):\n",
+    "        print(\"My age is {}\".format(self.age))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "My name is Rex\n"
+     "ename": "TypeError",
+     "evalue": "__init__() got an unexpected keyword argument 'color'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-62-6f94a433d382>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mRetreiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"blue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"retreiver\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Rex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblink182\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'color'"
      ]
     }
    ],
    "source": [
     "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
-    "r.speak()"
+    "r.blink182()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -362,10 +527,10 @@
      "evalue": "'Dog' object has no attribute 'speak'",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-800ba7303882>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mDog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"blue\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"retreiver\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Rex\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspeak\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'Dog' object has no attribute 'speak'"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-56-800ba7303882>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mDog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"blue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"retreiver\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Rex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspeak\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Dog' object has no attribute 'speak'"
      ]
     }
    ],
@@ -386,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,16 +587,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
-    "r =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
+    "rex =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
+    "thor =  Dog(color = \"blue\", breed = \"retreiver\", name = \"thor\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -443,30 +609,32 @@
     }
    ],
    "source": [
-    "r.printer()"
+    "rex.printer()\n",
+    "thor.class_add()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20\n"
+      "30\n"
      ]
     }
    ],
    "source": [
-    "r.class_add()\n",
-    "r.printer()"
+    "rex.class_add()\n",
+    "rex.printer()\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -478,12 +646,12 @@
     }
    ],
    "source": [
-    "r.speak(\"say something\")"
+    "rex.speak(\"say something\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -495,21 +663,21 @@
     }
    ],
    "source": [
-    "Dog.speak(\"say something\")"
+    "thor.speak(\"say something\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "20"
+       "30"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -573,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -590,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -631,7 +799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -648,14 +816,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019-11-24\n"
+      "2020-01-21\n"
      ]
     }
    ],
@@ -666,36 +834,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "24"
+       "['__add__',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__radd__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rsub__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " 'ctime',\n",
+       " 'day',\n",
+       " 'fromisoformat',\n",
+       " 'fromordinal',\n",
+       " 'fromtimestamp',\n",
+       " 'isocalendar',\n",
+       " 'isoformat',\n",
+       " 'isoweekday',\n",
+       " 'max',\n",
+       " 'min',\n",
+       " 'month',\n",
+       " 'replace',\n",
+       " 'resolution',\n",
+       " 'strftime',\n",
+       " 'timetuple',\n",
+       " 'today',\n",
+       " 'toordinal',\n",
+       " 'weekday',\n",
+       " 'year']"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "today.day"
+    "dir(today)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "11"
+       "1"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -706,16 +919,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2019"
+       "2020"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,16 +953,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "6"
+       "1"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -760,7 +973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 101,
    "metadata": {
     "scrolled": true
    },
@@ -816,7 +1029,7 @@
        " 'year']"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -834,14 +1047,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019-11-24 12:06:33.330468\n"
+      "2020-01-21 19:32:08.672203\n"
      ]
     }
    ],
@@ -852,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 107,
    "metadata": {
     "scrolled": true
    },
@@ -928,7 +1141,7 @@
        " 'year']"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -939,16 +1152,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "33"
+       "8"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -959,16 +1172,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "6"
+       "32"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -979,16 +1192,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "12"
+       "19"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -999,16 +1212,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "24"
+       "21"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,16 +1232,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "11"
+       "1"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1039,16 +1252,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2019"
+       "2020"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1059,16 +1272,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "6"
+       "1"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1089,25 +1302,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Today is a Friday\n"
+      "Hoje é Terça\n",
+      "Today is Tuesday\n",
+      "What?\n"
      ]
     }
    ],
    "source": [
-    "print(\"Today is a Friday\")"
+    "\n",
+    "def day_printer(language='EN'):\n",
+    "    if language=='EN':\n",
+    "        weekdays = {0:\"Monday\",\n",
+    "            1:\"Tuesday\",\n",
+    "            2:\"Wednesday\",\n",
+    "            3:\"Thursday\"}\n",
+    "        pretext=\"Today is \"\n",
+    "    \n",
+    "    elif language=='PT':\n",
+    "        weekdays= {0:\"Segunda\",\n",
+    "            1:\"Terça\",\n",
+    "            2:\"Quarta\",\n",
+    "            3:\"Quinta\"}\n",
+    "        pretext=\"Hoje é \"\n",
+    "    else:\n",
+    "        pretext=\"What?\"\n",
+    "        weekdays=[0,\"\",1,\"\",2,\"\",3,\"\"]\n",
+    "\n",
+    "        \n",
+    "    print(pretext+ weekdays[today.weekday()] )\n",
+    "    \n",
+    "    \n",
+    "day_printer('PT')\n",
+    "day_printer('EN')\n",
+    "day_printer('CH')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "\n",
     "#### Format Datetime output using strftime\n",
     "* (%y/%Y – Year)\n",
     "* (%a/%A- weekday)\n",
@@ -1120,7 +1361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1129,15 +1370,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019\n",
-      "19\n"
+      "2020\n",
+      "20\n"
      ]
     }
    ],
@@ -1148,15 +1389,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sunday\n",
-      "Sun\n"
+      "Tuesday\n",
+      "Tue\n"
      ]
     }
    ],
@@ -1167,15 +1408,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "November\n",
-      "Nov\n"
+      "January\n",
+      "Jan\n"
      ]
     }
    ],
@@ -1186,15 +1427,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11/24/19\n",
-      "24\n"
+      "01/21/20\n",
+      "21\n"
      ]
     }
    ],
@@ -1205,14 +1446,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sun Nov 24 12:10:24 2019\n"
+      "Tue Jan 21 19:57:30 2020\n"
      ]
     }
    ],
@@ -1229,14 +1470,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019-November-24\n"
+      "2020-January-21\n"
      ]
     }
    ],
@@ -1246,14 +1487,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019,November,24\n"
+      "2020,January,21\n"
      ]
     }
    ],
@@ -1263,14 +1504,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "19-Nov-24\n"
+      "20-Jan-21\n"
      ]
     }
    ],
@@ -1280,33 +1521,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12:10:24\n"
+      "0757\n"
      ]
     }
    ],
    "source": [
     "# I is the hour, # M is minutes, #S is seconds\n",
     "# army time\n",
-    "print(now.strftime(\"%I:%M:%S\"))"
+    "print(now.strftime(\"%I%M\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12:10\n"
+      "19:57\n"
      ]
     }
    ],
@@ -1325,7 +1566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1334,15 +1575,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019-11-24 12:19:01.737490\n",
-      "2019-12-04 17:34:01.737781\n"
+      "2020-01-21 20:00:44.727654\n",
+      "2020-02-01 01:15:44.728642\n"
      ]
     }
    ],
@@ -1353,15 +1594,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019-11-24 12:19:09.017928\n",
-      "2019-11-14 07:04:09.018214\n"
+      "2020-01-21 20:00:46.658815\n",
+      "2020-01-11 14:45:46.658815\n"
      ]
     }
    ],
@@ -1372,15 +1613,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019-11-24 12:19:44.676823\n",
-      "2019-12-08 12:19:44.677028\n"
+      "2020-01-21 20:01:03.819276\n",
+      "2020-02-04 20:01:03.819276\n"
      ]
     }
    ],
@@ -1410,7 +1651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
@@ -1419,7 +1660,7 @@
        "datetime.datetime"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1432,16 +1673,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2019"
+       "2020"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1452,16 +1693,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "11"
+       "1"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1472,16 +1713,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "10"
+       "7"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1492,16 +1733,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "6"
+       "1"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1534,7 +1775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1543,7 +1784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1552,7 +1793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1563,9 +1804,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 161,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'test' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-161-a07cb10eeb0f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# delete from memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'test' is not defined"
+     ]
+    }
+   ],
    "source": [
     "# delete from memory\n",
     "del test"
@@ -1573,7 +1826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1584,7 +1837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [
     {
@@ -1593,7 +1846,7 @@
        "['a', 'b']"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1611,7 +1864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1620,7 +1873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1630,17 +1883,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#del item\n",
+    "del item\n",
     "#item"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1650,7 +1903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [
     {
@@ -1659,7 +1912,7 @@
        "'some string I made'"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1678,6 +1931,47 @@
     "<p style=\"color:blue\">- reload it</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "import os\n",
+    "class cocumber:\n",
+    "    def __init__(self,test):\n",
+    "        with open('potato.pkl','wb') as f :\n",
+    "            pickle.dump(test, f)\n",
+    "        del test\n",
+    "        os.rename('potato.pkl', 'mashme3.txt')\n",
+    "        with open('meshme3.txt','rb') as f :    \n",
+    "            test = pickle.load(f)\n",
+    "        print(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'meshme3.txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-190-24b9bf6d3f76>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcocumber\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Staying ALive!!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-189-df4c0cfbf515>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'potato.pkl'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mashme3.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'meshme3.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m             \u001b[0mtest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'meshme3.txt'"
+     ]
+    }
+   ],
+   "source": [
+    "cocumber('Staying ALive!!')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb b/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb
index c33c59b..6bfc242 100644
--- a/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb	
+++ b/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb	
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -81,7 +81,7 @@
        "(5, 1)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
        "2"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -122,7 +122,7 @@
        "2"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +149,7 @@
        "1"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +169,7 @@
        "array([1, 2])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
        "array([2, 3])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +209,7 @@
        "5"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +229,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -279,7 +279,7 @@
        "(2, 3)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -295,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -343,7 +343,7 @@
        "(2, 3, 4)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -376,18 +376,16 @@
      "text": [
       "[[1 2 3 4]\n",
       " [2 3 4 4]\n",
-      " [2 3 4 4]] \n",
-      "\n",
+      " [2 3 4 4]]\n",
       "[[5 6 7 4]\n",
       " [4 6 9 4]\n",
-      " [2 3 4 4]] \n",
-      "\n"
+      " [2 3 4 4]]\n"
      ]
     }
    ],
    "source": [
     "for i in tensor:\n",
-    "    print(i, \"\\n\")"
+    "    print(i)"
    ]
   },
   {
@@ -407,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -429,7 +427,7 @@
        "(3, 4)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -440,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -451,7 +449,7 @@
        "       [2, 3, 4, 4]])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -462,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -471,7 +469,7 @@
        "array([ 5,  8, 11, 12])"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -502,27 +500,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2., 3., 4., 4.])"
+       "dtype('int32')"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.median(array, axis = 0)"
+    "array.dtype"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -531,7 +529,7 @@
        "array([2., 3., 4., 4.])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -549,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -569,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -647,9 +645,67 @@
     "<h3 style=\"color:blue\">Make a 2-d array in numpy:</h3>\n",
     "<p style=\"color:blue\">- find the row wise mean</p>\n",
     "<p style=\"color:blue\">- find the column wise mean</p>\n",
-    "<p style=\"color:blue\">- do the same for the transpose of the array you've made</p>"
+    "<p style=\"color:blue\">- do the same for the transpose of the array you've made</p>\n",
+    "<p style=\"color:blue\">- check if matrix square</p>\n",
+    "<p style=\"color:blue\">- multiple the matrix by transpose</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3. 3. 3. 3.]\n",
+      "[4. 4. 4.]\n",
+      "[4. 4. 4.]\n",
+      "[3. 3. 3. 3.]\n",
+      "Not a square!\n",
+      "[12. 12. 12.]\n",
+      "[12. 12. 12.]\n",
+      "Square as me!\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.ones([3,4])\n",
+    "\n",
+    "\n",
+    "print(a.sum(0))\n",
+    "print(a.sum(1))\n",
+    "\n",
+    "b = a.T\n",
+    "\n",
+    "print(b.sum(0))\n",
+    "print(b.sum(1))\n",
+    "\n",
+    "\n",
+    "if(a.shape[0]!=a.shape[1]):\n",
+    "    print(\"Not a square!\")\n",
+    "\n",
+    "\n",
+    "c = np.dot(a,b)\n",
+    "\n",
+    "print(c.sum(0))\n",
+    "print(c.sum(1))\n",
+    "\n",
+    "\n",
+    "if(c.shape[0]!=c.shape[1]):\n",
+    "    print(\"Not a square!\")\n",
+    "else:\n",
+    "    print(\"Square as me!\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1108,7 +1164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1125,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1135,7 +1191,7 @@
        "       [22, 30]])"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1196,7 +1252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1213,7 +1269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1230,7 +1286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1238,10 +1294,10 @@
      "evalue": "The dimensions aren't aligned",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-102-fe46837ad8c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAssertionError\u001b[0m: The dimensions aren't aligned"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-57-fe46837ad8c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0marray1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0marray1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
      ]
     }
    ],
@@ -1303,6 +1359,59 @@
     "<p style=\"color:blue\">- return the dot product of the two matrices</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[19 27 10 17 14 21]\n",
+      " [28 39 13 25 21 28]]\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "The dimensions aren't aligned",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0marray2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36mrafdot\u001b[1;34m(m1, m2)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0mm2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m array = np.array([\n",
+      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
+     ]
+    }
+   ],
+   "source": [
+    "def rafdot(m1, m2):\n",
+    "    assert m1.shape[1] == m2.shape[0], \"The dimensions aren't aligned\"\n",
+    "    return m1@m2\n",
+    "\n",
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5],\n",
+    "    [2,3,2,2,1,3]\n",
+    "])\n",
+    "\n",
+    "array2 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5]])\n",
+    "\n",
+    "c = rafdot(array,array1)\n",
+    "print(c)\n",
+    "\n",
+    "c = rafdot(array,array2)\n",
+    "print(c)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1514,6 +1623,36 @@
     "<p style=\"color:blue\">- returns the list</p>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[1, 4, 9],\n",
+       "        [4, 9, 9]]), array([[25, 36,  4, 25,  9,  4],\n",
+       "        [16, 36,  1,  9, 16, 25],\n",
+       "        [ 4,  9,  4,  4,  1,  9]])]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def raf_square(m1,m2):\n",
+    "    flist = []\n",
+    "    for k in m1, m2:\n",
+    "        c = k*k\n",
+    "        flist.append(c)\n",
+    "    return flist\n",
+    "\n",
+    "raf_square(array,array1)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1630,7 +1769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1643,16 +1782,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 1])"
+       "array([0, 0, 1], dtype=int64)"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1664,7 +1803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1675,7 +1814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -1684,7 +1823,7 @@
        "['A', 'A', 'B']"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1696,7 +1835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -1705,7 +1844,7 @@
        "[('item_A', 'A'), ('item_B', 'A'), ('item_C', 'B')]"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1716,7 +1855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -1727,7 +1866,7 @@
        "       ['item_C', 'B']], dtype='<U6')"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1797,7 +1936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1806,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1815,7 +1954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -1824,7 +1963,7 @@
        "[0, 1, 3, 6, 12, 51]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1835,7 +1974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1848,7 +1987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1859,7 +1998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -1870,7 +2009,7 @@
        "       [15,  8,  9]])"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1881,7 +2020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -1889,10 +2028,10 @@
       "text/plain": [
        "array([[0, 2, 1],\n",
        "       [0, 2, 1],\n",
-       "       [1, 2, 0]])"
+       "       [1, 2, 0]], dtype=int64)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1903,7 +2042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1916,7 +2055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -1924,13 +2063,13 @@
      "evalue": "list index out of range",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-47-86bed837003f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvectorized_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2089\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2090\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2091\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2092\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2093\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2165\u001b[0m                       for a in args]\n\u001b[1;32m   2166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2167\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2169\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-46-df07eceacbb6>\u001b[0m in \u001b[0;36mmyfunc\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmyfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"item_A\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"item_B\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"item_C\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvectorized_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvectorize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmyfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-84-86bed837003f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvectorized_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2089\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2091\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2092\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2093\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[1;34m(self, func, args)\u001b[0m\n\u001b[0;32m   2165\u001b[0m                       for a in args]\n\u001b[0;32m   2166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2167\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2169\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-83-df07eceacbb6>\u001b[0m in \u001b[0;36mmyfunc\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmyfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"item_A\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_B\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_C\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mvectorized_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvectorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmyfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
      ]
     }
    ],
@@ -2071,7 +2210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2080,7 +2219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2094,7 +2233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -2105,7 +2244,7 @@
        "       [2.        , 4.58257569, 0.        ]])"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2114,6 +2253,90 @@
     "squareform(pdist(array, \"euclidean\"))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.1 0.9]\n",
+      "[0. 1.]\n",
+      "[[0. 1.]\n",
+      " [1. 1.]]\n",
+      "(array([0.8, 0.1]), array([], dtype=float64), 2, array([1.61803399, 0.61803399]))\n",
+      "y = 0.09999999999999992x + 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
+      "  \n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "xdata = np.array([0.0,1.0])\n",
+    "ydata = np.array([0.1,0.9])\n",
+    "\n",
+    "data_pred = xdata.T * xdata.T\n",
+    "\n",
+    "A = np.vstack([xdata,np.ones(len(xdata))]).T\n",
+    "\n",
+    "\n",
+    "print(ydata)\n",
+    "print(xdata)\n",
+    "print(A)\n",
+    "\n",
+    "\n",
+    "print (np.linalg.lstsq(A, ydata))\n",
+    "\n",
+    "# the model has the following face y = a + bx\n",
+    "b, a = np.linalg.lstsq(A, ydata)[0]\n",
+    "print(\"y = \"+str(a)+'x + '+str(b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#find if a number is prime\n",
+    "#find if it is a prime of goldbach and the primes that build it\n",
+    "\n",
+    "\n",
+    "def isprime(number):\n",
+    "    if(number%2):\n",
+    "        if(number!=2):\n",
+    "            return 0\n",
+    "        else:\n",
+    "            return 1\n",
+    "    dividers = np.arange(number,1,2)\n",
+    "    print(dividers)\n",
+    "    for k in dividers:\n",
+    "        if number%divider:\n",
+    "            return 0\n",
+    "    return 1 \n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2129,7 +2352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2147,7 +2370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2159,7 +2382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -2170,7 +2393,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2192,7 +2415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2205,7 +2428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2214,7 +2437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -2223,7 +2446,7 @@
        "(3, 6)"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2234,7 +2457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -2285,7 +2508,7 @@
        "0  5  6  2  5  3  2"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2296,7 +2519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -2367,7 +2590,7 @@
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2378,7 +2601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -2387,7 +2610,7 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2398,7 +2621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -2407,7 +2630,7 @@
        "RangeIndex(start=0, stop=6, step=1)"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2425,7 +2648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2434,7 +2657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -2505,7 +2728,7 @@
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2516,22 +2739,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    int64\n",
-       "b    int64\n",
-       "c    int64\n",
-       "d    int64\n",
-       "e    int64\n",
-       "f    int64\n",
+       "a    int32\n",
+       "b    int32\n",
+       "c    int32\n",
+       "d    int32\n",
+       "e    int32\n",
+       "f    int32\n",
        "dtype: object"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2551,7 +2774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -2560,10 +2783,10 @@
        "0    5\n",
        "1    4\n",
        "2    2\n",
-       "Name: a, dtype: int64"
+       "Name: a, dtype: int32"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2574,7 +2797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -2583,7 +2806,7 @@
        "pandas.core.series.Series"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2594,7 +2817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2603,7 +2826,7 @@
        "[5, 4, 2]"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2614,7 +2837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2623,7 +2846,7 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2634,7 +2857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2689,7 +2912,7 @@
        "2  2  3"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2714,7 +2937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2726,10 +2949,10 @@
        "d    3\n",
        "e    4\n",
        "f    5\n",
-       "Name: 1, dtype: int64"
+       "Name: 1, dtype: int32"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2740,7 +2963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2749,10 +2972,10 @@
        "a    4\n",
        "b    6\n",
        "c    1\n",
-       "Name: 1, dtype: int64"
+       "Name: 1, dtype: int32"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2764,7 +2987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2825,7 +3048,7 @@
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2836,7 +3059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2907,7 +3130,7 @@
        "1  4  6  1  3  4  5"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2918,7 +3141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2989,7 +3212,7 @@
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3000,7 +3223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -3051,7 +3274,7 @@
        "2  2"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3062,7 +3285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -3105,7 +3328,7 @@
        "1  4  6"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3124,7 +3347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -3133,7 +3356,7 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3144,7 +3367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3153,7 +3376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -3224,7 +3447,7 @@
        "cc  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3235,7 +3458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -3247,10 +3470,10 @@
        "d    5\n",
        "e    3\n",
        "f    2\n",
-       "Name: aa, dtype: int64"
+       "Name: aa, dtype: int32"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3261,7 +3484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -3322,7 +3545,7 @@
        "bb  4  6  1  3  4  5"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3333,7 +3556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -3376,7 +3599,7 @@
        "aa  5  6"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3396,7 +3619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -3445,7 +3668,7 @@
        "bb  4  1"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3466,7 +3689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3475,7 +3698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -3507,21 +3730,21 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <td>aa</td>\n",
        "      <td>5</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <td>bb</td>\n",
        "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <td>cc</td>\n",
        "      <td>2</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
@@ -3532,13 +3755,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   a  b  c  d\n",
-       "0  5  6  2  5\n",
-       "1  4  6  1  3\n",
-       "2  2  3  2  2"
+       "    a  b  c  d\n",
+       "aa  5  6  2  5\n",
+       "bb  4  6  1  3\n",
+       "cc  2  3  2  2"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3556,7 +3779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -3659,7 +3882,7 @@
        "max    5.000000  6.000000  2.000000  5.000000"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3677,7 +3900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -3748,7 +3971,7 @@
        "d  0.928571  0.755929  0.188982  1.000000"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3766,7 +3989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -3779,7 +4002,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3790,19 +4013,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0    18\n",
-       "1    14\n",
-       "2     9\n",
+       "aa    18\n",
+       "bb    14\n",
+       "cc     9\n",
        "dtype: int64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3820,7 +4043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -3833,7 +4056,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3851,14 +4074,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/conagrabrands/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -3873,7 +4096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -3906,7 +4129,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <td>aa</td>\n",
        "      <td>5</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
@@ -3914,7 +4137,7 @@
        "      <td>AA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <td>bb</td>\n",
        "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>1</td>\n",
@@ -3922,7 +4145,7 @@
        "      <td>AA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <td>cc</td>\n",
        "      <td>2</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
@@ -3934,13 +4157,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   a  b  c  d class\n",
-       "0  5  6  2  5    AA\n",
-       "1  4  6  1  3    AA\n",
-       "2  2  3  2  2    BB"
+       "    a  b  c  d class\n",
+       "aa  5  6  2  5    AA\n",
+       "bb  4  6  1  3    AA\n",
+       "cc  2  3  2  2    BB"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3951,7 +4174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -4014,7 +4237,7 @@
        "BB     2   3  2  2"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4025,7 +4248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -4088,7 +4311,7 @@
        "BB     1  1  1  1"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4099,7 +4322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -4166,7 +4389,7 @@
        "BB     1  1  1  1      1"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4185,7 +4408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -4194,7 +4417,7 @@
        "Index(['AA', 'BB'], dtype='object', name='class')"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4205,7 +4428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -4263,7 +4486,7 @@
        "1    BB  1  1  1  1"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4274,7 +4497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -4283,7 +4506,7 @@
        "RangeIndex(start=0, stop=2, step=1)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4301,17 +4524,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
-    "path = \"data/iris.csv\"\n",
+    "path = \"../data/iris.csv\"\n",
     "df = pd.read_csv(path, sep = \",\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -4396,7 +4619,7 @@
        "4           5.0          3.6           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4423,7 +4646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -4432,7 +4655,7 @@
        "(61, 5)"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4444,7 +4667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -4453,7 +4676,7 @@
        "(23, 5)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4465,7 +4688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -4474,7 +4697,7 @@
        "(50, 5)"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4493,7 +4716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -4502,7 +4725,7 @@
        "array(['Setosa', 'Versicolor', 'Virginica'], dtype=object)"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4513,7 +4736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -4522,7 +4745,7 @@
        "(100, 5)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4543,7 +4766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -4552,7 +4775,7 @@
        "(50, 5)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4571,7 +4794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4581,7 +4804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -4630,7 +4853,7 @@
        "0           5.1          3.5           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4641,7 +4864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4650,7 +4873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -4735,7 +4958,7 @@
        "4          -5.0          3.6           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4746,7 +4969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -4757,7 +4980,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4768,7 +4991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4777,7 +5000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4787,7 +5010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -4872,7 +5095,7 @@
        "4           5.0         -3.6          -1.4         -0.2  Setosa"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4883,7 +5106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4892,7 +5115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -4977,7 +5200,7 @@
        "4          -5.0         -3.6          -1.4         -0.2  Setosa"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4995,7 +5218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -5009,7 +5232,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5020,7 +5243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5029,7 +5252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -5043,7 +5266,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5071,7 +5294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5080,7 +5303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -5104,10 +5327,24 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
        "      <th>sepal.width</th>\n",
        "      <th>petal.length</th>\n",
        "      <th>petal.width</th>\n",
+       "      <th>sepal.length_-4.3</th>\n",
+       "      <th>sepal.length_-4.4</th>\n",
+       "      <th>sepal.length_-4.5</th>\n",
+       "      <th>sepal.length_-4.6</th>\n",
+       "      <th>sepal.length_-4.7</th>\n",
+       "      <th>sepal.length_-4.8</th>\n",
+       "      <th>sepal.length_-4.9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sepal.length_-7.1</th>\n",
+       "      <th>sepal.length_-7.2</th>\n",
+       "      <th>sepal.length_-7.3</th>\n",
+       "      <th>sepal.length_-7.4</th>\n",
+       "      <th>sepal.length_-7.6</th>\n",
+       "      <th>sepal.length_-7.7</th>\n",
+       "      <th>sepal.length_-7.9</th>\n",
        "      <th>variety_Setosa</th>\n",
        "      <th>variety_Versicolor</th>\n",
        "      <th>variety_Virginica</th>\n",
@@ -5116,75 +5353,176 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
+       "      <td>-3.5</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
+       "      <td>-3.2</td>\n",
+       "      <td>-1.3</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
+       "      <td>-3.1</td>\n",
+       "      <td>-1.5</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
+       "      <td>-3.6</td>\n",
+       "      <td>-1.4</td>\n",
+       "      <td>-0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width  variety_Setosa  \\\n",
-       "0           5.1          3.5           1.4          0.2               1   \n",
-       "1           4.9          3.0           1.4          0.2               1   \n",
-       "2           4.7          3.2           1.3          0.2               1   \n",
-       "3           4.6          3.1           1.5          0.2               1   \n",
-       "4           5.0          3.6           1.4          0.2               1   \n",
+       "   sepal.width  petal.length  petal.width  sepal.length_-4.3  \\\n",
+       "0         -3.5          -1.4         -0.2                  0   \n",
+       "1         -3.0          -1.4         -0.2                  0   \n",
+       "2         -3.2          -1.3         -0.2                  0   \n",
+       "3         -3.1          -1.5         -0.2                  0   \n",
+       "4         -3.6          -1.4         -0.2                  0   \n",
+       "\n",
+       "   sepal.length_-4.4  sepal.length_-4.5  sepal.length_-4.6  sepal.length_-4.7  \\\n",
+       "0                  0                  0                  0                  0   \n",
+       "1                  0                  0                  0                  0   \n",
+       "2                  0                  0                  0                  1   \n",
+       "3                  0                  0                  1                  0   \n",
+       "4                  0                  0                  0                  0   \n",
+       "\n",
+       "   sepal.length_-4.8  sepal.length_-4.9  ...  sepal.length_-7.1  \\\n",
+       "0                  0                  0  ...                  0   \n",
+       "1                  0                  1  ...                  0   \n",
+       "2                  0                  0  ...                  0   \n",
+       "3                  0                  0  ...                  0   \n",
+       "4                  0                  0  ...                  0   \n",
        "\n",
-       "   variety_Versicolor  variety_Virginica  \n",
-       "0                   0                  0  \n",
-       "1                   0                  0  \n",
-       "2                   0                  0  \n",
-       "3                   0                  0  \n",
-       "4                   0                  0  "
+       "   sepal.length_-7.2  sepal.length_-7.3  sepal.length_-7.4  sepal.length_-7.6  \\\n",
+       "0                  0                  0                  0                  0   \n",
+       "1                  0                  0                  0                  0   \n",
+       "2                  0                  0                  0                  0   \n",
+       "3                  0                  0                  0                  0   \n",
+       "4                  0                  0                  0                  0   \n",
+       "\n",
+       "   sepal.length_-7.7  sepal.length_-7.9  variety_Setosa  variety_Versicolor  \\\n",
+       "0                  0                  0               1                   0   \n",
+       "1                  0                  0               1                   0   \n",
+       "2                  0                  0               1                   0   \n",
+       "3                  0                  0               1                   0   \n",
+       "4                  0                  0               1                   0   \n",
+       "\n",
+       "   variety_Virginica  \n",
+       "0                  0  \n",
+       "1                  0  \n",
+       "2                  0  \n",
+       "3                  0  \n",
+       "4                  0  \n",
+       "\n",
+       "[5 rows x 41 columns]"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5195,7 +5533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -5250,7 +5588,7 @@
        "2     C    CC"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5283,7 +5621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 76,
    "metadata": {
     "scrolled": true
    },
@@ -5292,46 +5630,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 sepal.length          5.1\n",
-      "sepal.width           3.5\n",
-      "petal.length          1.4\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 0, dtype: float64\n",
-      "1 sepal.length          4.9\n",
-      "sepal.width           3.0\n",
-      "petal.length          1.4\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 1, dtype: float64\n",
-      "2 sepal.length          4.7\n",
-      "sepal.width           3.2\n",
-      "petal.length          1.3\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 2, dtype: float64\n",
-      "3 sepal.length          4.6\n",
-      "sepal.width           3.1\n",
-      "petal.length          1.5\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 3, dtype: float64\n",
-      "4 sepal.length          5.0\n",
-      "sepal.width           3.6\n",
-      "petal.length          1.4\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 4, dtype: float64\n"
+      "0 col_1     A\n",
+      "col_2    AA\n",
+      "Name: 0, dtype: object\n",
+      "1 col_1     B\n",
+      "col_2    BB\n",
+      "Name: 1, dtype: object\n",
+      "2 col_1     C\n",
+      "col_2    CC\n",
+      "Name: 2, dtype: object\n"
      ]
     }
    ],
@@ -5342,18 +5649,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.4\n",
-      "1.4\n",
-      "1.3\n",
-      "1.5\n",
-      "1.4\n"
+     "ename": "KeyError",
+     "evalue": "'petal.length'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4735\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4736\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4737\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.get_value_at\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.validate_indexer\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-79-99dacd2ca267>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"petal.length\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1066\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1067\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1068\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1069\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1070\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4742\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4743\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4744\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4745\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4746\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4728\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4729\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4730\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4731\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4732\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'petal.length'"
      ]
     }
    ],
@@ -5371,167 +5694,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[0.2,\n",
-       " 0.2,\n",
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-       " 0.3,\n",
-       " 0.2,\n",
-       " 0.6,\n",
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-       " 0.2,\n",
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-       " 0.2,\n",
-       " 0.2,\n",
-       " 1.4,\n",
-       " 1.5,\n",
-       " 1.5,\n",
-       " 1.3,\n",
-       " 1.5,\n",
-       " 1.3,\n",
-       " 1.6,\n",
-       " 1.0,\n",
-       " 1.3,\n",
-       " 1.4,\n",
-       " 1.0,\n",
-       " 1.5,\n",
-       " 1.0,\n",
-       " 1.4,\n",
-       " 1.3,\n",
-       " 1.4,\n",
-       " 1.5,\n",
-       " 1.0,\n",
-       " 1.5,\n",
-       " 1.1,\n",
-       " 1.8,\n",
-       " 1.3,\n",
-       " 1.5,\n",
-       " 1.2,\n",
-       " 1.3,\n",
-       " 1.4,\n",
-       " 1.4,\n",
-       " 1.7,\n",
-       " 1.5,\n",
-       " 1.0,\n",
-       " 1.1,\n",
-       " 1.0,\n",
-       " 1.2,\n",
-       " 1.6,\n",
-       " 1.5,\n",
-       " 1.6,\n",
-       " 1.5,\n",
-       " 1.3,\n",
-       " 1.3,\n",
-       " 1.3,\n",
-       " 1.2,\n",
-       " 1.4,\n",
-       " 1.2,\n",
-       " 1.0,\n",
-       " 1.3,\n",
-       " 1.2,\n",
-       " 1.3,\n",
-       " 1.3,\n",
-       " 1.1,\n",
-       " 1.3,\n",
-       " 2.5,\n",
-       " 1.9,\n",
-       " 2.1,\n",
-       " 1.8,\n",
-       " 2.2,\n",
-       " 2.1,\n",
-       " 1.7,\n",
-       " 1.8,\n",
-       " 1.8,\n",
-       " 2.5,\n",
-       " 2.0,\n",
-       " 1.9,\n",
-       " 2.1,\n",
-       " 2.0,\n",
-       " 2.4,\n",
-       " 2.3,\n",
-       " 1.8,\n",
-       " 2.2,\n",
-       " 2.3,\n",
-       " 1.5,\n",
-       " 2.3,\n",
-       " 2.0,\n",
-       " 2.0,\n",
-       " 1.8,\n",
-       " 2.1,\n",
-       " 1.8,\n",
-       " 1.8,\n",
-       " 1.8,\n",
-       " 2.1,\n",
-       " 1.6,\n",
-       " 1.9,\n",
-       " 2.0,\n",
-       " 2.2,\n",
-       " 1.5,\n",
-       " 1.4,\n",
-       " 2.3,\n",
-       " 2.4,\n",
-       " 1.8,\n",
-       " 1.8,\n",
-       " 2.1,\n",
-       " 2.4,\n",
-       " 2.3,\n",
-       " 1.9,\n",
-       " 2.3,\n",
-       " 2.5,\n",
-       " 2.3,\n",
-       " 1.9,\n",
-       " 2.0,\n",
-       " 2.3,\n",
-       " 1.8]"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "KeyError",
+     "evalue": "'petal.width'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'petal.width'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-80-4e0306d0c85d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"petal.width\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'petal.width'"
+     ]
     }
    ],
    "source": [
@@ -5548,7 +5737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -5615,7 +5804,7 @@
        "4     E          1"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5638,7 +5827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -5693,7 +5882,7 @@
        "2     C          1"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5712,14 +5901,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/conagrabrands/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
       "of pandas will change to not sort by default.\n",
       "\n",
       "To accept the future behavior, pass 'sort=False'.\n",
@@ -5820,7 +6009,7 @@
        "2     C        NaN        1.0"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5849,7 +6038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -5908,7 +6097,7 @@
        "2     C          1          1"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5928,7 +6117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -6001,7 +6190,7 @@
        "4     E          1        NaN"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6025,7 +6214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6036,7 +6225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -6112,7 +6301,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6123,7 +6312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -6199,7 +6388,7 @@
        "7  False"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6210,7 +6399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
@@ -6286,7 +6475,7 @@
        "7   True"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6297,7 +6486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -6373,7 +6562,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6384,7 +6573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -6460,7 +6649,7 @@
        "7    1.0"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6471,7 +6660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -6542,7 +6731,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6553,7 +6742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -6614,7 +6803,7 @@
        "Index: [0, 1, 2, 3, 4, 5, 6, 7]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6625,7 +6814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -6701,7 +6890,7 @@
        "7  1.000000"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6712,7 +6901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -6788,7 +6977,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6799,7 +6988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -6884,7 +7073,7 @@
        "7      7  1.000000"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6902,127 +7091,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
-    "path = \"/Users/conagrabrands/Documents/PythonForAnalytics/Lectures/data/ca.json\""
+    "path = \"../data/ca.json\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 100,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Toronto\", \n",
-      "\n",
-      "        \"admin\": \"Ontario\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"3934421\", \n",
-      "\n",
-      "        \"iso2\": \"CA\", \n",
-      "\n",
-      "        \"capital\": \"admin\", \n",
-      "\n",
-      "        \"lat\": \"43.666667\", \n",
-      "\n",
-      "        \"lng\": \"-79.416667\", \n",
-      "\n",
-      "        \"population\": \"5213000\"\n",
-      "\n",
-      "    }, \n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Montr\\u00e9al\", \n",
-      "\n",
-      "        \"admin\": \"Qu\\u00e9bec\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"2356556\", \n",
-      "\n",
-      "        \"iso2\": \"CA\", \n",
-      "\n",
-      "        \"capital\": \"\", \n",
-      "\n",
-      "        \"lat\": \"45.5\", \n",
-      "\n",
-      "        \"lng\": \"-73.583333\", \n",
-      "\n",
-      "        \"population\": \"3678000\"\n",
-      "\n",
-      "    }, \n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Vancouver\", \n",
-      "\n",
-      "        \"admin\": \"British Columbia\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"603502\", \n",
-      "\n",
-      "        \"iso2\": \"CA\", \n",
-      "\n",
-      "        \"capital\": \"\", \n",
-      "\n",
-      "        \"lat\": \"49.25\", \n",
-      "\n",
-      "        \"lng\": \"-123.133333\", \n",
-      "\n",
-      "        \"population\": \"2313328\"\n",
-      "\n",
-      "    }, \n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Ottawa\", \n",
-      "\n",
-      "        \"admin\": \"Ontario\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"812129\", \n",
-      "\n",
-      "        \"iso2\": \"CA\", \n",
-      "\n",
-      "        \"capital\": \"primary\", \n",
-      "\n",
-      "        \"lat\": \"45.416667\", \n",
-      "\n",
-      "        \"lng\": \"-75.7\", \n",
-      "\n",
-      "        \"population\": \"1145000\"\n",
-      "\n",
-      "    }, \n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Calgary\", \n",
-      "\n",
-      "        \"admin\": \"Alberta\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"915322\", \n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open(path, \"r\") as file:\n",
     "    line = file.readlines()\n",
@@ -7057,11 +7139,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
     "\n",
     "columns = [\"age\", \"job\", \"city\"]\n",
     "data = [\n",
@@ -7082,9 +7163,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 102,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Expected object or value",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-102-1ce8f6ab196d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    593\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mshould_close\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    715\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 717\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    718\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[1;34m(self, json)\u001b[0m\n\u001b[0;32m    737\u001b[0m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"frame\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 739\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    741\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"series\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    847\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    848\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 849\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_no_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    850\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    851\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1091\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m             self.obj = DataFrame(\n\u001b[1;32m-> 1093\u001b[1;33m                 \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprecise_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprecise_float\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1094\u001b[0m             )\n\u001b[0;32m   1095\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"split\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Expected object or value"
+     ]
+    }
+   ],
    "source": [
     "df = pd.read_json(path)"
    ]
@@ -7623,7 +7721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7632,33 +7730,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
-    "chunk_size = 100"
+    "chunk_size = 150"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
-    "path = \"/Users/conagrabrands/Documents/PythonForAnalytics/Lectures/data/iris.csv\""
+    "path = \"../data/iris.csv\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(100, 5)\n",
-      "(50, 5)\n"
+      "(150, 5)\n"
      ]
     }
    ],
@@ -7669,15 +7766,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(2, 4)\n",
-      "(1, 4)\n"
+      "(3, 4)\n"
      ]
     }
    ],

From 8d821b7993002fadf9fb5ff41aa863c0f02086ba Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 4 Feb 2020 14:15:25 -0600
Subject: [PATCH 11/36] update

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index bc8c467..cb82515 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 
 tests/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_1-checkpoint.ipynb
+*.pkl

From 0ab5d468a293a219cecdbe8c874ea376fa288173 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 10:58:17 -0600
Subject: [PATCH 12/36] Updates

---
 .gitignore                                    |    3 +
 MSCA07013.egg-info/PKG-INFO                   |   69 +
 MSCA07013.egg-info/SOURCES.txt                |    6 +
 MSCA07013.egg-info/dependency_links.txt       |    1 +
 MSCA07013.egg-info/top_level.txt              |    1 +
 .../Challenge 2 - Face Recognition.ipynb      |    7 +
 .../Lecture 4 and 5 - Numpy and Pandas.ipynb  | 1315 ++++++++++++-----
 lectures/mashme3.txt                          |  Bin 25 -> 0 bytes
 .../Untitled-checkpoint.ipynb                 |    6 +
 msca/__init__.py.txt                          |    1 +
 msca/teste.py.txt                             |    5 +
 setup.py                                      |    2 +-
 12 files changed, 1023 insertions(+), 393 deletions(-)
 create mode 100644 MSCA07013.egg-info/PKG-INFO
 create mode 100644 MSCA07013.egg-info/SOURCES.txt
 create mode 100644 MSCA07013.egg-info/dependency_links.txt
 create mode 100644 MSCA07013.egg-info/top_level.txt
 delete mode 100644 lectures/mashme3.txt
 create mode 100644 msca/.ipynb_checkpoints/Untitled-checkpoint.ipynb
 create mode 100644 msca/__init__.py.txt
 create mode 100644 msca/teste.py.txt

diff --git a/.gitignore b/.gitignore
index cb82515..8f91a90 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
 
 tests/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_1-checkpoint.ipynb
 *.pkl
+lectures/mashme3.txt
+dist/MSCA07013-0.0.1-py3.7.egg
+lectures/mashme3.txt
diff --git a/MSCA07013.egg-info/PKG-INFO b/MSCA07013.egg-info/PKG-INFO
new file mode 100644
index 0000000..e371093
--- /dev/null
+++ b/MSCA07013.egg-info/PKG-INFO
@@ -0,0 +1,69 @@
+Metadata-Version: 2.1
+Name: MSCA07013
+Version: 0.0.1
+Summary: A small example package
+Home-page: https://github.com/ravescovi/python_for_analitycs
+Author: Rafael Vescovi
+Author-email: ravescovi@gmail.com
+License: UNKNOWN
+Description: # Welcome to University Of Chicago's Python For Analytics
+        This course introduces the python language of programming to students who are preparing to study analytics and data science
+        
+        Please follow the instructions below to get your computer ready for this class.
+        
+        _Note Mac users: Once software is downloaded, if you double click to launch it, you may get permission errors. Try to right click on the downloaded software, pick "open" and continue. (Apple is trying to protect you from accidentally starting malware/virus)_
+        
+        ## Install Python (anaconda distribution)
+        Please install Python 3.x from this website: https://www.anaconda.com/distribution/
+        (do not install 2.7, it will be discontinued soon.) 
+        
+        Mac users:
+        Accept all default prompts
+        
+        Windows users:
+        Accept all default prompts, **except** "Add Anaconda to my PATH envrionment variable." Make sure this is checked.
+        
+        Anaconda's distribution of Python is widely used in the industry, particularly among data scientists. This distribution makes it easy to use many libraries and packages for data analysis, building models, visualization, etc.
+        
+        #### Aditional steps:
+        Execute these statements at the terminal (Windows users should use Anaconda Prompt)
+          - `jupyter contrib nbextension install --user`
+          - `jupyter nbextensions_configurator enable --user`
+        
+        Once installed, please start jupyter notebook and execute code provided below
+        1. Start `Anaconda Navigator` and click `Launch` on the panel labeled `Jupyter Notebook`
+        2. Create new notebook from the web interface
+        3. Execute this code:
+        ```
+        %%timeit
+        sum(range(1_000_000))
+        ```
+        4. Execute this code:
+        ```
+        from psutil import virtual_memory, disk_usage, cpu_count, os
+        
+        bytes_in_gb = 1024**3
+        
+        print("Memory:\t",round(virtual_memory().total/bytes_in_gb,4), "Gigabytes")
+        print("Disk:\t",round(disk_usage(os.path.abspath(os.sep)).total/bytes_in_gb,4), "Gigabytes")
+        print("CPUs:\t", cpu_count())
+        ```
+        
+        ## Install Git
+        Please intall Git, a version control sotware, from this website: https://git-scm.com/downloads (you are ok to use default settings)
+        
+        Note that this is a command-line tool. Once installed, you may not see a new icon to click. We will install a Desktop client to remedy this.
+        
+        Although we don't make heavy use of version control, you will be introduced to the concept. Installing Git also installs "Git Bash," and comand line environment which simulates Unix/Linux. We will do several exercises which will require this environment.
+        
+        ## Clone this repository [Optional on day 1]
+        1. Visit this web page: https://github.com/ravescovi/python_for_analytics
+        2. Click "Clone or download" and pick the "Download ZIP" option (unless you already have a GitHub account)
+        
+        
+Platform: UNKNOWN
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
diff --git a/MSCA07013.egg-info/SOURCES.txt b/MSCA07013.egg-info/SOURCES.txt
new file mode 100644
index 0000000..abfdf89
--- /dev/null
+++ b/MSCA07013.egg-info/SOURCES.txt
@@ -0,0 +1,6 @@
+README.md
+setup.py
+MSCA07013.egg-info/PKG-INFO
+MSCA07013.egg-info/SOURCES.txt
+MSCA07013.egg-info/dependency_links.txt
+MSCA07013.egg-info/top_level.txt
\ No newline at end of file
diff --git a/MSCA07013.egg-info/dependency_links.txt b/MSCA07013.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/MSCA07013.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/MSCA07013.egg-info/top_level.txt b/MSCA07013.egg-info/top_level.txt
new file mode 100644
index 0000000..a8f9317
--- /dev/null
+++ b/MSCA07013.egg-info/top_level.txt
@@ -0,0 +1 @@
+msca
diff --git a/challenges/Challenge 2 - Face Recognition.ipynb b/challenges/Challenge 2 - Face Recognition.ipynb
index 323cbbd..1b5094a 100644
--- a/challenges/Challenge 2 - Face Recognition.ipynb	
+++ b/challenges/Challenge 2 - Face Recognition.ipynb	
@@ -20,6 +20,13 @@
     "2. Apply to 3 different pictures ."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb b/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb
index 6bfc242..621a347 100644
--- a/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb	
+++ b/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb	
@@ -2352,7 +2352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2370,7 +2370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2382,7 +2382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -2393,7 +2393,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2415,7 +2415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2428,7 +2428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2437,27 +2437,471 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 6)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['T',\n",
+       " '_AXIS_ALIASES',\n",
+       " '_AXIS_IALIASES',\n",
+       " '_AXIS_LEN',\n",
+       " '_AXIS_NAMES',\n",
+       " '_AXIS_NUMBERS',\n",
+       " '_AXIS_ORDERS',\n",
+       " '_AXIS_REVERSED',\n",
+       " '__abs__',\n",
+       " '__add__',\n",
+       " '__and__',\n",
+       " '__array__',\n",
+       " '__array_priority__',\n",
+       " '__array_wrap__',\n",
+       " '__bool__',\n",
+       " '__class__',\n",
+       " '__contains__',\n",
+       " '__copy__',\n",
+       " '__deepcopy__',\n",
+       " '__delattr__',\n",
+       " '__delitem__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__div__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__finalize__',\n",
+       " '__floordiv__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattr__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__iadd__',\n",
+       " '__iand__',\n",
+       " '__ifloordiv__',\n",
+       " '__imod__',\n",
+       " '__imul__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__invert__',\n",
+       " '__ior__',\n",
+       " '__ipow__',\n",
+       " '__isub__',\n",
+       " '__iter__',\n",
+       " '__itruediv__',\n",
+       " '__ixor__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__matmul__',\n",
+       " '__mod__',\n",
+       " '__module__',\n",
+       " '__mul__',\n",
+       " '__ne__',\n",
+       " '__neg__',\n",
+       " '__new__',\n",
+       " '__nonzero__',\n",
+       " '__or__',\n",
+       " '__pos__',\n",
+       " '__pow__',\n",
+       " '__radd__',\n",
+       " '__rand__',\n",
+       " '__rdiv__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rfloordiv__',\n",
+       " '__rmatmul__',\n",
+       " '__rmod__',\n",
+       " '__rmul__',\n",
+       " '__ror__',\n",
+       " '__round__',\n",
+       " '__rpow__',\n",
+       " '__rsub__',\n",
+       " '__rtruediv__',\n",
+       " '__rxor__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__setstate__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " '__truediv__',\n",
+       " '__weakref__',\n",
+       " '__xor__',\n",
+       " '_accessors',\n",
+       " '_add_numeric_operations',\n",
+       " '_add_series_only_operations',\n",
+       " '_add_series_or_dataframe_operations',\n",
+       " '_agg_by_level',\n",
+       " '_agg_examples_doc',\n",
+       " '_agg_summary_and_see_also_doc',\n",
+       " '_aggregate',\n",
+       " '_aggregate_multiple_funcs',\n",
+       " '_align_frame',\n",
+       " '_align_series',\n",
+       " '_box_col_values',\n",
+       " '_box_item_values',\n",
+       " '_builtin_table',\n",
+       " '_check_inplace_setting',\n",
+       " '_check_is_chained_assignment_possible',\n",
+       " '_check_label_or_level_ambiguity',\n",
+       " '_check_percentile',\n",
+       " '_check_setitem_copy',\n",
+       " '_clear_item_cache',\n",
+       " '_clip_with_one_bound',\n",
+       " '_clip_with_scalar',\n",
+       " '_combine_const',\n",
+       " '_combine_frame',\n",
+       " '_combine_match_columns',\n",
+       " '_combine_match_index',\n",
+       " '_consolidate',\n",
+       " '_consolidate_inplace',\n",
+       " '_construct_axes_dict',\n",
+       " '_construct_axes_dict_from',\n",
+       " '_construct_axes_from_arguments',\n",
+       " '_constructor',\n",
+       " '_constructor_expanddim',\n",
+       " '_constructor_sliced',\n",
+       " '_convert',\n",
+       " '_count_level',\n",
+       " '_create_indexer',\n",
+       " '_cython_table',\n",
+       " '_data',\n",
+       " '_deprecations',\n",
+       " '_dir_additions',\n",
+       " '_dir_deletions',\n",
+       " '_drop_axis',\n",
+       " '_drop_labels_or_levels',\n",
+       " '_ensure_valid_index',\n",
+       " '_find_valid_index',\n",
+       " '_from_arrays',\n",
+       " '_from_axes',\n",
+       " '_get_agg_axis',\n",
+       " '_get_axis',\n",
+       " '_get_axis_name',\n",
+       " '_get_axis_number',\n",
+       " '_get_axis_resolvers',\n",
+       " '_get_block_manager_axis',\n",
+       " '_get_bool_data',\n",
+       " '_get_cacher',\n",
+       " '_get_index_resolvers',\n",
+       " '_get_item_cache',\n",
+       " '_get_label_or_level_values',\n",
+       " '_get_numeric_data',\n",
+       " '_get_space_character_free_column_resolvers',\n",
+       " '_get_value',\n",
+       " '_get_values',\n",
+       " '_getitem_bool_array',\n",
+       " '_getitem_frame',\n",
+       " '_getitem_multilevel',\n",
+       " '_gotitem',\n",
+       " '_iget_item_cache',\n",
+       " '_indexed_same',\n",
+       " '_info_axis',\n",
+       " '_info_axis_name',\n",
+       " '_info_axis_number',\n",
+       " '_info_repr',\n",
+       " '_init_mgr',\n",
+       " '_internal_get_values',\n",
+       " '_internal_names',\n",
+       " '_internal_names_set',\n",
+       " '_is_builtin_func',\n",
+       " '_is_cached',\n",
+       " '_is_copy',\n",
+       " '_is_cython_func',\n",
+       " '_is_datelike_mixed_type',\n",
+       " '_is_homogeneous_type',\n",
+       " '_is_label_or_level_reference',\n",
+       " '_is_label_reference',\n",
+       " '_is_level_reference',\n",
+       " '_is_mixed_type',\n",
+       " '_is_numeric_mixed_type',\n",
+       " '_is_view',\n",
+       " '_ix',\n",
+       " '_ixs',\n",
+       " '_join_compat',\n",
+       " '_maybe_cache_changed',\n",
+       " '_maybe_update_cacher',\n",
+       " '_metadata',\n",
+       " '_needs_reindex_multi',\n",
+       " '_obj_with_exclusions',\n",
+       " '_protect_consolidate',\n",
+       " '_reduce',\n",
+       " '_reindex_axes',\n",
+       " '_reindex_columns',\n",
+       " '_reindex_index',\n",
+       " '_reindex_multi',\n",
+       " '_reindex_with_indexers',\n",
+       " '_repr_data_resource_',\n",
+       " '_repr_fits_horizontal_',\n",
+       " '_repr_fits_vertical_',\n",
+       " '_repr_html_',\n",
+       " '_repr_latex_',\n",
+       " '_reset_cache',\n",
+       " '_reset_cacher',\n",
+       " '_sanitize_column',\n",
+       " '_selected_obj',\n",
+       " '_selection',\n",
+       " '_selection_list',\n",
+       " '_selection_name',\n",
+       " '_series',\n",
+       " '_set_as_cached',\n",
+       " '_set_axis',\n",
+       " '_set_axis_name',\n",
+       " '_set_is_copy',\n",
+       " '_set_item',\n",
+       " '_set_value',\n",
+       " '_setitem_array',\n",
+       " '_setitem_frame',\n",
+       " '_setitem_slice',\n",
+       " '_setup_axes',\n",
+       " '_shallow_copy',\n",
+       " '_slice',\n",
+       " '_stat_axis',\n",
+       " '_stat_axis_name',\n",
+       " '_stat_axis_number',\n",
+       " '_to_dict_of_blocks',\n",
+       " '_try_aggregate_string_function',\n",
+       " '_typ',\n",
+       " '_unpickle_frame_compat',\n",
+       " '_unpickle_matrix_compat',\n",
+       " '_update_inplace',\n",
+       " '_validate_dtype',\n",
+       " '_values',\n",
+       " '_where',\n",
+       " '_xs',\n",
+       " 'abs',\n",
+       " 'add',\n",
+       " 'add_prefix',\n",
+       " 'add_suffix',\n",
+       " 'agg',\n",
+       " 'aggregate',\n",
+       " 'align',\n",
+       " 'all',\n",
+       " 'any',\n",
+       " 'append',\n",
+       " 'apply',\n",
+       " 'applymap',\n",
+       " 'as_matrix',\n",
+       " 'asfreq',\n",
+       " 'asof',\n",
+       " 'assign',\n",
+       " 'astype',\n",
+       " 'at',\n",
+       " 'at_time',\n",
+       " 'axes',\n",
+       " 'between_time',\n",
+       " 'bfill',\n",
+       " 'bool',\n",
+       " 'boxplot',\n",
+       " 'clip',\n",
+       " 'clip_lower',\n",
+       " 'clip_upper',\n",
+       " 'columns',\n",
+       " 'combine',\n",
+       " 'combine_first',\n",
+       " 'compound',\n",
+       " 'copy',\n",
+       " 'corr',\n",
+       " 'corrwith',\n",
+       " 'count',\n",
+       " 'cov',\n",
+       " 'cummax',\n",
+       " 'cummin',\n",
+       " 'cumprod',\n",
+       " 'cumsum',\n",
+       " 'describe',\n",
+       " 'diff',\n",
+       " 'div',\n",
+       " 'divide',\n",
+       " 'dot',\n",
+       " 'drop',\n",
+       " 'drop_duplicates',\n",
+       " 'droplevel',\n",
+       " 'dropna',\n",
+       " 'dtypes',\n",
+       " 'duplicated',\n",
+       " 'empty',\n",
+       " 'eq',\n",
+       " 'equals',\n",
+       " 'eval',\n",
+       " 'ewm',\n",
+       " 'expanding',\n",
+       " 'explode',\n",
+       " 'ffill',\n",
+       " 'fillna',\n",
+       " 'filter',\n",
+       " 'first',\n",
+       " 'first_valid_index',\n",
+       " 'floordiv',\n",
+       " 'from_dict',\n",
+       " 'from_records',\n",
+       " 'ftypes',\n",
+       " 'ge',\n",
+       " 'get',\n",
+       " 'get_dtype_counts',\n",
+       " 'get_ftype_counts',\n",
+       " 'get_values',\n",
+       " 'groupby',\n",
+       " 'gt',\n",
+       " 'head',\n",
+       " 'hist',\n",
+       " 'iat',\n",
+       " 'idxmax',\n",
+       " 'idxmin',\n",
+       " 'iloc',\n",
+       " 'index',\n",
+       " 'infer_objects',\n",
+       " 'info',\n",
+       " 'insert',\n",
+       " 'interpolate',\n",
+       " 'isin',\n",
+       " 'isna',\n",
+       " 'isnull',\n",
+       " 'items',\n",
+       " 'iteritems',\n",
+       " 'iterrows',\n",
+       " 'itertuples',\n",
+       " 'ix',\n",
+       " 'join',\n",
+       " 'keys',\n",
+       " 'kurt',\n",
+       " 'kurtosis',\n",
+       " 'last',\n",
+       " 'last_valid_index',\n",
+       " 'le',\n",
+       " 'loc',\n",
+       " 'lookup',\n",
+       " 'lt',\n",
+       " 'mad',\n",
+       " 'mask',\n",
+       " 'max',\n",
+       " 'mean',\n",
+       " 'median',\n",
+       " 'melt',\n",
+       " 'memory_usage',\n",
+       " 'merge',\n",
+       " 'min',\n",
+       " 'mod',\n",
+       " 'mode',\n",
+       " 'mul',\n",
+       " 'multiply',\n",
+       " 'ndim',\n",
+       " 'ne',\n",
+       " 'nlargest',\n",
+       " 'notna',\n",
+       " 'notnull',\n",
+       " 'nsmallest',\n",
+       " 'nunique',\n",
+       " 'pct_change',\n",
+       " 'pipe',\n",
+       " 'pivot',\n",
+       " 'pivot_table',\n",
+       " 'plot',\n",
+       " 'pop',\n",
+       " 'pow',\n",
+       " 'prod',\n",
+       " 'product',\n",
+       " 'quantile',\n",
+       " 'query',\n",
+       " 'radd',\n",
+       " 'rank',\n",
+       " 'rdiv',\n",
+       " 'reindex',\n",
+       " 'reindex_like',\n",
+       " 'rename',\n",
+       " 'rename_axis',\n",
+       " 'reorder_levels',\n",
+       " 'replace',\n",
+       " 'resample',\n",
+       " 'reset_index',\n",
+       " 'rfloordiv',\n",
+       " 'rmod',\n",
+       " 'rmul',\n",
+       " 'rolling',\n",
+       " 'round',\n",
+       " 'rpow',\n",
+       " 'rsub',\n",
+       " 'rtruediv',\n",
+       " 'sample',\n",
+       " 'select_dtypes',\n",
+       " 'sem',\n",
+       " 'set_axis',\n",
+       " 'set_index',\n",
+       " 'shape',\n",
+       " 'shift',\n",
+       " 'size',\n",
+       " 'skew',\n",
+       " 'slice_shift',\n",
+       " 'sort_index',\n",
+       " 'sort_values',\n",
+       " 'sparse',\n",
+       " 'squeeze',\n",
+       " 'stack',\n",
+       " 'std',\n",
+       " 'style',\n",
+       " 'sub',\n",
+       " 'subtract',\n",
+       " 'sum',\n",
+       " 'swapaxes',\n",
+       " 'swaplevel',\n",
+       " 'tail',\n",
+       " 'take',\n",
+       " 'to_clipboard',\n",
+       " 'to_csv',\n",
+       " 'to_dense',\n",
+       " 'to_dict',\n",
+       " 'to_excel',\n",
+       " 'to_feather',\n",
+       " 'to_gbq',\n",
+       " 'to_hdf',\n",
+       " 'to_html',\n",
+       " 'to_json',\n",
+       " 'to_latex',\n",
+       " 'to_msgpack',\n",
+       " 'to_numpy',\n",
+       " 'to_parquet',\n",
+       " 'to_period',\n",
+       " 'to_pickle',\n",
+       " 'to_records',\n",
+       " 'to_sparse',\n",
+       " 'to_sql',\n",
+       " 'to_stata',\n",
+       " 'to_string',\n",
+       " 'to_timestamp',\n",
+       " 'to_xarray',\n",
+       " 'transform',\n",
+       " 'transpose',\n",
+       " 'truediv',\n",
+       " 'truncate',\n",
+       " 'tshift',\n",
+       " 'tz_convert',\n",
+       " 'tz_localize',\n",
+       " 'unstack',\n",
+       " 'update',\n",
+       " 'values',\n",
+       " 'var',\n",
+       " 'where',\n",
+       " 'xs']"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -2499,27 +2943,37 @@
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "   0  1  2  3  4  5\n",
-       "0  5  6  2  5  3  2"
+       "0  5  6  2  5  3  2\n",
+       "1  4  6  1  3  4  5"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head(1)"
+    "df.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
@@ -2590,7 +3044,7 @@
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2601,7 +3055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -2610,7 +3064,7 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2621,7 +3075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -2630,7 +3084,7 @@
        "RangeIndex(start=0, stop=6, step=1)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2648,16 +3102,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.columns = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"]"
+    "df.columns = [\"R\", \"b\", \"c\", \"d\", \"e\", \"f\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -2681,7 +3135,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -2722,13 +3176,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   a  b  c  d  e  f\n",
+       "   R  b  c  d  e  f\n",
        "0  5  6  2  5  3  2\n",
        "1  4  6  1  3  4  5\n",
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2739,13 +3193,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    int32\n",
+       "R    int32\n",
        "b    int32\n",
        "c    int32\n",
        "d    int32\n",
@@ -2754,7 +3208,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2774,7 +3228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -2783,21 +3237,21 @@
        "0    5\n",
        "1    4\n",
        "2    2\n",
-       "Name: a, dtype: int32"
+       "Name: R, dtype: int32"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[\"a\"]"
+    "df[\"R\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
@@ -2806,18 +3260,18 @@
        "pandas.core.series.Series"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(df[\"a\"])"
+    "type(df[\"R\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
@@ -2826,18 +3280,18 @@
        "[5, 4, 2]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[\"a\"].tolist()"
+    "df[\"R\"].tolist()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
@@ -2846,18 +3300,18 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[\"a\"].index"
+    "df[\"R\"].index"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [
     {
@@ -2881,19 +3335,19 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>c</th>\n",
        "      <th>b</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>5</td>\n",
+       "      <td>2</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>4</td>\n",
+       "      <td>1</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2906,19 +3360,19 @@
        "</div>"
       ],
       "text/plain": [
-       "   a  b\n",
-       "0  5  6\n",
-       "1  4  6\n",
+       "   c  b\n",
+       "0  2  6\n",
+       "1  1  6\n",
        "2  2  3"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[[\"a\", \"b\"]]"
+    "df[[\"c\", \"b\"]]"
    ]
   },
   {
@@ -2937,45 +3391,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    4\n",
+       "R    5\n",
        "b    6\n",
-       "c    1\n",
-       "d    3\n",
-       "e    4\n",
-       "f    5\n",
-       "Name: 1, dtype: int32"
+       "c    2\n",
+       "d    5\n",
+       "e    3\n",
+       "f    2\n",
+       "Name: 0, dtype: int32"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.iloc[1]"
+    "df.iloc[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    4\n",
+       "R    4\n",
        "b    6\n",
        "c    1\n",
        "Name: 1, dtype: int32"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2987,7 +3441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [
     {
@@ -3011,7 +3465,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -3043,12 +3497,12 @@
        "</div>"
       ],
       "text/plain": [
-       "   a  b  c  d  e  f\n",
+       "   R  b  c  d  e  f\n",
        "1  4  6  1  3  4  5\n",
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 166,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3059,7 +3513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [
     {
@@ -3083,7 +3537,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -3124,13 +3578,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   a  b  c  d  e  f\n",
+       "   R  b  c  d  e  f\n",
        "0  5  6  2  5  3  2\n",
        "2  2  3  2  2  1  3\n",
        "1  4  6  1  3  4  5"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3141,7 +3595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
@@ -3165,7 +3619,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -3206,13 +3660,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   a  b  c  d  e  f\n",
+       "   R  b  c  d  e  f\n",
        "0  5  6  2  5  3  2\n",
        "1  4  6  1  3  4  5\n",
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3223,7 +3677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [
     {
@@ -3247,7 +3701,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -3268,13 +3722,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   a\n",
+       "   R\n",
        "0  5\n",
        "1  4\n",
        "2  2"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3285,7 +3739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
@@ -3309,7 +3763,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>b</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -3324,11 +3778,11 @@
        "</div>"
       ],
       "text/plain": [
-       "   a  b\n",
+       "   R  b\n",
        "1  4  6"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3347,7 +3801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
@@ -3356,7 +3810,7 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3367,7 +3821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3376,7 +3830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [
     {
@@ -3400,7 +3854,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -3441,13 +3895,13 @@
        "</div>"
       ],
       "text/plain": [
-       "    a  b  c  d  e  f\n",
+       "    R  b  c  d  e  f\n",
        "aa  5  6  2  5  3  2\n",
        "bb  4  6  1  3  4  5\n",
        "cc  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3458,13 +3912,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    5\n",
+       "R    5\n",
        "b    6\n",
        "c    2\n",
        "d    5\n",
@@ -3473,7 +3927,7 @@
        "Name: aa, dtype: int32"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3484,7 +3938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [
     {
@@ -3508,7 +3962,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -3540,12 +3994,12 @@
        "</div>"
       ],
       "text/plain": [
-       "    a  b  c  d  e  f\n",
+       "    R  b  c  d  e  f\n",
        "aa  5  6  2  5  3  2\n",
        "bb  4  6  1  3  4  5"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3556,7 +4010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 179,
    "metadata": {},
    "outputs": [
     {
@@ -3580,7 +4034,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>b</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -3595,17 +4049,17 @@
        "</div>"
       ],
       "text/plain": [
-       "    a  b\n",
+       "    R  b\n",
        "aa  5  6"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.loc[[\"aa\"], [\"a\",\"b\"]]"
+    "df.loc[[\"aa\"], [\"R\",\"b\"]]"
    ]
   },
   {
@@ -3619,7 +4073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 181,
    "metadata": {},
    "outputs": [
     {
@@ -3643,7 +4097,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
+       "      <th>R</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -3663,19 +4117,19 @@
        "</div>"
       ],
       "text/plain": [
-       "    a  c\n",
+       "    R  c\n",
        "aa  5  2\n",
        "bb  4  1"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "rows = [\"aa\", \"bb\"]\n",
-    "cols = [\"a\", \"c\"]\n",
+    "cols = [\"R\", \"c\"]\n",
     "\n",
     "df.loc[rows,cols]"
    ]
@@ -3689,16 +4143,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 184,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df1 = df[[\"a\", \"b\", \"c\", \"d\"]]"
+    "df1 = df[[ \"b\", \"c\", \"d\"]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 185,
    "metadata": {},
    "outputs": [
     {
@@ -3722,7 +4176,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -3731,21 +4184,18 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>aa</td>\n",
-       "      <td>5</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>bb</td>\n",
-       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>cc</td>\n",
-       "      <td>2</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
@@ -3755,13 +4205,13 @@
        "</div>"
       ],
       "text/plain": [
-       "    a  b  c  d\n",
-       "aa  5  6  2  5\n",
-       "bb  4  6  1  3\n",
-       "cc  2  3  2  2"
+       "    b  c  d\n",
+       "aa  6  2  5\n",
+       "bb  6  1  3\n",
+       "cc  3  2  2"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 185,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3779,7 +4229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [
     {
@@ -3803,7 +4253,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -3815,53 +4264,45 @@
        "      <td>3.000000</td>\n",
        "      <td>3.000000</td>\n",
        "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>mean</td>\n",
-       "      <td>3.666667</td>\n",
        "      <td>5.000000</td>\n",
        "      <td>1.666667</td>\n",
        "      <td>3.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>std</td>\n",
-       "      <td>1.527525</td>\n",
        "      <td>1.732051</td>\n",
        "      <td>0.577350</td>\n",
        "      <td>1.527525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>min</td>\n",
-       "      <td>2.000000</td>\n",
        "      <td>3.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>25%</td>\n",
-       "      <td>3.000000</td>\n",
        "      <td>4.500000</td>\n",
        "      <td>1.500000</td>\n",
        "      <td>2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>50%</td>\n",
-       "      <td>4.000000</td>\n",
        "      <td>6.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>75%</td>\n",
-       "      <td>4.500000</td>\n",
        "      <td>6.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>4.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>max</td>\n",
-       "      <td>5.000000</td>\n",
        "      <td>6.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>5.000000</td>\n",
@@ -3871,18 +4312,18 @@
        "</div>"
       ],
       "text/plain": [
-       "              a         b         c         d\n",
-       "count  3.000000  3.000000  3.000000  3.000000\n",
-       "mean   3.666667  5.000000  1.666667  3.333333\n",
-       "std    1.527525  1.732051  0.577350  1.527525\n",
-       "min    2.000000  3.000000  1.000000  2.000000\n",
-       "25%    3.000000  4.500000  1.500000  2.500000\n",
-       "50%    4.000000  6.000000  2.000000  3.000000\n",
-       "75%    4.500000  6.000000  2.000000  4.000000\n",
-       "max    5.000000  6.000000  2.000000  5.000000"
+       "              b         c         d\n",
+       "count  3.000000  3.000000  3.000000\n",
+       "mean   5.000000  1.666667  3.333333\n",
+       "std    1.732051  0.577350  1.527525\n",
+       "min    3.000000  1.000000  2.000000\n",
+       "25%    4.500000  1.500000  2.500000\n",
+       "50%    6.000000  2.000000  3.000000\n",
+       "75%    6.000000  2.000000  4.000000\n",
+       "max    6.000000  2.000000  5.000000"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3900,7 +4341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 187,
    "metadata": {},
    "outputs": [
     {
@@ -3924,7 +4365,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -3932,29 +4372,19 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>a</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.944911</td>\n",
-       "      <td>-0.188982</td>\n",
-       "      <td>0.928571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <td>b</td>\n",
-       "      <td>0.944911</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>-0.500000</td>\n",
        "      <td>0.755929</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>c</td>\n",
-       "      <td>-0.188982</td>\n",
        "      <td>-0.500000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.188982</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>d</td>\n",
-       "      <td>0.928571</td>\n",
        "      <td>0.755929</td>\n",
        "      <td>0.188982</td>\n",
        "      <td>1.000000</td>\n",
@@ -3964,14 +4394,13 @@
        "</div>"
       ],
       "text/plain": [
-       "          a         b         c         d\n",
-       "a  1.000000  0.944911 -0.188982  0.928571\n",
-       "b  0.944911  1.000000 -0.500000  0.755929\n",
-       "c -0.188982 -0.500000  1.000000  0.188982\n",
-       "d  0.928571  0.755929  0.188982  1.000000"
+       "          b         c         d\n",
+       "b  1.000000 -0.500000  0.755929\n",
+       "c -0.500000  1.000000  0.188982\n",
+       "d  0.755929  0.188982  1.000000"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3989,20 +4418,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    11\n",
        "b    15\n",
        "c     5\n",
        "d    10\n",
        "dtype: int64"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4013,25 +4441,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "aa    18\n",
-       "bb    14\n",
-       "cc     9\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n"
+     ]
     }
    ],
    "source": [
-    "df1.sum(axis = 1)"
+    "a = df1.sum(axis = 1)\n",
+    "print(type(a))"
    ]
   },
   {
@@ -4043,20 +4466,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    4.0\n",
        "b    6.0\n",
        "c    2.0\n",
        "d    3.0\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4074,7 +4496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [
     {
@@ -4096,7 +4518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [
     {
@@ -4120,7 +4542,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -4130,7 +4551,6 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>aa</td>\n",
-       "      <td>5</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
        "      <td>5</td>\n",
@@ -4138,7 +4558,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>bb</td>\n",
-       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
@@ -4146,7 +4565,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>cc</td>\n",
-       "      <td>2</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
@@ -4157,13 +4575,13 @@
        "</div>"
       ],
       "text/plain": [
-       "    a  b  c  d class\n",
-       "aa  5  6  2  5    AA\n",
-       "bb  4  6  1  3    AA\n",
-       "cc  2  3  2  2    BB"
+       "    b  c  d class\n",
+       "aa  6  2  5    AA\n",
+       "bb  6  1  3    AA\n",
+       "cc  3  2  2    BB"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4174,7 +4592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [
     {
@@ -4198,7 +4616,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -4208,20 +4625,17 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>AA</td>\n",
-       "      <td>9</td>\n",
        "      <td>12</td>\n",
        "      <td>3</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>BB</td>\n",
-       "      <td>2</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
@@ -4231,13 +4645,13 @@
        "</div>"
       ],
       "text/plain": [
-       "       a   b  c  d\n",
-       "class             \n",
-       "AA     9  12  3  8\n",
-       "BB     2   3  2  2"
+       "        b  c  d\n",
+       "class          \n",
+       "AA     12  3  8\n",
+       "BB      3  2  2"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4248,7 +4662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [
     {
@@ -4272,7 +4686,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -4282,7 +4695,6 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -4291,27 +4703,25 @@
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>BB</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       a  b  c  d\n",
-       "class            \n",
-       "AA     2  2  2  2\n",
-       "BB     1  1  1  1"
+       "       b  c  d\n",
+       "class         \n",
+       "AA     2  2  2\n",
+       "BB     1  1  1"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 200,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4322,7 +4732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 201,
    "metadata": {},
    "outputs": [
     {
@@ -4346,7 +4756,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
@@ -4358,13 +4767,11 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>AA</td>\n",
-       "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
@@ -4376,20 +4783,19 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       a  b  c  d  class\n",
-       "class                   \n",
-       "AA     2  1  2  2      1\n",
-       "BB     1  1  1  1      1"
+       "       b  c  d  class\n",
+       "class                \n",
+       "AA     1  2  2      1\n",
+       "BB     1  1  1      1"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4497,7 +4903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [
     {
@@ -4506,7 +4912,7 @@
        "RangeIndex(start=0, stop=2, step=1)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4524,7 +4930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 210,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4534,7 +4940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 211,
    "metadata": {},
    "outputs": [
     {
@@ -4619,7 +5025,7 @@
        "4           5.0          3.6           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4646,28 +5052,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 217,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(61, 5)"
+       "(118, 5)"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 217,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df1 = df[df[\"sepal.length\"] > 6]\n",
+    "df1 = df[df[\"sepal.length\"] > 5]\n",
     "df1.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 218,
    "metadata": {},
    "outputs": [
     {
@@ -4676,7 +5082,7 @@
        "(23, 5)"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 218,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4688,7 +5094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 219,
    "metadata": {},
    "outputs": [
     {
@@ -4697,7 +5103,7 @@
        "(50, 5)"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 219,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4716,7 +5122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 225,
    "metadata": {},
    "outputs": [
     {
@@ -4725,7 +5131,7 @@
        "array(['Setosa', 'Versicolor', 'Virginica'], dtype=object)"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4736,7 +5142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 226,
    "metadata": {},
    "outputs": [
     {
@@ -4745,7 +5151,7 @@
        "(100, 5)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4766,7 +5172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 227,
    "metadata": {},
    "outputs": [
     {
@@ -4775,7 +5181,7 @@
        "(50, 5)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 227,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4794,7 +5200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 228,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4804,7 +5210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 229,
    "metadata": {},
    "outputs": [
     {
@@ -4853,7 +5259,7 @@
        "0           5.1          3.5           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 229,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4864,7 +5270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 230,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4873,7 +5279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 231,
    "metadata": {},
    "outputs": [
     {
@@ -4958,7 +5364,7 @@
        "4          -5.0          3.6           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4969,7 +5375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 232,
    "metadata": {},
    "outputs": [
     {
@@ -4980,7 +5386,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 232,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4991,7 +5397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 233,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5000,7 +5406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 234,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5010,7 +5416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 236,
    "metadata": {},
    "outputs": [
     {
@@ -5095,7 +5501,7 @@
        "4           5.0         -3.6          -1.4         -0.2  Setosa"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 236,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5106,7 +5512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 237,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5115,7 +5521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 238,
    "metadata": {},
    "outputs": [
     {
@@ -5200,7 +5606,7 @@
        "4          -5.0         -3.6          -1.4         -0.2  Setosa"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 238,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5218,7 +5624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 239,
    "metadata": {},
    "outputs": [
     {
@@ -5232,7 +5638,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 239,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5243,7 +5649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 240,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5252,7 +5658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 241,
    "metadata": {},
    "outputs": [
     {
@@ -5266,7 +5672,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 241,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5294,7 +5700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 242,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5303,7 +5709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 243,
    "metadata": {},
    "outputs": [
     {
@@ -5522,7 +5928,7 @@
        "[5 rows x 41 columns]"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 243,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5533,7 +5939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 244,
    "metadata": {},
    "outputs": [
     {
@@ -5588,7 +5994,7 @@
        "2     C    CC"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 244,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5621,7 +6027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 246,
    "metadata": {
     "scrolled": true
    },
@@ -5649,40 +6055,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 249,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'petal.length'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4735\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4736\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4737\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.get_value_at\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.validate_indexer\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-79-99dacd2ca267>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"petal.length\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1066\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1067\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1068\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1069\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1070\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4742\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4743\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4744\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4745\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4746\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4728\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4729\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4730\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4731\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4732\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'petal.length'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "col_1     A\n",
+      "col_2    AA\n",
+      "Name: 0, dtype: object\n",
+      "col_1     B\n",
+      "col_2    BB\n",
+      "Name: 1, dtype: object\n",
+      "col_1     C\n",
+      "col_2    CC\n",
+      "Name: 2, dtype: object\n"
      ]
     }
    ],
    "source": [
     "for idx, r in df.head(5).iterrows():\n",
-    "    print(r[\"petal.length\"])"
+    "    print(r)"
    ]
   },
   {
@@ -5694,37 +6088,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 252,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'petal.width'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'petal.width'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-80-4e0306d0c85d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"petal.width\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'petal.width'"
-     ]
+     "data": {
+      "text/plain": [
+       "['A', 'B', 'C']"
+      ]
+     },
+     "execution_count": 252,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[\"petal.width\"].tolist()"
+    "df[\"col_1\"].tolist()"
    ]
   },
   {
@@ -5737,7 +6116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 253,
    "metadata": {},
    "outputs": [
     {
@@ -5804,7 +6183,7 @@
        "4     E          1"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 253,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5827,7 +6206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 254,
    "metadata": {},
    "outputs": [
     {
@@ -5882,7 +6261,7 @@
        "2     C          1"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 254,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5901,7 +6280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 255,
    "metadata": {},
    "outputs": [
     {
@@ -6009,7 +6388,7 @@
        "2     C        NaN        1.0"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 255,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6038,7 +6417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 257,
    "metadata": {},
    "outputs": [
     {
@@ -6097,7 +6476,7 @@
        "2     C          1          1"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 257,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6117,7 +6496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 258,
    "metadata": {},
    "outputs": [
     {
@@ -6190,7 +6569,7 @@
        "4     E          1        NaN"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 258,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6214,7 +6593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 259,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6225,7 +6604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 260,
    "metadata": {},
    "outputs": [
     {
@@ -6301,7 +6680,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 260,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6312,7 +6691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 261,
    "metadata": {},
    "outputs": [
     {
@@ -6388,7 +6767,7 @@
        "7  False"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 261,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6399,7 +6778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 262,
    "metadata": {},
    "outputs": [
     {
@@ -6475,7 +6854,7 @@
        "7   True"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 262,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6486,7 +6865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 263,
    "metadata": {},
    "outputs": [
     {
@@ -6562,7 +6941,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 263,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6573,7 +6952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 264,
    "metadata": {},
    "outputs": [
     {
@@ -6649,7 +7028,7 @@
        "7    1.0"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 264,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6660,7 +7039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 266,
    "metadata": {},
    "outputs": [
     {
@@ -6731,7 +7110,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 266,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6742,7 +7121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 267,
    "metadata": {},
    "outputs": [
     {
@@ -6803,7 +7182,7 @@
        "Index: [0, 1, 2, 3, 4, 5, 6, 7]"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 267,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6814,7 +7193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 268,
    "metadata": {},
    "outputs": [
     {
@@ -6890,7 +7269,7 @@
        "7  1.000000"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 268,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6901,7 +7280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 269,
    "metadata": {},
    "outputs": [
     {
@@ -6977,7 +7356,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 269,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6988,7 +7367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 270,
    "metadata": {},
    "outputs": [
     {
@@ -7073,7 +7452,7 @@
        "7      7  1.000000"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 270,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7091,11 +7470,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 301,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Documents\\GitHub\\python_for_analytics\\lectures\n",
+      "['../data\\\\ca.json']\n"
+     ]
+    }
+   ],
    "source": [
-    "path = \"../data/ca.json\""
+    "print(os.getcwd())\n",
+    "\n",
+    "dir(os.getcwd())\n",
+    "\n",
+    "import glob\n",
+    "path = glob.glob('../data/ca*')\n",
+    "print(path)"
    ]
   },
   {
@@ -7139,9 +7533,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 297,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>data scientist</td>\n",
+       "      <td>chicago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>data scientist</td>\n",
+       "      <td>new york</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age             job      city\n",
+       "0   31  data scientist   chicago\n",
+       "1   28  data scientist  new york\n",
+       "2   28            None      None"
+      ]
+     },
+     "execution_count": 297,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "columns = [\"age\", \"job\", \"city\"]\n",
@@ -7151,7 +7606,8 @@
     "    [28,None,None]\n",
     "]\n",
     "\n",
-    "df = pd.DataFrame(data, columns = columns)"
+    "df = pd.DataFrame(data, columns = columns)\n",
+    "df"
    ]
   },
   {
@@ -7163,7 +7619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 303,
    "metadata": {},
    "outputs": [
     {
@@ -7173,7 +7629,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-102-1ce8f6ab196d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-303-fa996717fc03>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../data/ca.json'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    593\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mshould_close\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    715\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 717\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    718\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[1;34m(self, json)\u001b[0m\n\u001b[0;32m    737\u001b[0m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"frame\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 739\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    741\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"series\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
@@ -7184,14 +7640,75 @@
     }
    ],
    "source": [
-    "df = pd.read_json(path)"
+    "df = pd.read_json('../data/ca.json')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 304,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>data scientist</td>\n",
+       "      <td>chicago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>data scientist</td>\n",
+       "      <td>new york</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age             job      city\n",
+       "0   31  data scientist   chicago\n",
+       "1   28  data scientist  new york\n",
+       "2   28            None      None"
+      ]
+     },
+     "execution_count": 304,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df"
    ]
@@ -7215,7 +7732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 305,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7233,7 +7750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 306,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7242,7 +7759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 308,
    "metadata": {},
    "outputs": [
     {
@@ -7305,7 +7822,7 @@
        "2  2.0  Faye Raker             130              60"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 308,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7316,7 +7833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 309,
    "metadata": {},
    "outputs": [
     {
@@ -7375,7 +7892,7 @@
        "2  2.0  Faye Raker  {'height': 130, 'weight': 60}"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 309,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7386,7 +7903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 311,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7406,7 +7923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 312,
    "metadata": {},
    "outputs": [
     {
@@ -7465,7 +7982,7 @@
        "1  [{'name': 'Summit', 'population': 1234}, {'nam...  "
       ]
      },
-     "execution_count": 29,
+     "execution_count": 312,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7476,7 +7993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 313,
    "metadata": {},
    "outputs": [
     {
@@ -7538,7 +8055,7 @@
        "1   John Kasich           m  "
       ]
      },
-     "execution_count": 30,
+     "execution_count": 313,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7622,7 +8139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 314,
    "metadata": {},
    "outputs": [
     {
@@ -7701,7 +8218,7 @@
        "4    Cuyahoga        1337   John Kasich           m"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 314,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7721,7 +8238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 317,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7730,16 +8247,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 321,
    "metadata": {},
    "outputs": [],
    "source": [
-    "chunk_size = 150"
+    "chunk_size = 20"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 322,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7748,14 +8265,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 323,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(150, 5)\n"
+      "(20, 5)\n",
+      "(20, 5)\n",
+      "(20, 5)\n",
+      "(20, 5)\n",
+      "(20, 5)\n",
+      "(20, 5)\n",
+      "(20, 5)\n",
+      "(10, 5)\n"
      ]
     }
    ],
@@ -7766,14 +8290,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 324,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(3, 4)\n"
+      "(1, 4)\n",
+      "(1, 4)\n",
+      "(2, 4)\n",
+      "(1, 4)\n",
+      "(1, 4)\n",
+      "(1, 4)\n",
+      "(1, 4)\n",
+      "(1, 4)\n"
      ]
     }
    ],
diff --git a/lectures/mashme3.txt b/lectures/mashme3.txt
deleted file mode 100644
index c4ccfeb75659b78a15f34c8ee0854820be1e719a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 25
gcmZo*j^JltU<fWrtjx?yS8(*nEK5~XEM(9F08<PGoB#j-

diff --git a/msca/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/msca/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/msca/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/msca/__init__.py.txt b/msca/__init__.py.txt
new file mode 100644
index 0000000..9e2909b
--- /dev/null
+++ b/msca/__init__.py.txt
@@ -0,0 +1 @@
+from teste import sum
\ No newline at end of file
diff --git a/msca/teste.py.txt b/msca/teste.py.txt
new file mode 100644
index 0000000..36e336f
--- /dev/null
+++ b/msca/teste.py.txt
@@ -0,0 +1,5 @@
+
+
+
+def raf_sum(a,b):
+  return a+b
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 527506b..5665831 100644
--- a/setup.py
+++ b/setup.py
@@ -12,7 +12,7 @@
     long_description=long_description,
     long_description_content_type="text/markdown",
     url="https://github.com/ravescovi/python_for_analitycs",
-    packages=setuptools.find_packages(),
+    packages=['msca'],
     classifiers=[
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: MIT License",

From 4189e4c28a42dd76016d3d1383fd39914a7643ae Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 11:23:02 -0600
Subject: [PATCH 13/36] correct packaging

---
 .gitignore                                    |  9 +--
 MSCA07013.egg-info/PKG-INFO                   | 69 -------------------
 MSCA07013.egg-info/SOURCES.txt                |  6 --
 MSCA07013.egg-info/dependency_links.txt       |  1 -
 MSCA07013.egg-info/top_level.txt              |  1 -
 .../Untitled-checkpoint.ipynb                 |  6 --
 msca/__init__.py.txt                          |  1 -
 msca/teste.py.txt                             |  5 --
 setup.py                                      | 14 ++--
 9 files changed, 11 insertions(+), 101 deletions(-)
 delete mode 100644 MSCA07013.egg-info/PKG-INFO
 delete mode 100644 MSCA07013.egg-info/SOURCES.txt
 delete mode 100644 MSCA07013.egg-info/dependency_links.txt
 delete mode 100644 MSCA07013.egg-info/top_level.txt
 delete mode 100644 msca/.ipynb_checkpoints/Untitled-checkpoint.ipynb
 delete mode 100644 msca/__init__.py.txt
 delete mode 100644 msca/teste.py.txt

diff --git a/.gitignore b/.gitignore
index 8f91a90..977fab8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 
-tests/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_1-checkpoint.ipynb
+*checkpoint.ipynb
 *.pkl
-lectures/mashme3.txt
-dist/MSCA07013-0.0.1-py3.7.egg
-lectures/mashme3.txt
+*.pyc
+/dist/
+/*.egg-info
+__pycache__
\ No newline at end of file
diff --git a/MSCA07013.egg-info/PKG-INFO b/MSCA07013.egg-info/PKG-INFO
deleted file mode 100644
index e371093..0000000
--- a/MSCA07013.egg-info/PKG-INFO
+++ /dev/null
@@ -1,69 +0,0 @@
-Metadata-Version: 2.1
-Name: MSCA07013
-Version: 0.0.1
-Summary: A small example package
-Home-page: https://github.com/ravescovi/python_for_analitycs
-Author: Rafael Vescovi
-Author-email: ravescovi@gmail.com
-License: UNKNOWN
-Description: # Welcome to University Of Chicago's Python For Analytics
-        This course introduces the python language of programming to students who are preparing to study analytics and data science
-        
-        Please follow the instructions below to get your computer ready for this class.
-        
-        _Note Mac users: Once software is downloaded, if you double click to launch it, you may get permission errors. Try to right click on the downloaded software, pick "open" and continue. (Apple is trying to protect you from accidentally starting malware/virus)_
-        
-        ## Install Python (anaconda distribution)
-        Please install Python 3.x from this website: https://www.anaconda.com/distribution/
-        (do not install 2.7, it will be discontinued soon.) 
-        
-        Mac users:
-        Accept all default prompts
-        
-        Windows users:
-        Accept all default prompts, **except** "Add Anaconda to my PATH envrionment variable." Make sure this is checked.
-        
-        Anaconda's distribution of Python is widely used in the industry, particularly among data scientists. This distribution makes it easy to use many libraries and packages for data analysis, building models, visualization, etc.
-        
-        #### Aditional steps:
-        Execute these statements at the terminal (Windows users should use Anaconda Prompt)
-          - `jupyter contrib nbextension install --user`
-          - `jupyter nbextensions_configurator enable --user`
-        
-        Once installed, please start jupyter notebook and execute code provided below
-        1. Start `Anaconda Navigator` and click `Launch` on the panel labeled `Jupyter Notebook`
-        2. Create new notebook from the web interface
-        3. Execute this code:
-        ```
-        %%timeit
-        sum(range(1_000_000))
-        ```
-        4. Execute this code:
-        ```
-        from psutil import virtual_memory, disk_usage, cpu_count, os
-        
-        bytes_in_gb = 1024**3
-        
-        print("Memory:\t",round(virtual_memory().total/bytes_in_gb,4), "Gigabytes")
-        print("Disk:\t",round(disk_usage(os.path.abspath(os.sep)).total/bytes_in_gb,4), "Gigabytes")
-        print("CPUs:\t", cpu_count())
-        ```
-        
-        ## Install Git
-        Please intall Git, a version control sotware, from this website: https://git-scm.com/downloads (you are ok to use default settings)
-        
-        Note that this is a command-line tool. Once installed, you may not see a new icon to click. We will install a Desktop client to remedy this.
-        
-        Although we don't make heavy use of version control, you will be introduced to the concept. Installing Git also installs "Git Bash," and comand line environment which simulates Unix/Linux. We will do several exercises which will require this environment.
-        
-        ## Clone this repository [Optional on day 1]
-        1. Visit this web page: https://github.com/ravescovi/python_for_analytics
-        2. Click "Clone or download" and pick the "Download ZIP" option (unless you already have a GitHub account)
-        
-        
-Platform: UNKNOWN
-Classifier: Programming Language :: Python :: 3
-Classifier: License :: OSI Approved :: MIT License
-Classifier: Operating System :: OS Independent
-Requires-Python: >=3.6
-Description-Content-Type: text/markdown
diff --git a/MSCA07013.egg-info/SOURCES.txt b/MSCA07013.egg-info/SOURCES.txt
deleted file mode 100644
index abfdf89..0000000
--- a/MSCA07013.egg-info/SOURCES.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-README.md
-setup.py
-MSCA07013.egg-info/PKG-INFO
-MSCA07013.egg-info/SOURCES.txt
-MSCA07013.egg-info/dependency_links.txt
-MSCA07013.egg-info/top_level.txt
\ No newline at end of file
diff --git a/MSCA07013.egg-info/dependency_links.txt b/MSCA07013.egg-info/dependency_links.txt
deleted file mode 100644
index 8b13789..0000000
--- a/MSCA07013.egg-info/dependency_links.txt
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/MSCA07013.egg-info/top_level.txt b/MSCA07013.egg-info/top_level.txt
deleted file mode 100644
index a8f9317..0000000
--- a/MSCA07013.egg-info/top_level.txt
+++ /dev/null
@@ -1 +0,0 @@
-msca
diff --git a/msca/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/msca/.ipynb_checkpoints/Untitled-checkpoint.ipynb
deleted file mode 100644
index 2fd6442..0000000
--- a/msca/.ipynb_checkpoints/Untitled-checkpoint.ipynb
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/msca/__init__.py.txt b/msca/__init__.py.txt
deleted file mode 100644
index 9e2909b..0000000
--- a/msca/__init__.py.txt
+++ /dev/null
@@ -1 +0,0 @@
-from teste import sum
\ No newline at end of file
diff --git a/msca/teste.py.txt b/msca/teste.py.txt
deleted file mode 100644
index 36e336f..0000000
--- a/msca/teste.py.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
-
-def raf_sum(a,b):
-  return a+b
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 5665831..76f0608 100644
--- a/setup.py
+++ b/setup.py
@@ -1,22 +1,20 @@
-import setuptools
+from setuptools import setup, find_packages
 
-with open("README.md", "r") as fh:
-    long_description = fh.read()
 
-setuptools.setup(
-    name="MSCA07013", # Replace with your own username
+setup(
+    name="msca", 
     version="0.0.1",
     author="Rafael Vescovi",
     author_email="ravescovi@gmail.com",
-    description="A small example package",
-    long_description=long_description,
+    description="MSCA Class Package",
     long_description_content_type="text/markdown",
     url="https://github.com/ravescovi/python_for_analitycs",
     packages=['msca'],
+    include_package_data=True,
     classifiers=[
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],
     python_requires='>=3.6',
-)
\ No newline at end of file
+)

From 5b5ab38aee539624c2109e747e65e9a9ccc1183d Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 11:23:13 -0600
Subject: [PATCH 14/36] correct packaging

---
 challenges/Challenge 1 - Solution.ipynb | 76 +++++++++++++++++++++++++
 msca/__init__.py                        |  1 +
 msca/teste.py                           |  5 ++
 3 files changed, 82 insertions(+)
 create mode 100644 challenges/Challenge 1 - Solution.ipynb
 create mode 100644 msca/__init__.py
 create mode 100644 msca/teste.py

diff --git a/challenges/Challenge 1 - Solution.ipynb b/challenges/Challenge 1 - Solution.ipynb
new file mode 100644
index 0000000..73ab1cb
--- /dev/null
+++ b/challenges/Challenge 1 - Solution.ipynb	
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NQueens:\n",
+    "    \"\"\"Generate all valid solutions for the n queens puzzle\"\"\"\n",
+    "    def __init__(self, size, show=False):\n",
+    "        self.size = size\n",
+    "        self.solutions = 0\n",
+    "        self.solve()\n",
+    "\n",
+    "    def solve(self):\n",
+    "        \"\"\"Solve the n queens puzzle and print the number of solutions\"\"\"\n",
+    "        positions = [-1] * self.size\n",
+    "        self.put_queen(positions, 0)\n",
+    "        print(\"Found\", self.solutions, \"solutions.\")\n",
+    "        return self.solutions\n",
+    "\n",
+    "    def put_queen(self, positions, target_row):\n",
+    "        \"\"\"\n",
+    "        Try to place a queen on target_row by checking all N possible cases.\n",
+    "        If a valid place is found the function calls itself trying to place a queen\n",
+    "        on the next row until all N queens are placed on the NxN board.\n",
+    "        \"\"\"\n",
+    "        # Base (stop) case - all N rows are occupied\n",
+    "        if target_row == self.size:\n",
+    "            self.solutions += 1\n",
+    "        else:\n",
+    "            # For all N columns positions try to place a queen\n",
+    "            for column in range(self.size):\n",
+    "                # Reject all invalid positions\n",
+    "                if self.check_place(positions, target_row, column):\n",
+    "                    positions[target_row] = column\n",
+    "                    self.put_queen(positions, target_row + 1)\n",
+    "\n",
+    "    def check_place(self, positions, ocuppied_rows, column):\n",
+    "        \"\"\"\n",
+    "        Check if a given position is under attack from any of\n",
+    "        the previously placed queens (check column and diagonal positions)\n",
+    "        \"\"\"\n",
+    "        for i in range(ocuppied_rows):\n",
+    "            if positions[i] == column or \\\n",
+    "                positions[i] - i == column - ocuppied_rows or \\\n",
+    "                positions[i] + i == column + ocuppied_rows:\n",
+    "\n",
+    "                return False\n",
+    "        return True\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/msca/__init__.py b/msca/__init__.py
new file mode 100644
index 0000000..38af7b2
--- /dev/null
+++ b/msca/__init__.py
@@ -0,0 +1 @@
+from .teste import *
\ No newline at end of file
diff --git a/msca/teste.py b/msca/teste.py
new file mode 100644
index 0000000..36e336f
--- /dev/null
+++ b/msca/teste.py
@@ -0,0 +1,5 @@
+
+
+
+def raf_sum(a,b):
+  return a+b
\ No newline at end of file

From f807b8a1f5bc9223227aa0d08bbe2bd5391124c7 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 13:16:55 -0600
Subject: [PATCH 15/36] new things

---
 .../Challenge 1 - N Queens-checkpoint.ipynb   |  125 -
 ...enge 2 - Face Recognition-checkpoint.ipynb |   55 -
 .../Challenge 3 - ImageNet-checkpoint.ipynb   |   37 -
 challenges/Challenge 1 - Solution.ipynb       |   76 -
 ...ators, and Error Handling-checkpoint.ipynb | 1849 ----
 ...tetime and Saving Objects-checkpoint.ipynb | 1876 ----
 ... and 5 - Numpy and Pandas-checkpoint.ipynb | 7817 -----------------
 msca/__init__.py                              |    5 +-
 8 files changed, 4 insertions(+), 11836 deletions(-)
 delete mode 100644 challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb
 delete mode 100644 challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb
 delete mode 100644 challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb
 delete mode 100644 challenges/Challenge 1 - Solution.ipynb
 delete mode 100644 lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb
 delete mode 100644 lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb
 delete mode 100644 lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb

diff --git a/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb
deleted file mode 100644
index def6c73..0000000
--- a/challenges/.ipynb_checkpoints/Challenge 1 - N Queens-checkpoint.ipynb	
+++ /dev/null
@@ -1,125 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# N-Queens Challenge\n",
-    "\n",
-    "From wikipedia:\n",
-    "    \n",
-    ">The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard so that no two queens threaten each other; thus, a solution requires that no two queens share the same row, column, or diagonal. The eight queens puzzle is an example of the more general n queens problem of placing n non-attacking queens on an n×n chessboard, for which solutions exist for all natural numbers n with the exception of n = 2 and n = 3.\n",
-    "Chess composer Max Bezzel published the eight queens puzzle in 1848. Franz Nauck published the first solutions in 1850. Nauck also extended the puzzle to the n queens problem, with n queens on a chessboard of n×n squares.\n",
-    "    Since then, many mathematicians, including Carl Friedrich Gauss, have worked on both the eight queens puzzle and its generalized n-queens version. In 1874, S. Gunther proposed a method using determinants to find solutions. J.W.L. Glaisher refined Gunther's approach.\n",
-    "    In 1972, Edsger Dijkstra used this problem to illustrate the power of what he called structured programming. He published a highly detailed description of a depth-first backtracking algorithm.2\n",
-    "\n",
-    "## Build a code that calculates the ratio of solutions vs the possible arrangements for N queens in a chess board of NxN size.\n",
-    "\n",
-    "\n",
-    "1. Build an UDF that calculates the total ammount of arragements of N queens\n",
-    "2. Build an UDF that if a Queen can be place on a given position and returns true or false\n",
-    "3. Apply the 2nd UDF for N queens on an NxN board\n",
-    "4. Repeat #3 until it exhausts the possibilities\n",
-    "5. Build an UDF that manages the results in a table? or tuple? build an assesment of this object that returns the compiled results.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "For N = 1 \n",
-      "Found 1 solutions.\n",
-      "1\n",
-      "For N = 2 \n",
-      "Found 0 solutions.\n",
-      "0\n",
-      "For N = 3 \n",
-      "Found 0 solutions.\n",
-      "0\n",
-      "For N = 4 \n",
-      "Found 2 solutions.\n",
-      "2\n",
-      "For N = 5 \n",
-      "Found 10 solutions.\n",
-      "10\n",
-      "For N = 6 \n",
-      "Found 4 solutions.\n",
-      "4\n",
-      "For N = 7 \n",
-      "Found 40 solutions.\n",
-      "40\n",
-      "For N = 8 \n",
-      "Found 92 solutions.\n",
-      "92\n",
-      "For N = 9 \n",
-      "Found 352 solutions.\n",
-      "352\n",
-      "For N = 10 \n",
-      "Found 724 solutions.\n",
-      "724\n",
-      "For N = 11 \n",
-      "Found 2680 solutions.\n",
-      "2680\n",
-      "For N = 12 \n",
-      "Found 14200 solutions.\n",
-      "14200\n",
-      "For N = 13 \n",
-      "Found 73712 solutions.\n",
-      "73712\n",
-      "For N = 14 \n",
-      "Found 365596 solutions.\n",
-      "365596\n",
-      "For N = 15 \n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "for k in range(1,21):\n",
-    "    print(\"For N = {} \".format(k))\n",
-    "    print(NQueens(k).solutions)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb
deleted file mode 100644
index 37f12ea..0000000
--- a/challenges/.ipynb_checkpoints/Challenge 2 - Face Recognition-checkpoint.ipynb	
+++ /dev/null
@@ -1,55 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# N-Queens Challenge\n",
-    "\n",
-    "From wikipedia:\n",
-    "    \n",
-    ">The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard so that no two queens threaten each other; thus, a solution requires that no two queens share the same row, column, or diagonal. The eight queens puzzle is an example of the more general n queens problem of placing n non-attacking queens on an n×n chessboard, for which solutions exist for all natural numbers n with the exception of n = 2 and n = 3.\n",
-    "Chess composer Max Bezzel published the eight queens puzzle in 1848. Franz Nauck published the first solutions in 1850. Nauck also extended the puzzle to the n queens problem, with n queens on a chessboard of n×n squares.\n",
-    "    Since then, many mathematicians, including Carl Friedrich Gauss, have worked on both the eight queens puzzle and its generalized n-queens version. In 1874, S. Gunther proposed a method using determinants to find solutions. J.W.L. Glaisher refined Gunther's approach.\n",
-    "    In 1972, Edsger Dijkstra used this problem to illustrate the power of what he called structured programming. He published a highly detailed description of a depth-first backtracking algorithm.2\n",
-    "\n",
-    "## Build a code that calculates the ratio of solutions vs the possible arrangements for N queens in a chess board of NxN size.\n",
-    "\n",
-    "\n",
-    "1. Build an UDF that calculates the total ammount of arragements of N queens\n",
-    "2. Build an UDF that if a Queen can be place on a given position and returns true or false\n",
-    "3. Apply the 2nd UDF for N queens on an NxN board\n",
-    "4. Repeat #3 until it exhausts the possibilities\n",
-    "5. Build an UDF that manages the results in a table? or tuple? build an assesment of this object that returns the compiled results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb b/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb
deleted file mode 100644
index f5197cd..0000000
--- a/challenges/.ipynb_checkpoints/Challenge 3 - ImageNet-checkpoint.ipynb	
+++ /dev/null
@@ -1,37 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/challenges/Challenge 1 - Solution.ipynb b/challenges/Challenge 1 - Solution.ipynb
deleted file mode 100644
index 73ab1cb..0000000
--- a/challenges/Challenge 1 - Solution.ipynb	
+++ /dev/null
@@ -1,76 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class NQueens:\n",
-    "    \"\"\"Generate all valid solutions for the n queens puzzle\"\"\"\n",
-    "    def __init__(self, size, show=False):\n",
-    "        self.size = size\n",
-    "        self.solutions = 0\n",
-    "        self.solve()\n",
-    "\n",
-    "    def solve(self):\n",
-    "        \"\"\"Solve the n queens puzzle and print the number of solutions\"\"\"\n",
-    "        positions = [-1] * self.size\n",
-    "        self.put_queen(positions, 0)\n",
-    "        print(\"Found\", self.solutions, \"solutions.\")\n",
-    "        return self.solutions\n",
-    "\n",
-    "    def put_queen(self, positions, target_row):\n",
-    "        \"\"\"\n",
-    "        Try to place a queen on target_row by checking all N possible cases.\n",
-    "        If a valid place is found the function calls itself trying to place a queen\n",
-    "        on the next row until all N queens are placed on the NxN board.\n",
-    "        \"\"\"\n",
-    "        # Base (stop) case - all N rows are occupied\n",
-    "        if target_row == self.size:\n",
-    "            self.solutions += 1\n",
-    "        else:\n",
-    "            # For all N columns positions try to place a queen\n",
-    "            for column in range(self.size):\n",
-    "                # Reject all invalid positions\n",
-    "                if self.check_place(positions, target_row, column):\n",
-    "                    positions[target_row] = column\n",
-    "                    self.put_queen(positions, target_row + 1)\n",
-    "\n",
-    "    def check_place(self, positions, ocuppied_rows, column):\n",
-    "        \"\"\"\n",
-    "        Check if a given position is under attack from any of\n",
-    "        the previously placed queens (check column and diagonal positions)\n",
-    "        \"\"\"\n",
-    "        for i in range(ocuppied_rows):\n",
-    "            if positions[i] == column or \\\n",
-    "                positions[i] - i == column - ocuppied_rows or \\\n",
-    "                positions[i] + i == column + ocuppied_rows:\n",
-    "\n",
-    "                return False\n",
-    "        return True\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb
deleted file mode 100644
index 40f91ab..0000000
--- a/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb	
+++ /dev/null
@@ -1,1849 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lecture 2\n",
-    "* user defined functions\n",
-    "* comprehensions\n",
-    "* lambda functions\n",
-    "* generators\n",
-    "* exception handling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### User Defined Functions\n",
-    "* def means we are defining a function\n",
-    "* def function_name(param1, param2)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "def my_function(param1, param2):\n",
-    "   do some stuff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x):\n",
-    "    print(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "function"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(my_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### return will let you store output from a function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x):\n",
-    "    return x+2 # note you can just add on the fly here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "y = my_func(2)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### multiple params"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x,y):\n",
-    "    return x+y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "y = my_func(5,2)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### a bit more complicated"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x,y):\n",
-    "    \n",
-    "    output = None\n",
-    "    z = x+y\n",
-    "    \n",
-    "    if z < 5:\n",
-    "        output = True\n",
-    "    else:\n",
-    "        output = False\n",
-    "        \n",
-    "    return output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "output = my_func(2,1)\n",
-    "output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "output = my_func(2,5)\n",
-    "output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a function that</h3>\n",
-    "<p style=\"color:blue\">- takes as an input a list of integers</p>\n",
-    "<p style=\"color:blue\">- adds together all the tiems less than 10</p>\n",
-    "<p style=\"color:blue\">- returns the final sum</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### note python will assume the positioning of params\n",
-    "* consider the below\n",
-    "* the first param is a string\n",
-    "* the second is a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x,y):\n",
-    "    x = x.lower()\n",
-    "    y = y + 2\n",
-    "    print(x)\n",
-    "    print(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'int' object has no attribute 'lower'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-24-d492ee7d6bfe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"str\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-23-20206ed1ff95>\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'lower'"
-     ]
-    }
-   ],
-   "source": [
-    "#my_func(5,\"str\") # won't work"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "str\n",
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(\"str\", 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we could explicitly use the keyword, even if things are out of order"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "str\n",
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(y = 5, x = \"str\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### but we can't do a keyword followed by a positional argument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-3-f73cfd3c8222>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-f73cfd3c8222>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    my_func(y = 5, \"str\") # won't work\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(y = 5, \"str\") # won't work"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we can use args to have a variable amount of arguments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(*args):\n",
-    "    for arg in args:\n",
-    "        print(arg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(1,2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "5\n",
-      "6\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(1,2,5,6,2,3,4,5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a function that</h3>\n",
-    "<p style=\"color:blue\">- takes an inifinte amount of params</p>\n",
-    "<p style=\"color:blue\">- puts them all in a list</p>\n",
-    "<p style=\"color:blue\">- returns the list</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we can use kwargs to have keyword arguments that are optional\n",
-    "* arguments are put into a dictionary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(**kwargs):\n",
-    "    print(type(kwargs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'dict'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(x = 2, y = 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(**kwargs):\n",
-    "    print(kwargs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'x': 2, 'y': 3}\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(x = 2, y = 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x,y, **kwargs):\n",
-    "    val = x + y\n",
-    "    if \"z\" in kwargs:\n",
-    "        # remember, we can't access the value simply using\n",
-    "        # z, we have to grab it form the kwargs dictionary\n",
-    "        val+=kwargs[\"z\"]\n",
-    "    print(val)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(5,7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "my_func() takes 2 positional arguments but 3 were given",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-8751ad374ad8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: my_func() takes 2 positional arguments but 3 were given"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(5,2,4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(5,2,z=4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a function that takes two params</h3>\n",
-    "<p style=\"color:blue\">- divids the first param by the second</p>\n",
-    "<p style=\"color:blue\">- searches for a third named \"third\"</p>\n",
-    "<p style=\"color:blue\">- if it exists add that to the division of the first two params</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we can also set defaults for params\n",
-    "* the default can be overriden by passing in a value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x, y = 2):\n",
-    "    print(x + y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(2,5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(x = 2, y = 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(x = 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### use of stars\n",
-    "* the star unpacks the tuple, saying this is a list of arguments, vs treat this list as one argument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(*args):\n",
-    "    for i in args:\n",
-    "        print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func([1,2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(*[1,2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lambda Functions\n",
-    "* annonymous\n",
-    "* defined on the fly with one line\n",
-    "* multiple params but one expression\n",
-    "* commonly used in mapping, applying and filtering\n",
-    "* lambda params: expression\n",
-    "* https://docs.python.org/3/reference/expressions.html#lambda"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 4, 5]"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_lambda = lambda x: x + 2\n",
-    "\n",
-    "a = [1,2,3]\n",
-    "\n",
-    "list(map(my_lambda, a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### but we don't even need to store the function in my_lambda"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 4, 5]"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(map(lambda x: x+2, a))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[4, 5]"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [2,3,4,5]\n",
-    "\n",
-    "list(filter(lambda x: x > 3, a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">write a lambda that</h3>\n",
-    "<p style=\"color:blue\">- maps to a list of strings and</p>\n",
-    "<p style=\"color:blue\">- replaces all the letter o with e</p>\n",
-    "<p style=\"color:blue\">- converts to upper case</p>\n",
-    "<p style=\"color:blue\">- .replace(item_replace, replace_with)</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## list comprehension\n",
-    "* memory efficient way to run a for loop and put the results in a list or iterable\n",
-    "* on the fly apply functions to the elements of a list\n",
-    "* similar to filtering and mapping\n",
-    "* https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2,3,4,5]\n",
-    "b = []\n",
-    "for i in a:\n",
-    "    b.append(i)\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = [i for i in a]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('a', 'b', 'c')"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"A\", \"B\", \"C\"]\n",
-    "b = tuple(x.lower() for x in a)\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{(1, 'a'), (2, 'b'), (3, 'c')}"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2,3]\n",
-    "b = [\"a\",\"b\",\"c\"]\n",
-    "\n",
-    "my_dict = {(x[0],x[1]) for x in zip(a,b)}\n",
-    "my_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[4, 5]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = [i for i in a if i > 3]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-1, -2, -3, -4, -5]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = [i*-1 for i in a]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c']"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"A\", \"B\", \"C\"]\n",
-    "b = [x.lower() for x in a]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c']"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_func = lambda x: x.lower()\n",
-    "a = [\"A\", \"B\", \"C\"]\n",
-    "b = [my_func(x) for x in a]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">write a lambda that</h3>\n",
-    "<p style=\"color:blue\">- use comprehension to apply to a list of numbers</p>\n",
-    "<p style=\"color:blue\">- the lambda should return the remainder of the number divided by 2</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "51%2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "50%2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generators\n",
-    "* special function that returns a lazy iterator.\n",
-    "* do not store contents in memory.\n",
-    "* Instead, the state of the function is remembered.\n",
-    "* Keep track of where we are in the iterator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst = [num for num in range(5)]\n",
-    "gen = (num for num in range(5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fcd988c25d0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in gen:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## use next() to grab the next item in the generator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gen = (num for num in range(2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "StopIteration",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### can use yield instead of return, to return the next item of the generator.  yield automatically tells python this is a generator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def infinite_sequence():\n",
-    "    for i in [num for num in range(5)]:\n",
-    "        yield i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gen = infinite_sequence()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object infinite_sequence at 0x7fcd988c2350>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in gen:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen = infinite_sequence()\n",
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### very memory efficient"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "81528064"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nums_squared_lc = [i * 2 for i in range(10000000)]\n",
-    "sys.getsizeof(nums_squared_lc)#/ 1e+9"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "128"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# returns bytes\n",
-    "nums_squared_lc = (i * 2 for i in range(10000000))\n",
-    "sys.getsizeof(nums_squared_lc)#/ 1e+9"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### could use generator for dealing with text files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "csv = [\n",
-    "    \"permalink,company,numEmps,category,city,state,fundedDate,raisedAmt,raisedCurrency,round\",\n",
-    "    \"digg,Digg,60,web,San Francisco,CA,1-Dec-06,8500000,USD,b\",\n",
-    "    \"digg,Digg,60,web,San Francisco,CA,1-Oct-05,2800000,USD,a\",\n",
-    "    \"facebook,Facebook,450,web,Palo Alto,CA,1-Sep-04,500000,USD,angel\",\n",
-    "    \"facebook,Facebook,450,web,Palo Alto,CA,1-May-05,12700000,USD,a\",\n",
-    "    \"photobucket,Photobucket,60,web,Palo Alto,CA,1-Mar-05,3000000,USD,a\"\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "file_name = \"techcrunch.csv\"\n",
-    "\n",
-    "# declares a generator\n",
-    "lines = (line for line in open(file_name))\n",
-    "\n",
-    "# split the lines on the comma\n",
-    "list_line = (s.rstrip().split(\",\") for s in lines)\n",
-    "\n",
-    "# gives us the next line\n",
-    "cols = next(list_line)\n",
-    "\n",
-    "# this will be helpful for filtering, keys are column names\n",
-    "# \n",
-    "company_dicts = (dict(zip(cols, data)) for data in list_line)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fcd68774450>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lines = (line for line in csv)\n",
-    "lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fcd687745d0>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_line = (s.rstrip().split(\",\") for s in lines)\n",
-    "list_line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['permalink',\n",
-       " 'company',\n",
-       " 'numEmps',\n",
-       " 'category',\n",
-       " 'city',\n",
-       " 'state',\n",
-       " 'fundedDate',\n",
-       " 'raisedAmt',\n",
-       " 'raisedCurrency',\n",
-       " 'round']"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(list_line)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['digg',\n",
-       " 'Digg',\n",
-       " '60',\n",
-       " 'web',\n",
-       " 'San Francisco',\n",
-       " 'CA',\n",
-       " '1-Dec-06',\n",
-       " '8500000',\n",
-       " 'USD',\n",
-       " 'b']"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(list_line)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exception Handling and Errors\n",
-    "* https://docs.python.org/3/tutorial/errors.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### syntax errors\n",
-    "* missing parenth or bracket maybe, some formatting issue"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-76-35c08edd0df2>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-76-35c08edd0df2>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print((2)))\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "print((2)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### exception errors:\n",
-    "* you've tried to do something that can't be done"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-78-fad870a50e27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "print(5/0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "can only concatenate str (not \"int\") to str",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-79-89309f5042e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"string\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"string\" + 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### assert\n",
-    "* logical test\n",
-    "* if the test isn't met we raise an assertion error\n",
-    "* assert logical, error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "The length of x is not greater than 3",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-36-523a2a17546a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The length of x is not greater than 3\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m: The length of x is not greater than 3"
-     ]
-    }
-   ],
-   "source": [
-    "x = [2,3]\n",
-    "\n",
-    "assert len(x) > 3, \"The length of x is not greater than 3\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "x is empty",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-37-9bd7d8a13151>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"x is empty\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m: x is empty"
-     ]
-    }
-   ],
-   "source": [
-    "x = []\n",
-    "\n",
-    "assert len(x) != 0, \"x is empty\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### raise\n",
-    "* we could also raise an exception"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "Exception",
-     "evalue": "x should not exceed 5.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-f2f500004ca1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x should not exceed 5.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mException\u001b[0m: x should not exceed 5."
-     ]
-    }
-   ],
-   "source": [
-    "x = 10\n",
-    "if x > 5:\n",
-    "    raise Exception('x should not exceed 5.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### try and except\n",
-    "* tell python to try a block of code, if there is an error, revert to except"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found an error.  Item String isn't a number or float\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,2,3,4,\"String\"]\n",
-    "\n",
-    "for i in a:\n",
-    "    \n",
-    "    try:\n",
-    "        i = i+2\n",
-    "    except:\n",
-    "        print(\"Found an error.  Item {} isn't a number or float\".format(i))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### plenty of built in exception/errors classese in python\n",
-    "* for instance, IndexError\n",
-    "* NameError\n",
-    "* https://docs.python.org/3/library/exceptions.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-83-070992024e9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "a = [2]\n",
-    "a[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'z' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-84-c01ac3f50fdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'z' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "print(z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### excepting the error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "An error occurred\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1, 2, 3] \n",
-    "try:  \n",
-    "    print(a[1]) \n",
-    "  \n",
-    "    # Throws error since there are only 3 elements in array \n",
-    "    print(a[3])\n",
-    "    \n",
-    "except IndexError: \n",
-    "    print(\"An error occurred\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### raise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-97-b6c41dcfb79b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"string\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0;31m# raises the error that was caught\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
-     ]
-    }
-   ],
-   "source": [
-    "a = 5\n",
-    "\n",
-    "try:\n",
-    "    print(a + \"string\")\n",
-    "except TypeError:\n",
-    "    raise # raises the error that was caught"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb
deleted file mode 100644
index 7fdd6a9..0000000
--- a/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb	
+++ /dev/null
@@ -1,1876 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### OOO\n",
-    "* simple OOO example\n",
-    "* classes - template of objects\n",
-    "* objects - member or instance of class\n",
-    "* classes can have\n",
-    "    * characteristics/attribuets\n",
-    "    * methods"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simple framework for a dog\n",
-    "class dog:\n",
-    "    \n",
-    "    # these are params here, things we set when\n",
-    "    # we initialize our instance of our class\n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'blue'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sheperd'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog.breed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__module__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " '__weakref__',\n",
-       " 'breed',\n",
-       " 'color',\n",
-       " 'name']"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(my_dog)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "__main__.dog"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(my_dog)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simple framework for a dog\n",
-    "class dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        \n",
-    "    def speak(self):\n",
-    "        xxx = self.color\n",
-    "        print(\"My color is \" + xxx)\n",
-    "        \n",
-    "    def change_color(self, carrot):\n",
-    "        self.color = carrot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<bound method dog.speak of <__main__.dog object at 0x000001F1CE85A9C8>>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog.speak"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "My color is blue\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_dog.speak()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'blue'"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'red'"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog.change_color(\"red\")\n",
-    "my_dog.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simple framework for a dog\n",
-    "class dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name, distance=0):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance += distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")\n",
-    "my_dog.distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog.walk(5)\n",
-    "my_dog.distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog.walk(12)\n",
-    "my_dog.distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "35"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dog.walk(18)\n",
-    "my_dog.distance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inheritence\n",
-    "* Inherit properties and functions from other objects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance += distance\n",
-    "        \n",
-    "class Retreiver(Dog):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'blue'"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__module__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " '__weakref__',\n",
-       " 'breed',\n",
-       " 'color',\n",
-       " 'distance',\n",
-       " 'name',\n",
-       " 'walk']"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance += distance\n",
-    "        \n",
-    "class Retreiver(Dog):\n",
-    "\n",
-    "    def speak(self):\n",
-    "        print(\"My name is {}\".format(self.name))\n",
-    "        \n",
-    "    def blink182(self):\n",
-    "        print(\"My age is {}\".format(self.age))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "__init__() got an unexpected keyword argument 'color'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-62-6f94a433d382>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mRetreiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"blue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"retreiver\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Rex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblink182\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'color'"
-     ]
-    }
-   ],
-   "source": [
-    "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
-    "r.blink182()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Dog' object has no attribute 'speak'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-56-800ba7303882>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mDog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"blue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"retreiver\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Rex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspeak\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m: 'Dog' object has no attribute 'speak'"
-     ]
-    }
-   ],
-   "source": [
-    "r =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
-    "r.speak()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Class Methods\n",
-    "* takes cls as the first input\n",
-    "* bound to the class, not the instance of the object\n",
-    "* can access class variables though not instance variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Dog:\n",
-    "    \n",
-    "    class_atr = 0\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def speak(cls, speak):\n",
-    "        print(speak)\n",
-    "        \n",
-    "    @classmethod\n",
-    "    def add(cls,x,y):\n",
-    "        return x+y\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def printer(cls):\n",
-    "        print(cls.class_atr)\n",
-    "        \n",
-    "    @classmethod\n",
-    "    def class_add(cls):\n",
-    "        cls.class_atr = cls.class_atr + 10\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        class_atr=+1\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance = Dog.add(self.distance, distance)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rex =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n"
-     ]
-    }
-   ],
-   "source": [
-    "rex.printer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "30\n"
-     ]
-    }
-   ],
-   "source": [
-    "rex.class_add()\n",
-    "rex.printer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "say something\n"
-     ]
-    }
-   ],
-   "source": [
-    "r.speak(\"say something\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "say something\n"
-     ]
-    }
-   ],
-   "source": [
-    "Dog.speak(\"say something\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r.walk(5)\n",
-    "r.walk(10)\n",
-    "r.distance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Static Method\n",
-    "* neither bound to the instance or class\n",
-    "* no access to the instance or class attribuets\n",
-    "* could be a standalone function but we want to keep it in the class\n",
-    "* for instance, distance measures inside a KNN class.  then we can reference the functions when we have a fit method\n",
-    "* aren't super useful due to limited access of information\n",
-    "* utility type funciton"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Distance:\n",
-    "    \n",
-    "    @staticmethod\n",
-    "    def add(a,b):\n",
-    "        print(a+b)\n",
-    "    \n",
-    "    @staticmethod\n",
-    "    def sub(a,b):\n",
-    "        print(a-b)\n",
-    "    \n",
-    "    def __init__(self,x,y):\n",
-    "        self.x = x\n",
-    "        self.y = y\n",
-    "        \n",
-    "    def operate(self, how):\n",
-    "        if how == \"add\":\n",
-    "            Distance.add(self.x, self.y)\n",
-    "        else:\n",
-    "            Distance.sub(self.x, self.y)\n",
-    "        \n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = Distance(10,5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15\n"
-     ]
-    }
-   ],
-   "source": [
-    "d.operate(\"add\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "d.operate(\"sub\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Characteristics/Properties/Attributes\n",
-    "* accessed using dot notation\n",
-    "* methods are used using dot notation and parentheses ()\n",
-    "* we can now read the sklearn documentation that much better\n",
-    "* https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
-    "* since sklearn documentation is consistent, we can easily look at any if the page and disect how the data structure works\n",
-    "    * what methods we can use\n",
-    "    * what characteristics we can access\n",
-    "    * what params we can pass\n",
-    "* https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### DateTimes in Python\n",
-    "* https://docs.python.org/3/library/datetime.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "from datetime import date"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get Todays Date"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24\n"
-     ]
-    }
-   ],
-   "source": [
-    "today = date.today()\n",
-    "print(today)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "today.day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "today.month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2019"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "today.year"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Weekdays start at monday and index at 0\n",
-    "* 0:Monday\n",
-    "* 1:Tuesday\n",
-    "* 2:Wednesday\n",
-    "* 3:Thursday\n",
-    "* 4:Friday\n",
-    "* 5:Saturday\n",
-    "* 6:Sunday"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "today.weekday()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__radd__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__rsub__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__sub__',\n",
-       " '__subclasshook__',\n",
-       " 'ctime',\n",
-       " 'day',\n",
-       " 'fromisoformat',\n",
-       " 'fromordinal',\n",
-       " 'fromtimestamp',\n",
-       " 'isocalendar',\n",
-       " 'isoformat',\n",
-       " 'isoweekday',\n",
-       " 'max',\n",
-       " 'min',\n",
-       " 'month',\n",
-       " 'replace',\n",
-       " 'resolution',\n",
-       " 'strftime',\n",
-       " 'timetuple',\n",
-       " 'today',\n",
-       " 'toordinal',\n",
-       " 'weekday',\n",
-       " 'year']"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(today)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get Time and Date Now"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:06:33.330468\n"
-     ]
-    }
-   ],
-   "source": [
-    "now = datetime.now()\n",
-    "print(now)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__radd__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__rsub__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__sub__',\n",
-       " '__subclasshook__',\n",
-       " 'astimezone',\n",
-       " 'combine',\n",
-       " 'ctime',\n",
-       " 'date',\n",
-       " 'day',\n",
-       " 'dst',\n",
-       " 'fold',\n",
-       " 'fromisoformat',\n",
-       " 'fromordinal',\n",
-       " 'fromtimestamp',\n",
-       " 'hour',\n",
-       " 'isocalendar',\n",
-       " 'isoformat',\n",
-       " 'isoweekday',\n",
-       " 'max',\n",
-       " 'microsecond',\n",
-       " 'min',\n",
-       " 'minute',\n",
-       " 'month',\n",
-       " 'now',\n",
-       " 'replace',\n",
-       " 'resolution',\n",
-       " 'second',\n",
-       " 'strftime',\n",
-       " 'strptime',\n",
-       " 'time',\n",
-       " 'timestamp',\n",
-       " 'timetuple',\n",
-       " 'timetz',\n",
-       " 'today',\n",
-       " 'toordinal',\n",
-       " 'tzinfo',\n",
-       " 'tzname',\n",
-       " 'utcfromtimestamp',\n",
-       " 'utcnow',\n",
-       " 'utcoffset',\n",
-       " 'utctimetuple',\n",
-       " 'weekday',\n",
-       " 'year']"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(now)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "33"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.second"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.minute"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2019"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.weekday()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a variable of the current date</h3>\n",
-    "<p style=\"color:blue\">- make a list with the weekdays</p>\n",
-    "<p style=\"color:blue\">- convert the weekday() integer to the actual date name</p>\n",
-    "<p style=\"color:blue\">- print the below, without hard coding it</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Today is a Friday\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Today is a Friday\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Format Datetime output using strftime\n",
-    "* (%y/%Y – Year)\n",
-    "* (%a/%A- weekday)\n",
-    "* (%b/%B- month)\n",
-    "* (%d - day of month)\n",
-    "* %c- indicates the local date and time\n",
-    "* 12 hours time is declared [print now.strftime(\"%I:%M:%S %P) ]\n",
-    "* 24 hours time is declared [print now.strftime(\"%H:%M\")] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "now = datetime.now()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019\n",
-      "19\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%Y\"))\n",
-    "print(now.strftime(\"%y\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sunday\n",
-      "Sun\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%A\"))\n",
-    "print(now.strftime(\"%a\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "November\n",
-      "Nov\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%B\"))\n",
-    "print(now.strftime(\"%b\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11/24/19\n",
-      "24\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%D\"))\n",
-    "print(now.strftime(\"%d\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sun Nov 24 12:10:24 2019\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%c\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we can string these together"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-November-24\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%Y-%B-%d\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019,November,24\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%Y,%B,%d\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "19-Nov-24\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%y-%b-%d\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12:10:24\n"
-     ]
-    }
-   ],
-   "source": [
-    "# I is the hour, # M is minutes, #S is seconds\n",
-    "# army time\n",
-    "print(now.strftime(\"%I:%M:%S\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12:10\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 24 hour clock\n",
-    "print(now.strftime(\"%H:%M\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Use timedelta to add and subtract dates\n",
-    "* does not support years, because the duration of a year depends on which year (for example, leap years have Feb 29)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import timedelta"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:19:01.737490\n",
-      "2019-12-04 17:34:01.737781\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(datetime.now())\n",
-    "print(datetime.now() + timedelta(days=10, hours = 5, minutes = 15))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:19:09.017928\n",
-      "2019-11-14 07:04:09.018214\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(datetime.now())\n",
-    "print(datetime.now() - timedelta(days=10, hours = 5, minutes = 15))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:19:44.676823\n",
-      "2019-12-08 12:19:44.677028\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(datetime.now())\n",
-    "print(datetime.now() + timedelta(weeks=2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:19:54.454511\n",
-      "2019-11-10 12:19:54.454799\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(datetime.now())\n",
-    "print(datetime.now() - timedelta(weeks=2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.datetime"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# returns a datetime so we can extract info\n",
-    "x = datetime.now() - timedelta(weeks=2)\n",
-    "type(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2019"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.weekday()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a variable with the current date</h3>\n",
-    "<p style=\"color:blue\">- add/subtract a year</p>\n",
-    "<p style=\"color:blue\">- add subtract a year and 2 weeks</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Saving Objects in Python\n",
-    "* serialization, marshaling, and flattening. \n",
-    "* same—to save an object to a file for later retrieval\n",
-    "* accomplishes this by writing the object as one long stream of bytes. \n",
-    "* pickling also used to save models in sklearn\n",
-    "* https://docs.python.org/3/library/pickle.html\n",
-    "* https://scikit-learn.org/stable/modules/model_persistence.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = [\"a\", \"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file = open('test_pickle.pkl', 'wb') \n",
-    "pickle.dump(test, file)\n",
-    "file.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# delete from memory\n",
-    "del test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file = open('test_pickle.pkl', 'rb') \n",
-    "test = pickle.load(file)\n",
-    "file.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b']"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### using with"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "item = \"some string I made\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"test_pickle.pkl\", \"wb\") as file:\n",
-    "    pickle.dump(item, file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#del item\n",
-    "#item"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"test_pickle.pkl\", \"rb\") as file:\n",
-    "    item = pickle.load(file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'some string I made'"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "item"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a python object</h3>\n",
-    "<p style=\"color:blue\">- save the object</p>\n",
-    "<p style=\"color:blue\">- delete the object from memory</p>\n",
-    "<p style=\"color:blue\">- reload it</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb
deleted file mode 100644
index 8966296..0000000
--- a/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb	
+++ /dev/null
@@ -1,7817 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Numpy Arrays\n",
-    "* multi dimensional arrays or data\n",
-    "* can be 1d, 2d, 3d\n",
-    "* very computationally efficient to perform linear algebra operations over\n",
-    "* elementwise operations\n",
-    "* row and column wise calculations\n",
-    "* https://docs.scipy.org/doc/numpy-1.11.0/numpy-user-1.11.0.pdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Arrays vs. Matrix Class\n",
-    "* \"Short Answer: Use Arrays.\"\n",
-    "* They are the standard vector/matrix/tensor type of numpy. Many numpy functions return arrays, not matrices.\n",
-    "* The array class is intended to be a general-purposen-dimensional array for many kinds of numerical computing, whilematrixis intended to facilitate linear algebracomputations specifically."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1-D arrays\n",
-    "* note his only has one demension\n",
-    "* we could use a method called reshape to change this (we will get into this more later on)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3,4,5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5,)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5, 1)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = np.reshape(a, (5,1))\n",
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[1,0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### indexing and slicing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 3])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in a:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### looping\n",
-    "* numpy loops through the left most axis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,4]\n",
-    "])\n",
-    "\n",
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 2 3]\n",
-      "[2 3 4]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# since we have 2 items in the first axis we will end up with 2 items in our iteration\n",
-    "for i in array:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,3,4,4],\n",
-    "    [2,3,4,4]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,7,4],\n",
-    "    [4,6,9,4],\n",
-    "    [2,3,4,4]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3, 4)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# depth 2\n",
-    "# rows 3\n",
-    "# columns 4\n",
-    "tensor = np.array([array, array1])\n",
-    "tensor.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* remember numpy will iterate left most index\n",
-    "* in this 3-d cube, it's iterating the dpeth\n",
-    "* so we end up printing our 2 initial matrices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3 4]\n",
-      " [2 3 4 4]\n",
-      " [2 3 4 4]]\n",
-      "[[5 6 7 4]\n",
-      " [4 6 9 4]\n",
-      " [2 3 4 4]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in tensor:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### performing matrix operations\n",
-    "* axis 0 is the y axis\n",
-    "* the up and down axis\n",
-    "* the number of rows\n",
-    "* so any operation done on axis 0 is going to be columns\n",
-    "* done up and down\n",
-    "* axis 1 is the length of our array\n",
-    "* any summing or anything is done across our dataframe\n",
-    "* doing some operation to our rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,3,4,4],\n",
-    "    [2,3,4,4]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 4)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3, 4],\n",
-       "       [2, 3, 4, 4],\n",
-       "       [2, 3, 4, 4]])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 5,  8, 11, 12])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10, 13, 13])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('int32')"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2., 3., 4., 4.])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.median(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* the other option would be to loop which is far more costly of an operation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "13\n",
-      "13\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array:\n",
-    "    print(sum(i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "8\n",
-      "11\n",
-      "12\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array.T:\n",
-    "    print(sum(i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3, 4],\n",
-       "       [2, 3, 4, 4],\n",
-       "       [2, 3, 4, 4]])"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### tranpose, turn rows into columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 2],\n",
-       "       [2, 3, 3],\n",
-       "       [3, 4, 4],\n",
-       "       [4, 4, 4]])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make a 2-d array in numpy:</h3>\n",
-    "<p style=\"color:blue\">- find the row wise mean</p>\n",
-    "<p style=\"color:blue\">- find the column wise mean</p>\n",
-    "<p style=\"color:blue\">- do the same for the transpose of the array you've made</p>\n",
-    "<p style=\"color:blue\">- check if matrix square</p>\n",
-    "<p style=\"color:blue\">- multiple the matrix by transpose</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[3. 3. 3. 3.]\n",
-      "[4. 4. 4.]\n",
-      "[4. 4. 4.]\n",
-      "[3. 3. 3. 3.]\n",
-      "Not a square!\n",
-      "[12. 12. 12.]\n",
-      "[12. 12. 12.]\n",
-      "Square as me!\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = np.ones([3,4])\n",
-    "\n",
-    "\n",
-    "print(a.sum(0))\n",
-    "print(a.sum(1))\n",
-    "\n",
-    "b = a.T\n",
-    "\n",
-    "print(b.sum(0))\n",
-    "print(b.sum(1))\n",
-    "\n",
-    "\n",
-    "if(a.shape[0]!=a.shape[1]):\n",
-    "    print(\"Not a square!\")\n",
-    "\n",
-    "\n",
-    "c = np.dot(a,b)\n",
-    "\n",
-    "print(c.sum(0))\n",
-    "print(c.sum(1))\n",
-    "\n",
-    "\n",
-    "if(c.shape[0]!=c.shape[1]):\n",
-    "    print(\"Not a square!\")\n",
-    "else:\n",
-    "    print(\"Square as me!\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### other useful metrics\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.quantile.html\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1101, 2020, 3212],\n",
-       "       [2212, 3112, 4121]])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([555.5, 546. , 454.5])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1656.5, 2566. , 3666.5])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1101, 2020, 3212])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2212, 3112, 4121])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.max(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1656.5, 2566. , 3666.5])"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.quantile(array,.5, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1106.555, 2025.46 , 3216.545])"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.percentile(array,.5, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### reshape\n",
-    "* change the shape of your data\n",
-    "* adding indices\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4])"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# this is indexed by one element\n",
-    "array = np.array([1,2,3,4])\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4,)"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4, 1)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.reshape(array, (4,1))\n",
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array[0][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we've now added two dimension using reshape\n",
-    "array1 = np.reshape(array,(2,2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2],\n",
-       "       [3, 4]])"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([1,2,3,4,5,6,7,8])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4, 5, 6, 7, 8])"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array1 = np.reshape(array, (2,2,2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[1, 2],\n",
-       "        [3, 4]],\n",
-       "\n",
-       "       [[5, 6],\n",
-       "        [7, 8]]])"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2, 2)"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2]\n",
-      " [3 4]] \n",
-      "\n",
-      "[[5 6]\n",
-      " [7 8]] \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array1:\n",
-    "    print(i, \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### norm of a vector\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.norm.html\n",
-    "* defaults to the l2 norm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.7416573867739413"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([1,2,3])\n",
-    "np.linalg.norm(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.7416573867739413"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sqrt(14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### matrix multiplication/dot product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2],\n",
-    "    [2,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6],\n",
-    "    [4,6]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# shortcut\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.matmul(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.dot(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### remember the dimension constraints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,2],\n",
-    "    [4,6,1]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3) (2, 3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(array.shape, array1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "The dimensions aren't aligned",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-57-fe46837ad8c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0marray1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0marray1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
-     ]
-    }
-   ],
-   "source": [
-    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
-    "\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3) (3, 6)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[19, 27, 10, 17, 14, 21],\n",
-       "       [28, 39, 13, 25, 21, 28]])"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5],\n",
-    "    [2,3,2,2,1,3]\n",
-    "])\n",
-    "\n",
-    "print(array.shape, array1.shape)\n",
-    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
-    "\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
-    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
-    "<p style=\"color:blue\">- tests to make sure they can be multipled using assert</p>\n",
-    "<p style=\"color:blue\">- return the dot product of the two matrices</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[19 27 10 17 14 21]\n",
-      " [28 39 13 25 21 28]]\n"
-     ]
-    },
-    {
-     "ename": "AssertionError",
-     "evalue": "The dimensions aren't aligned",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0marray2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36mrafdot\u001b[1;34m(m1, m2)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0mm2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m array = np.array([\n",
-      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
-     ]
-    }
-   ],
-   "source": [
-    "def rafdot(m1, m2):\n",
-    "    assert m1.shape[1] == m2.shape[0], \"The dimensions aren't aligned\"\n",
-    "    return m1@m2\n",
-    "\n",
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5],\n",
-    "    [2,3,2,2,1,3]\n",
-    "])\n",
-    "\n",
-    "array2 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5]])\n",
-    "\n",
-    "c = rafdot(array,array1)\n",
-    "print(c)\n",
-    "\n",
-    "c = rafdot(array,array2)\n",
-    "print(c)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### broadcasting\n",
-    "* expand the dimension to add to a matrix\n",
-    "* https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([1,1])\n",
-    "array1 = np.reshape(array1, (2,1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [2, 3, 3]])"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1],\n",
-       "       [1]])"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 3, 4],\n",
-       "       [3, 4, 4]])"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.add(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [1, 2, 2]])"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.subtract(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 3, 4],\n",
-       "       [3, 4, 4]])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [1, 2, 2]])"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array - array1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### elementwise squaring\n",
-    "* this is the elementwise operations arrays that make arrays so powerful"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1,  32, 243],\n",
-       "       [ 32, 243, 243]])"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array**5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1,  32, 243],\n",
-       "       [ 32, 243, 243]])"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.power(array, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
-    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
-    "<p style=\"color:blue\">- iterates the rows and columns and squares each element</p>\n",
-    "<p style=\"color:blue\">- puts the squared items in a list</p>\n",
-    "<p style=\"color:blue\">- returns the list</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array([[1, 4, 9],\n",
-       "        [4, 9, 9]]), array([[25, 36,  4, 25,  9,  4],\n",
-       "        [16, 36,  1,  9, 16, 25],\n",
-       "        [ 4,  9,  4,  4,  1,  9]])]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def raf_square(m1,m2):\n",
-    "    flist = []\n",
-    "    for k in m1, m2:\n",
-    "        c = k*k\n",
-    "        flist.append(c)\n",
-    "    return flist\n",
-    "\n",
-    "raf_square(array,array1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### argmax and min\n",
-    "* returns the index of the max/min value\n",
-    "* In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([1,2,3,4,5])\n",
-    "np.argmax(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmin(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3],\n",
-    "    [3,10,5],\n",
-    "    [15,8,9]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 1, 2])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(array, axis = 0) # column level"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 1, 0])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(array, axis = 1) # row level"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### let's say these are distances\n",
-    "* each row is an item\n",
-    "* and each column is the distance to a cluster\n",
-    "array = np.array(["
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3],\n",
-    "    [3,10,5],\n",
-    "    [15,8,9]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 1], dtype=int64)"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# this could then give us the index of the min distance\n",
-    "np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "items = [\"item_A\", \"item_B\", \"item_C\"]\n",
-    "clusters = [\"A\", \"B\", \"C\"]\n",
-    "closest_cluster = np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['A', 'A', 'B']"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "closest_cluster_labels = [clusters[idx] for idx in closest_cluster]\n",
-    "closest_cluster_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('item_A', 'A'), ('item_B', 'A'), ('item_C', 'B')]"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(zip(items, closest_cluster_labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['item_A', 'A'],\n",
-       "       ['item_B', 'A'],\n",
-       "       ['item_C', 'B']], dtype='<U6')"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    items,\n",
-    "    closest_cluster_labels\n",
-    "]).T\n",
-    "\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['item_A', 'B'],\n",
-       "       ['item_B', 'B'],\n",
-       "       ['item_C', 'B']], dtype='<U6')"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    items,\n",
-    "    [clusters[idx] for idx in np.argmin(array, axis = 1)]\n",
-    "]).T\n",
-    "\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(np.array([1,1,2,2,5,5,5]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### argsort"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([1,3,6,0,51,12])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sorted_args = np.argsort(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 3, 6, 12, 51]"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[array[idx] for idx in sorted_args]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3],\n",
-    "    [3,10,5],\n",
-    "    [15,8,9]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "items = [\"item_A\", \"item_B\", \"item_C\"]\n",
-    "clusters = [\"A\", \"B\", \"C\"]\n",
-    "closest_cluster = np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1,  6,  3],\n",
-       "       [ 3, 10,  5],\n",
-       "       [15,  8,  9]])"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 2, 1],\n",
-       "       [0, 2, 1],\n",
-       "       [1, 2, 0]], dtype=int64)"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argsort(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def myfunc(x):\n",
-    "    items = [\"item_A\", \"item_B\", \"item_C\"]\n",
-    "    return items[x]\n",
-    "\n",
-    "vectorized_func = np.vectorize(myfunc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-84-86bed837003f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvectorized_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2089\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2091\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2092\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2093\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[1;34m(self, func, args)\u001b[0m\n\u001b[0;32m   2165\u001b[0m                       for a in args]\n\u001b[0;32m   2166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2167\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2169\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-83-df07eceacbb6>\u001b[0m in \u001b[0;36mmyfunc\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmyfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"item_A\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_B\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_C\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mvectorized_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvectorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmyfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "vectorized_func(array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distance Measures\n",
-    "* used in many ML algorithms\n",
-    "* rec systems, kmeans, knn, decision trees\n",
-    "* https://docs.scipy.org/doc/scipy/reference/spatial.distance.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### euclidean distance\n",
-    "* magnitude makes a difference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3])\n",
-    "b = np.array([4,2,1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.605551275463989"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sqrt(sum((a-b)**2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### cosine similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6415330278717848"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.dot(a, b)/(np.linalg.norm(a)*np.linalg.norm(b))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### jaccard coefficient"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [\"the\", \"old\", \"man\", \"by\", \"the\", \"sea\"]\n",
-    "b = [\"the\", \"old\", \"man\", \"by\", \"the\", \"chair\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def jaccard_similarity(a, b):\n",
-    "    return len(set(a).intersection(set(b))) / len(set(a).union(set(b)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6666666666666666"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "jaccard_similarity(a,b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### distance matrix\n",
-    "* pdist returns a reduced distance matrix, since they are symetrical\n",
-    "* squareform makes this a dense form"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.spatial.distance import pdist, squareform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,4,2,1],\n",
-    "    [2,3,4,5]\n",
-    "    \n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.        , 3.87298335, 2.        ],\n",
-       "       [3.87298335, 0.        , 4.58257569],\n",
-       "       [2.        , 4.58257569, 0.        ]])"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squareform(pdist(array, \"euclidean\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.1 0.9]\n",
-      "[0. 1.]\n",
-      "[[0. 1.]\n",
-      " [1. 1.]]\n",
-      "(array([0.8, 0.1]), array([], dtype=float64), 2, array([1.61803399, 0.61803399]))\n",
-      "y = 0.09999999999999992x + 0.8\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
-      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
-      "  \n",
-      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
-      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "xdata = np.array([0.0,1.0])\n",
-    "ydata = np.array([0.1,0.9])\n",
-    "\n",
-    "data_pred = xdata.T * xdata.T\n",
-    "\n",
-    "A = np.vstack([xdata,np.ones(len(xdata))]).T\n",
-    "\n",
-    "\n",
-    "print(ydata)\n",
-    "print(xdata)\n",
-    "print(A)\n",
-    "\n",
-    "\n",
-    "print (np.linalg.lstsq(A, ydata))\n",
-    "\n",
-    "# the model has the following face y = a + bx\n",
-    "b, a = np.linalg.lstsq(A, ydata)[0]\n",
-    "print(\"y = \"+str(a)+'x + '+str(b))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#find if a number is prime\n",
-    "#find if it is a prime of goldbach and the primes that build it\n",
-    "\n",
-    "\n",
-    "def isprime(number):\n",
-    "    if(number%2):\n",
-    "        if(number!=2):\n",
-    "            return 0\n",
-    "        else:\n",
-    "            return 1\n",
-    "    dividers = np.arange(number,1,2)\n",
-    "    print(dividers)\n",
-    "    for k in dividers:\n",
-    "        if number%divider:\n",
-    "            return 0\n",
-    "    return 1 \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas\n",
-    "* data wrangling\n",
-    "* groupby\n",
-    "* extension of numpys to add a dataframe capability\n",
-    "* similar to R dataframes\n",
-    "* integrates with numpy\n",
-    "* https://pandas.pydata.org/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Series\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html\n",
-    "* One-dimensional ndarray with axis labels (including time series)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vals = [1,2,3,4,5]\n",
-    "idxs = [\"a\",\"b\",\"c\",\"d\",\"e\"]\n",
-    "\n",
-    "my_series = pd.Series(vals, idxs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1\n",
-       "b    2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_series[[\"a\", \"b\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dataframe\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\n",
-    "* core object in pandas\n",
-    "* 2 dimensional\n",
-    "* index and columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array1 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5],\n",
-    "    [2,3,2,2,1,3]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 6)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "   0  1  2  3  4  5\n",
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-    "df.head(1)"
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   0  1  2  3  4  5\n",
-       "0  5  6  2  5  3  2\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=3, step=1)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=6, step=1)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### change headers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.columns = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>e</th>\n",
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-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c  d  e  f\n",
-       "0  5  6  2  5  3  2\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    int32\n",
-       "b    int32\n",
-       "c    int32\n",
-       "d    int32\n",
-       "e    int32\n",
-       "f    int32\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### accessing columns\n",
-    "* returns series\n",
-    "* index still attached to the series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    5\n",
-       "1    4\n",
-       "2    2\n",
-       "Name: a, dtype: int32"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"a\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(df[\"a\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5, 4, 2]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"a\"].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=3, step=1)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"a\"].index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
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-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "0  5  6\n",
-       "1  4  6\n",
-       "2  2  3"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[[\"a\", \"b\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### index subsetting\n",
-    "* iloc[] is position selecting\n",
-    "* iloc[rows, columns]\n",
-    "* rows/columns can be a list of indicies (integers) or use : to seperate a range\n",
-    "* [2:] means give me 2 and everything after for slicing\n",
-    "* [1] means give me index 1\n",
-    "* [1,2,3] means give me index 1,2,3\n",
-    "* [1],[0,1] means give me row 1 and columns 0 and 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    4\n",
-       "b    6\n",
-       "c    1\n",
-       "d    3\n",
-       "e    4\n",
-       "f    5\n",
-       "Name: 1, dtype: int32"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    4\n",
-       "b    6\n",
-       "c    1\n",
-       "Name: 1, dtype: int32"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# need integer based subsetting\n",
-    "df.iloc[1,[0,1,2]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "   a  b  c  d  e  f\n",
-       "1  4  6  1  3  4  5\n",
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-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df.iloc[[1,2]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
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-       "      <td>4</td>\n",
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-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c  d  e  f\n",
-       "0  5  6  2  5  3  2\n",
-       "2  2  3  2  2  1  3\n",
-       "1  4  6  1  3  4  5"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df.iloc[[0,2,1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
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-      "text/plain": [
-       "   a  b  c  d  e  f\n",
-       "0  5  6  2  5  3  2\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[0:,0:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "</div>"
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-       "   a\n",
-       "0  5\n",
-       "1  4\n",
-       "2  2"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df.iloc[0:,0:1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "1  4  6"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[[1],[0,1]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### loc\n",
-    "* used if we have labels and not integer indec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=3, step=1)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.index = [\"aa\", \"bb\", \"cc\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>3</td>\n",
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-       "      <td>bb</td>\n",
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-       "    <tr>\n",
-       "      <td>cc</td>\n",
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-       "      <td>2</td>\n",
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-       "</div>"
-      ],
-      "text/plain": [
-       "    a  b  c  d  e  f\n",
-       "aa  5  6  2  5  3  2\n",
-       "bb  4  6  1  3  4  5\n",
-       "cc  2  3  2  2  1  3"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    5\n",
-       "b    6\n",
-       "c    2\n",
-       "d    5\n",
-       "e    3\n",
-       "f    2\n",
-       "Name: aa, dtype: int32"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.loc[\"aa\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
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-   "outputs": [
-    {
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-   ],
-   "source": [
-    "df.loc[[\"aa\", \"bb\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
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-   "outputs": [
-    {
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-       "</div>"
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-      "text/plain": [
-       "    a  b\n",
-       "aa  5  6"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.loc[[\"aa\"], [\"a\",\"b\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">how might we</h3>\n",
-    "<p style=\"color:blue\">- filter if we have a large number of indices?</p>\n",
-    "<p style=\"color:blue\">- we don't want to type out a list of 1000s of index labels</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "    <tr>\n",
-       "      <td>bb</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
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-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    a  c\n",
-       "aa  5  2\n",
-       "bb  4  1"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rows = [\"aa\", \"bb\"]\n",
-    "cols = [\"a\", \"c\"]\n",
-    "\n",
-    "df.loc[rows,cols]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### subset for columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = df[[\"a\", \"b\", \"c\", \"d\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <td>2</td>\n",
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-       "    <tr>\n",
-       "      <td>bb</td>\n",
-       "      <td>4</td>\n",
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-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <td>cc</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
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-      "text/plain": [
-       "    a  b  c  d\n",
-       "aa  5  6  2  5\n",
-       "bb  4  6  1  3\n",
-       "cc  2  3  2  2"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### describe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>count</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>mean</td>\n",
-       "      <td>3.666667</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>1.666667</td>\n",
-       "      <td>3.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>std</td>\n",
-       "      <td>1.527525</td>\n",
-       "      <td>1.732051</td>\n",
-       "      <td>0.577350</td>\n",
-       "      <td>1.527525</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>min</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>25%</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>2.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>50%</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>75%</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>max</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              a         b         c         d\n",
-       "count  3.000000  3.000000  3.000000  3.000000\n",
-       "mean   3.666667  5.000000  1.666667  3.333333\n",
-       "std    1.527525  1.732051  0.577350  1.527525\n",
-       "min    2.000000  3.000000  1.000000  2.000000\n",
-       "25%    3.000000  4.500000  1.500000  2.500000\n",
-       "50%    4.000000  6.000000  2.000000  3.000000\n",
-       "75%    4.500000  6.000000  2.000000  4.000000\n",
-       "max    5.000000  6.000000  2.000000  5.000000"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### correlate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>a</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.944911</td>\n",
-       "      <td>-0.188982</td>\n",
-       "      <td>0.928571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>b</td>\n",
-       "      <td>0.944911</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>0.755929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>c</td>\n",
-       "      <td>-0.188982</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.188982</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>d</td>\n",
-       "      <td>0.928571</td>\n",
-       "      <td>0.755929</td>\n",
-       "      <td>0.188982</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          a         b         c         d\n",
-       "a  1.000000  0.944911 -0.188982  0.928571\n",
-       "b  0.944911  1.000000 -0.500000  0.755929\n",
-       "c -0.188982 -0.500000  1.000000  0.188982\n",
-       "d  0.928571  0.755929  0.188982  1.000000"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.corr()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### summations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    11\n",
-       "b    15\n",
-       "c     5\n",
-       "d    10\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.sum(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "aa    18\n",
-       "bb    14\n",
-       "cc     9\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.sum(axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### median"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    4.0\n",
-       "b    6.0\n",
-       "c    2.0\n",
-       "d    3.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.median(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### new column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n"
-     ]
-    }
-   ],
-   "source": [
-    "df1[\"class\"] = [\"AA\", \"AA\", \"BB\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>aa</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>bb</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>cc</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>BB</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    a  b  c  d class\n",
-       "aa  5  6  2  5    AA\n",
-       "bb  4  6  1  3    AA\n",
-       "cc  2  3  2  2    BB"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>AA</td>\n",
-       "      <td>9</td>\n",
-       "      <td>12</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>BB</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       a   b  c  d\n",
-       "class             \n",
-       "AA     9  12  3  8\n",
-       "BB     2   3  2  2"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>AA</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>BB</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       a  b  c  d\n",
-       "class            \n",
-       "AA     2  2  2  2\n",
-       "BB     1  1  1  1"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>AA</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>BB</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       a  b  c  d  class\n",
-       "class                   \n",
-       "AA     2  1  2  2      1\n",
-       "BB     1  1  1  1      1"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# count distinct\n",
-    "df1.groupby(\"class\").nunique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### reset index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['AA', 'BB'], dtype='object', name='class')"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").count().index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>AA</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>BB</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  class  a  b  c  d\n",
-       "0    AA  2  2  2  2\n",
-       "1    BB  1  1  1  1"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").count().reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=2, step=1)"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").count().reset_index().index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### reading in csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"../data/iris.csv\"\n",
-    "df = pd.read_csv(path, sep = \",\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1          3.5           1.4          0.2  Setosa\n",
-       "1           4.9          3.0           1.4          0.2  Setosa\n",
-       "2           4.7          3.2           1.3          0.2  Setosa\n",
-       "3           4.6          3.1           1.5          0.2  Setosa\n",
-       "4           5.0          3.6           1.4          0.2  Setosa"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">read in the iris csv:</h3>\n",
-    "<p style=\"color:blue\">- find the descriptive statistics</p>\n",
-    "<p style=\"color:blue\">- create a correlation matrix </p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### subset on rules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(61, 5)"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = df[df[\"sepal.length\"] > 6]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(23, 5)"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = df[(df[\"sepal.length\"] > 6) & (df[\"sepal.width\"] > 3)]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(50, 5)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = df[df[\"variety\"] == \"Setosa\"]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### isin()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Setosa', 'Versicolor', 'Virginica'], dtype=object)"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"variety\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(100, 5)"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst = [\"Setosa\", \"Versicolor\"]\n",
-    "\n",
-    "df1 = df[df[\"variety\"].isin(lst)]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### like"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(50, 5)"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = df[df[\"variety\"].str.contains(\"Versi\")]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### apply"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x):\n",
-    "    return -x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1          3.5           1.4          0.2  Setosa"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(my_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>-5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>-4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>-4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>-4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0          -5.1          3.5           1.4          0.2  Setosa\n",
-       "1          -4.9          3.0           1.4          0.2  Setosa\n",
-       "2          -4.7          3.2           1.3          0.2  Setosa\n",
-       "3          -4.6          3.1           1.5          0.2  Setosa\n",
-       "4          -5.0          3.6           1.4          0.2  Setosa"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['sepal.length', 'sepal.width', 'petal.length', 'petal.width',\n",
-       "       'variety'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['sepal.length', 'sepal.width', 'petal.length', 'petal.width']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in cols:\n",
-    "    df[col] = df[col].apply(my_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>-3.5</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>-1.3</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>-3.1</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>-3.6</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1         -3.5          -1.4         -0.2  Setosa\n",
-       "1           4.9         -3.0          -1.4         -0.2  Setosa\n",
-       "2           4.7         -3.2          -1.3         -0.2  Setosa\n",
-       "3           4.6         -3.1          -1.5         -0.2  Setosa\n",
-       "4           5.0         -3.6          -1.4         -0.2  Setosa"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(lambda x: -x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>-5.1</td>\n",
-       "      <td>-3.5</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>-4.9</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>-4.7</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>-1.3</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>-4.6</td>\n",
-       "      <td>-3.1</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>-3.6</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0          -5.1         -3.5          -1.4         -0.2  Setosa\n",
-       "1          -4.9         -3.0          -1.4         -0.2  Setosa\n",
-       "2          -4.7         -3.2          -1.3         -0.2  Setosa\n",
-       "3          -4.6         -3.1          -1.5         -0.2  Setosa\n",
-       "4          -5.0         -3.6          -1.4         -0.2  Setosa"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### datatypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sepal.length    float64\n",
-       "sepal.width     float64\n",
-       "petal.length    float64\n",
-       "petal.width     float64\n",
-       "variety          object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(str)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sepal.length     object\n",
-       "sepal.width     float64\n",
-       "petal.length    float64\n",
-       "petal.width     float64\n",
-       "variety          object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### get dummies\n",
-    "* for string or object variables\n",
-    "* can specify specific columns\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">what might we need dummies for?</h3>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.get_dummies(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>sepal.length_-4.3</th>\n",
-       "      <th>sepal.length_-4.4</th>\n",
-       "      <th>sepal.length_-4.5</th>\n",
-       "      <th>sepal.length_-4.6</th>\n",
-       "      <th>sepal.length_-4.7</th>\n",
-       "      <th>sepal.length_-4.8</th>\n",
-       "      <th>sepal.length_-4.9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sepal.length_-7.1</th>\n",
-       "      <th>sepal.length_-7.2</th>\n",
-       "      <th>sepal.length_-7.3</th>\n",
-       "      <th>sepal.length_-7.4</th>\n",
-       "      <th>sepal.length_-7.6</th>\n",
-       "      <th>sepal.length_-7.7</th>\n",
-       "      <th>sepal.length_-7.9</th>\n",
-       "      <th>variety_Setosa</th>\n",
-       "      <th>variety_Versicolor</th>\n",
-       "      <th>variety_Virginica</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>-3.5</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>-1.3</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>-3.1</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>-3.6</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 41 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.width  petal.length  petal.width  sepal.length_-4.3  \\\n",
-       "0         -3.5          -1.4         -0.2                  0   \n",
-       "1         -3.0          -1.4         -0.2                  0   \n",
-       "2         -3.2          -1.3         -0.2                  0   \n",
-       "3         -3.1          -1.5         -0.2                  0   \n",
-       "4         -3.6          -1.4         -0.2                  0   \n",
-       "\n",
-       "   sepal.length_-4.4  sepal.length_-4.5  sepal.length_-4.6  sepal.length_-4.7  \\\n",
-       "0                  0                  0                  0                  0   \n",
-       "1                  0                  0                  0                  0   \n",
-       "2                  0                  0                  0                  1   \n",
-       "3                  0                  0                  1                  0   \n",
-       "4                  0                  0                  0                  0   \n",
-       "\n",
-       "   sepal.length_-4.8  sepal.length_-4.9  ...  sepal.length_-7.1  \\\n",
-       "0                  0                  0  ...                  0   \n",
-       "1                  0                  1  ...                  0   \n",
-       "2                  0                  0  ...                  0   \n",
-       "3                  0                  0  ...                  0   \n",
-       "4                  0                  0  ...                  0   \n",
-       "\n",
-       "   sepal.length_-7.2  sepal.length_-7.3  sepal.length_-7.4  sepal.length_-7.6  \\\n",
-       "0                  0                  0                  0                  0   \n",
-       "1                  0                  0                  0                  0   \n",
-       "2                  0                  0                  0                  0   \n",
-       "3                  0                  0                  0                  0   \n",
-       "4                  0                  0                  0                  0   \n",
-       "\n",
-       "   sepal.length_-7.7  sepal.length_-7.9  variety_Setosa  variety_Versicolor  \\\n",
-       "0                  0                  0               1                   0   \n",
-       "1                  0                  0               1                   0   \n",
-       "2                  0                  0               1                   0   \n",
-       "3                  0                  0               1                   0   \n",
-       "4                  0                  0               1                   0   \n",
-       "\n",
-       "   variety_Virginica  \n",
-       "0                  0  \n",
-       "1                  0  \n",
-       "2                  0  \n",
-       "3                  0  \n",
-       "4                  0  \n",
-       "\n",
-       "[5 rows x 41 columns]"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>BB</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>CC</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1 col_2\n",
-       "0     A    AA\n",
-       "1     B    BB\n",
-       "2     C    CC"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col_1 = np.array([\"A\", \"B\", \"C\"])\n",
-    "col_2 = np.array([\"AA\", \"BB\", \"CC\"])\n",
-    "\n",
-    "df = pd.DataFrame({\n",
-    "    \"col_1\":col_1,\n",
-    "    \"col_2\":col_2\n",
-    "})\n",
-    "\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">perform get dummies on col_1 only?</h3>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### iterate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 col_1     A\n",
-      "col_2    AA\n",
-      "Name: 0, dtype: object\n",
-      "1 col_1     B\n",
-      "col_2    BB\n",
-      "Name: 1, dtype: object\n",
-      "2 col_1     C\n",
-      "col_2    CC\n",
-      "Name: 2, dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "for idx, r in df.head(5).iterrows():\n",
-    "    print(idx, r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'petal.length'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4735\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4736\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4737\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.get_value_at\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.validate_indexer\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-79-99dacd2ca267>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"petal.length\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1066\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1067\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1068\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1069\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1070\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4742\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4743\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4744\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4745\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4746\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4728\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4729\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4730\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4731\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4732\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'petal.length'"
-     ]
-    }
-   ],
-   "source": [
-    "for idx, r in df.head(5).iterrows():\n",
-    "    print(r[\"petal.length\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'petal.width'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'petal.width'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-80-4e0306d0c85d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"petal.width\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'petal.width'"
-     ]
-    }
-   ],
-   "source": [
-    "df[\"petal.width\"].tolist()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Using iris, group by variety and find the count of the other columns?</h3>\n",
-    "<h3 style=\"color:blue\">Using iris, group by variety and find the sum of the other columns?</h3>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_1_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>E</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_1_ind\n",
-       "0     A          1\n",
-       "1     B          1\n",
-       "2     C          1\n",
-       "3     D          1\n",
-       "4     E          1"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col_1 = np.array([\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
-    "col_2 = np.array([\"A\", \"B\", \"C\"])\n",
-    "\n",
-    "df = pd.DataFrame({\n",
-    "    \"col_1\":col_1,\n",
-    "    \"col_1_ind\":1\n",
-    "})\n",
-    "\n",
-    "df1 = pd.DataFrame({\n",
-    "    \"col_1\":col_2,\n",
-    "    \"col_2_ind\":1\n",
-    "})\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_2_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
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-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_2_ind\n",
-       "0     A          1\n",
-       "1     B          1\n",
-       "2     C          1"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### concat\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
-      "of pandas will change to not sort by default.\n",
-      "\n",
-      "To accept the future behavior, pass 'sort=False'.\n",
-      "\n",
-      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
-      "\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_1_ind</th>\n",
-       "      <th>col_2_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
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-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>E</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_1_ind  col_2_ind\n",
-       "0     A        1.0        NaN\n",
-       "1     B        1.0        NaN\n",
-       "2     C        1.0        NaN\n",
-       "3     D        1.0        NaN\n",
-       "4     E        1.0        NaN\n",
-       "0     A        NaN        1.0\n",
-       "1     B        NaN        1.0\n",
-       "2     C        NaN        1.0"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([df, df1], axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### merge\n",
-    "* https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.merge.html\n",
-    "* assumes inner join"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "select *\n",
-    "from df inner join df2\n",
-    "    on df.col_1 = df2.col_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_1_ind</th>\n",
-       "      <th>col_2_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_1_ind  col_2_ind\n",
-       "0     A          1          1\n",
-       "1     B          1          1\n",
-       "2     C          1          1"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.merge(df1, how = \"inner\", left_on = \"col_1\", right_on = \"col_1\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "select *\n",
-    "from df left join df2\n",
-    "    on df.col_1 = df2.col_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_1_ind</th>\n",
-       "      <th>col_2_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>E</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_1_ind  col_2_ind\n",
-       "0     A          1        1.0\n",
-       "1     B          1        1.0\n",
-       "2     C          1        1.0\n",
-       "3     D          1        NaN\n",
-       "4     E          1        NaN"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.merge(df1, how = \"left\", left_on = \"col_1\", right_on = \"col_1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Imputation\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.interpolate.html#pandas.DataFrame.interpolate\n",
-    "* missing values\n",
-    "* When summing data, NA (missing) values will be treated as zero.\n",
-    "* If the data are all NA, the result will be 0.\n",
-    "* Cumulative methods like cumsum() and cumprod() ignore NA values by default, but preserve them in the resulting arrays. To override this behaviour and include NA values, use skipna=False.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "data = [1,2,3,4,None,5,2,1]\n",
-    "df = pd.DataFrame(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0\n",
-       "0  1.0\n",
-       "1  2.0\n",
-       "2  3.0\n",
-       "3  4.0\n",
-       "4  NaN\n",
-       "5  5.0\n",
-       "6  2.0\n",
-       "7  1.0"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       0\n",
-       "0  False\n",
-       "1  False\n",
-       "2  False\n",
-       "3  False\n",
-       "4   True\n",
-       "5  False\n",
-       "6  False\n",
-       "7  False"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       0\n",
-       "0   True\n",
-       "1   True\n",
-       "2   True\n",
-       "3   True\n",
-       "4  False\n",
-       "5   True\n",
-       "6   True\n",
-       "7   True"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.notna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0\n",
-       "0  1.0\n",
-       "1  2.0\n",
-       "2  3.0\n",
-       "3  4.0\n",
-       "4  0.0\n",
-       "5  5.0\n",
-       "6  2.0\n",
-       "7  1.0"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "</style>\n",
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-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       0\n",
-       "0    1.0\n",
-       "1    2.0\n",
-       "2    3.0\n",
-       "3    4.0\n",
-       "4  100.0\n",
-       "5    5.0\n",
-       "6    2.0\n",
-       "7    1.0"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.fillna(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    <tr>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
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-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
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-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.0</td>\n",
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-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
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-    "df.dropna(axis=0)"
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-    "df.fillna(df.median())"
-   ]
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-   "outputs": [
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-       "      <td>5</td>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
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-       "      <td>6</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
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-      "text/plain": [
-       "   index         0\n",
-       "0      0  1.000000\n",
-       "1      1  2.000000\n",
-       "2      2  3.000000\n",
-       "3      3  4.000000\n",
-       "4      4  2.571429\n",
-       "5      5  5.000000\n",
-       "6      6  2.000000\n",
-       "7      7  1.000000"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[0].fillna(df[0].mean()).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Json data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"../data/ca.json\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "with open(path, \"r\") as file:\n",
-    "    line = file.readlines()\n",
-    "    for l in line[0:50]:\n",
-    "        print(l)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "{\n",
-    "    \"brian\" {\n",
-    "        \"age\":31,\n",
-    "        \"job\":data scientist,\n",
-    "        \"city\"chicago\n",
-    "        },\n",
-    "        \n",
-    "   \"james\":{\n",
-    "        \"age\":28,\n",
-    "        \"job\":data scientist,\n",
-    "        \"city\"new york\n",
-    "        }\n",
-    "        \n",
-    "     \"james\":{\n",
-    "        \"age\":28,\n",
-    "        }\n",
-    "    \n",
-    "    \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "columns = [\"age\", \"job\", \"city\"]\n",
-    "data = [\n",
-    "    [31, \"data scientist\", \"chicago\"],\n",
-    "    [28, \"data scientist\", \"new york\"],\n",
-    "    [28,None,None]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data, columns = columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Expected object or value",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-102-1ce8f6ab196d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    593\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mshould_close\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    715\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 717\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    718\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[1;34m(self, json)\u001b[0m\n\u001b[0;32m    737\u001b[0m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"frame\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 739\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    741\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"series\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    847\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    848\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 849\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_no_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    850\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    851\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1091\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m             self.obj = DataFrame(\n\u001b[1;32m-> 1093\u001b[1;33m                 \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprecise_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprecise_float\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1094\u001b[0m             )\n\u001b[0;32m   1095\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"split\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Expected object or value"
-     ]
-    }
-   ],
-   "source": [
-    "df = pd.read_json(path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.json.json_normalize.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* Nested JSON files can be time consuming and difficult process to flatten and load into Pandas.\n",
-    "* Let’s unpack the works column into a standalone dataframe. We’ll also grab the flat columns.\n",
-    "* nycphil = json_normalize(d['programs'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = [\n",
-    "        {'id': 1, 'name': \"Cole Volk\",'fitness': \n",
-    "             {'height': 130, 'weight': 60}},\n",
-    "    \n",
-    "        {'name': \"Mose Reg\",'fitness': \n",
-    "             {'height': 130, 'weight': 60}},\n",
-    "    \n",
-    "        {'id': 2, 'name': 'Faye Raker','fitness': \n",
-    "             {'height': 130, 'weight': 60}}\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas.io.json import json_normalize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>name</th>\n",
-       "      <th>fitness.height</th>\n",
-       "      <th>fitness.weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Cole Volk</td>\n",
-       "      <td>130</td>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Mose Reg</td>\n",
-       "      <td>130</td>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Faye Raker</td>\n",
-       "      <td>130</td>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id        name  fitness.height  fitness.weight\n",
-       "0  1.0   Cole Volk             130              60\n",
-       "1  NaN    Mose Reg             130              60\n",
-       "2  2.0  Faye Raker             130              60"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>name</th>\n",
-       "      <th>fitness</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Cole Volk</td>\n",
-       "      <td>{'height': 130, 'weight': 60}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Mose Reg</td>\n",
-       "      <td>{'height': 130, 'weight': 60}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Faye Raker</td>\n",
-       "      <td>{'height': 130, 'weight': 60}</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id        name                        fitness\n",
-       "0  1.0   Cole Volk  {'height': 130, 'weight': 60}\n",
-       "1  NaN    Mose Reg  {'height': 130, 'weight': 60}\n",
-       "2  2.0  Faye Raker  {'height': 130, 'weight': 60}"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data, max_level = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = [\n",
-    "        {'state': 'Florida','shortname': 'FL',\n",
-    "            'info': {'governor': 'Rick Scott', \"gender\":\"m\"},\n",
-    "            'counties': [{'name': 'Dade', 'population': 12345},\n",
-    "                         {'name': 'Broward', 'population': 40000},\n",
-    "                         {'name': 'Palm Beach', 'population': 60000}]},\n",
-    "    \n",
-    "         {'state': 'Ohio', 'shortname': 'OH',\n",
-    "          'info': {'governor': 'John Kasich', \"gender\":\"m\"},\n",
-    "          'counties': [{'name': 'Summit', 'population': 1234},\n",
-    "                       {'name': 'Cuyahoga', 'population': 1337}]}\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>shortname</th>\n",
-       "      <th>info</th>\n",
-       "      <th>counties</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Florida</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>{'governor': 'Rick Scott', 'gender': 'm'}</td>\n",
-       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Ohio</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>{'governor': 'John Kasich', 'gender': 'm'}</td>\n",
-       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     state shortname                                        info  \\\n",
-       "0  Florida        FL   {'governor': 'Rick Scott', 'gender': 'm'}   \n",
-       "1     Ohio        OH  {'governor': 'John Kasich', 'gender': 'm'}   \n",
-       "\n",
-       "                                            counties  \n",
-       "0  [{'name': 'Dade', 'population': 12345}, {'name...  \n",
-       "1  [{'name': 'Summit', 'population': 1234}, {'nam...  "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data, max_level = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>shortname</th>\n",
-       "      <th>counties</th>\n",
-       "      <th>info.governor</th>\n",
-       "      <th>info.gender</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Florida</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Ohio</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
-       "      <td>John Kasich</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     state shortname                                           counties  \\\n",
-       "0  Florida        FL  [{'name': 'Dade', 'population': 12345}, {'name...   \n",
-       "1     Ohio        OH  [{'name': 'Summit', 'population': 1234}, {'nam...   \n",
-       "\n",
-       "  info.governor info.gender  \n",
-       "0    Rick Scott           m  \n",
-       "1   John Kasich           m  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data, max_level = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>shortname</th>\n",
-       "      <th>counties</th>\n",
-       "      <th>info.governor</th>\n",
-       "      <th>info.gender</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Florida</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Ohio</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
-       "      <td>John Kasich</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     state shortname                                           counties  \\\n",
-       "0  Florida        FL  [{'name': 'Dade', 'population': 12345}, {'name...   \n",
-       "1     Ohio        OH  [{'name': 'Summit', 'population': 1234}, {'nam...   \n",
-       "\n",
-       "  info.governor info.gender  \n",
-       "0    Rick Scott           m  \n",
-       "1   John Kasich           m  "
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>info.governor</th>\n",
-       "      <th>info.gender</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Dade</td>\n",
-       "      <td>12345</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Broward</td>\n",
-       "      <td>40000</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>Palm Beach</td>\n",
-       "      <td>60000</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>Summit</td>\n",
-       "      <td>1234</td>\n",
-       "      <td>John Kasich</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>Cuyahoga</td>\n",
-       "      <td>1337</td>\n",
-       "      <td>John Kasich</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         name  population info.governor info.gender\n",
-       "0        Dade       12345    Rick Scott           m\n",
-       "1     Broward       40000    Rick Scott           m\n",
-       "2  Palm Beach       60000    Rick Scott           m\n",
-       "3      Summit        1234   John Kasich           m\n",
-       "4    Cuyahoga        1337   John Kasich           m"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# make each row a county, then start parsing data as such\n",
-    "json_normalize(data, \"counties\", [[\"info\",\"governor\"], [\"info\", \"gender\"]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chunks\n",
-    "* Can use chunks to process pieces of a dataframe at a time if it won't fit into memory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_size = 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"../data/iris.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 5)\n",
-      "(50, 5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for c in pd.read_csv(path, chunksize = chunk_size):\n",
-    "    print(c.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 4)\n",
-      "(1, 4)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for c in pd.read_csv(path, chunksize = chunk_size):\n",
-    "    df = c.groupby(\"variety\").sum()\n",
-    "    print(df.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/msca/__init__.py b/msca/__init__.py
index 38af7b2..d9fe5f4 100644
--- a/msca/__init__.py
+++ b/msca/__init__.py
@@ -1 +1,4 @@
-from .teste import *
\ No newline at end of file
+from .teste import *
+import face_recon
+import nqueens
+import imagenet
\ No newline at end of file

From 32cf961d086448758344ef8c1c8fd96691ea8733 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 13:17:03 -0600
Subject: [PATCH 16/36] new things

---
 msca/README.md     |  3 +++
 msca/face_recon.py |  0
 msca/imagenet.py   |  0
 msca/nqueens.py    | 27 +++++++++++++++++++++++++++
 4 files changed, 30 insertions(+)
 create mode 100644 msca/README.md
 create mode 100644 msca/face_recon.py
 create mode 100644 msca/imagenet.py
 create mode 100644 msca/nqueens.py

diff --git a/msca/README.md b/msca/README.md
new file mode 100644
index 0000000..e3a1836
--- /dev/null
+++ b/msca/README.md
@@ -0,0 +1,3 @@
+# The MSCA challenge library
+
+Lets learn how to build and operate a python module and its libraries.
diff --git a/msca/face_recon.py b/msca/face_recon.py
new file mode 100644
index 0000000..e69de29
diff --git a/msca/imagenet.py b/msca/imagenet.py
new file mode 100644
index 0000000..e69de29
diff --git a/msca/nqueens.py b/msca/nqueens.py
new file mode 100644
index 0000000..afb8f69
--- /dev/null
+++ b/msca/nqueens.py
@@ -0,0 +1,27 @@
+class raf_NQueens:
+    def __init__(self, size, show=False):
+        self.size = size
+        self.solutions = 0
+        self.solve()
+
+    def solve(self):
+        positions = [-1] * self.size
+        self.put_queen(positions, 0)
+        return self.solutions
+
+    def put_queen(self, positions, target_row):
+        if target_row == self.size:
+            self.solutions += 1
+        else:
+            for column in range(self.size):
+                if self.check_place(positions, target_row, column):
+                    positions[target_row] = column
+                    self.put_queen(positions, target_row + 1)
+
+    def check_place(self, positions, ocuppied_rows, column):
+        for i in range(ocuppied_rows):
+            if positions[i] == column or \
+                positions[i] - i == column - ocuppied_rows or \
+                positions[i] + i == column + ocuppied_rows:
+                return False
+        return True

From 8bb25b8eb3c67bf053734f1be541e369221a998e Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 13:17:30 -0600
Subject: [PATCH 17/36] new things

---
 msca/nqueens.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/msca/nqueens.py b/msca/nqueens.py
index afb8f69..6ef119b 100644
--- a/msca/nqueens.py
+++ b/msca/nqueens.py
@@ -1,4 +1,4 @@
-class raf_NQueens:
+class raf_nq:
     def __init__(self, size, show=False):
         self.size = size
         self.solutions = 0

From a00ecd264051cc25e6716dbc4971f1d3e585a9ae Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 13:18:13 -0600
Subject: [PATCH 18/36] new things

---
 msca/__init__.py | 2 +-
 msca/teste.py    | 5 -----
 2 files changed, 1 insertion(+), 6 deletions(-)
 delete mode 100644 msca/teste.py

diff --git a/msca/__init__.py b/msca/__init__.py
index d9fe5f4..c095125 100644
--- a/msca/__init__.py
+++ b/msca/__init__.py
@@ -1,4 +1,4 @@
-from .teste import *
+import base
 import face_recon
 import nqueens
 import imagenet
\ No newline at end of file
diff --git a/msca/teste.py b/msca/teste.py
deleted file mode 100644
index 36e336f..0000000
--- a/msca/teste.py
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
-
-def raf_sum(a,b):
-  return a+b
\ No newline at end of file

From a9c620d1a3763a30de0fe56f1d5e18005bcfdff9 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 13:18:20 -0600
Subject: [PATCH 19/36] new things

---
 msca/base.py | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 msca/base.py

diff --git a/msca/base.py b/msca/base.py
new file mode 100644
index 0000000..36e336f
--- /dev/null
+++ b/msca/base.py
@@ -0,0 +1,5 @@
+
+
+
+def raf_sum(a,b):
+  return a+b
\ No newline at end of file

From fb2a5f0768a2ac425b3b450d6909f88777f9500e Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 13:43:26 -0600
Subject: [PATCH 20/36] fix

---
 msca/__init__.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/msca/__init__.py b/msca/__init__.py
index c095125..29072b4 100644
--- a/msca/__init__.py
+++ b/msca/__init__.py
@@ -1,4 +1,4 @@
-import base
-import face_recon
-import nqueens
-import imagenet
\ No newline at end of file
+from . import base
+from . import face_recon
+from . import nqueens
+from . import imagenet
\ No newline at end of file

From a6f39789b4738a902c6a4e0aa883893badcc317c Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 13:54:28 -0600
Subject: [PATCH 21/36] fix

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 76f0608..feae445 100644
--- a/setup.py
+++ b/setup.py
@@ -9,7 +9,7 @@
     description="MSCA Class Package",
     long_description_content_type="text/markdown",
     url="https://github.com/ravescovi/python_for_analitycs",
-    packages=['msca'],
+    packages=find_packages(exclude=["*test*"]),
     include_package_data=True,
     classifiers=[
         "Programming Language :: Python :: 3",

From 451b17b35a49ab8d8240e7b310195d7166adb353 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 5 Feb 2020 13:54:32 -0600
Subject: [PATCH 22/36] fix

---
 data/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 data/__init__.py

diff --git a/data/__init__.py b/data/__init__.py
new file mode 100644
index 0000000..e69de29

From 4ecb313da52fc8ea7f1021f3a6f40c9dba4bf303 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 11 Feb 2020 19:37:02 -0600
Subject: [PATCH 23/36] new commit

---
 build/lib/msca/__init__.py |  4 ++++
 build/lib/msca/base.py     | 11 +++++++++++
 msca/base.py               |  8 +++++++-
 3 files changed, 22 insertions(+), 1 deletion(-)
 create mode 100644 build/lib/msca/__init__.py
 create mode 100644 build/lib/msca/base.py

diff --git a/build/lib/msca/__init__.py b/build/lib/msca/__init__.py
new file mode 100644
index 0000000..29072b4
--- /dev/null
+++ b/build/lib/msca/__init__.py
@@ -0,0 +1,4 @@
+from . import base
+from . import face_recon
+from . import nqueens
+from . import imagenet
\ No newline at end of file
diff --git a/build/lib/msca/base.py b/build/lib/msca/base.py
new file mode 100644
index 0000000..3fd402f
--- /dev/null
+++ b/build/lib/msca/base.py
@@ -0,0 +1,11 @@
+
+
+
+def raf_sum(a,b):
+  return a+b
+
+def potato(state):
+  if state=='hot':
+    print('hot potato')
+  else:
+    print('No cool')
\ No newline at end of file
diff --git a/msca/base.py b/msca/base.py
index 36e336f..3fd402f 100644
--- a/msca/base.py
+++ b/msca/base.py
@@ -2,4 +2,10 @@
 
 
 def raf_sum(a,b):
-  return a+b
\ No newline at end of file
+  return a+b
+
+def potato(state):
+  if state=='hot':
+    print('hot potato')
+  else:
+    print('No cool')
\ No newline at end of file

From 3d66f0d53e2147f968e62394f67d83e60434e9c6 Mon Sep 17 00:00:00 2001
From: Lucia Ronchi <lronchi@uchicago.edu>
Date: Tue, 11 Feb 2020 19:39:09 -0600
Subject: [PATCH 24/36] Added function

---
 msca/base.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/msca/base.py b/msca/base.py
index 36e336f..1e8e044 100644
--- a/msca/base.py
+++ b/msca/base.py
@@ -2,4 +2,8 @@
 
 
 def raf_sum(a,b):
-  return a+b
\ No newline at end of file
+  return a+b
+
+
+ def Lucia(x):
+     x**2

From 7fc4a336173c841f9a7fc44be479b0e659e23f52 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 18 Feb 2020 15:40:01 -0600
Subject: [PATCH 25/36] new things

---
 build/lib/msca/__init__.py |  4 ----
 build/lib/msca/base.py     | 11 -----------
 msca/__init__.py           |  4 ++--
 msca/face_recon.py         |  0
 4 files changed, 2 insertions(+), 17 deletions(-)
 delete mode 100644 build/lib/msca/__init__.py
 delete mode 100644 build/lib/msca/base.py
 delete mode 100644 msca/face_recon.py

diff --git a/build/lib/msca/__init__.py b/build/lib/msca/__init__.py
deleted file mode 100644
index 29072b4..0000000
--- a/build/lib/msca/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from . import base
-from . import face_recon
-from . import nqueens
-from . import imagenet
\ No newline at end of file
diff --git a/build/lib/msca/base.py b/build/lib/msca/base.py
deleted file mode 100644
index 3fd402f..0000000
--- a/build/lib/msca/base.py
+++ /dev/null
@@ -1,11 +0,0 @@
-
-
-
-def raf_sum(a,b):
-  return a+b
-
-def potato(state):
-  if state=='hot':
-    print('hot potato')
-  else:
-    print('No cool')
\ No newline at end of file
diff --git a/msca/__init__.py b/msca/__init__.py
index 29072b4..cbdcccb 100644
--- a/msca/__init__.py
+++ b/msca/__init__.py
@@ -1,4 +1,4 @@
 from . import base
-from . import face_recon
+from . import face_det
 from . import nqueens
-from . import imagenet
\ No newline at end of file
+from . import imagenet
diff --git a/msca/face_recon.py b/msca/face_recon.py
deleted file mode 100644
index e69de29..0000000

From e3a24bbb524539bc92ec156278d4ee02d9366deb Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 18 Feb 2020 19:13:26 -0600
Subject: [PATCH 26/36] raf

---
 data/abba.png                            |   Bin 0 -> 276231 bytes
 data/haarcascade_frontalface_default.xml | 33314 +++++++++++++++++++++
 msca/face_det.py                         |    29 +
 3 files changed, 33343 insertions(+)
 create mode 100644 data/abba.png
 create mode 100644 data/haarcascade_frontalface_default.xml
 create mode 100644 msca/face_det.py

diff --git a/data/abba.png b/data/abba.png
new file mode 100644
index 0000000000000000000000000000000000000000..bc1657f354c8678bd9285c2cf1159a44be550944
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diff --git a/data/haarcascade_frontalface_default.xml b/data/haarcascade_frontalface_default.xml
new file mode 100644
index 0000000..cbd1aa8
--- /dev/null
+++ b/data/haarcascade_frontalface_default.xml
@@ -0,0 +1,33314 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 24x24 discrete(?) adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>24</height>
+  <width>24</width>
+  <stageParams>
+    <maxWeakCount>211</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount></featureParams>
+  <stageNum>25</stageNum>
+  <stages>
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-5.0425500869750977e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 0 -3.1511999666690826e-02</internalNodes>
+          <leafValues>
+            2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 1 1.2396000325679779e-02</internalNodes>
+          <leafValues>
+            -1.8633940219879150e+00 1.3272049427032471e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 2 2.1927999332547188e-02</internalNodes>
+          <leafValues>
+            -1.5105249881744385e+00 1.0625729560852051e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 3 5.7529998011887074e-03</internalNodes>
+          <leafValues>
+            -8.7463897466659546e-01 1.1760339736938477e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 4 1.5014000236988068e-02</internalNodes>
+          <leafValues>
+            -7.7945697307586670e-01 1.2608419656753540e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 5 9.9371001124382019e-02</internalNodes>
+          <leafValues>
+            5.5751299858093262e-01 -1.8743000030517578e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 6 2.7340000960975885e-03</internalNodes>
+          <leafValues>
+            -1.6911929845809937e+00 4.4009700417518616e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 7 -1.8859000876545906e-02</internalNodes>
+          <leafValues>
+            -1.4769539833068848e+00 4.4350099563598633e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 8 5.9739998541772366e-03</internalNodes>
+          <leafValues>
+            -8.5909199714660645e-01 8.5255599021911621e-01</leafValues></_></weakClassifiers></_>
+    <_>
+      <maxWeakCount>16</maxWeakCount>
+      <stageThreshold>-4.9842400550842285e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 9 -2.1110000088810921e-02</internalNodes>
+          <leafValues>
+            1.2435649633407593e+00 -1.5713009834289551e+00</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -1.6204780340194702e+00 1.1817760467529297e+00</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
+          <internalNodes>
+            0 -1 12 9.1660000383853912e-02</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
+            0 -1 13 3.6288000643253326e-02</internalNodes>
+          <leafValues>
+            2.6763799786567688e-01 -2.1831810474395752e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 14 -1.9109999760985374e-02</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
+            0 -1 15 8.2539999857544899e-03</internalNodes>
+          <leafValues>
+            -1.0852910280227661e+00 5.3564202785491943e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+          <leafValues>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          3 19 9 2 3.</_></rects></_>
+    <_>
+      <rects>
+        <_>
+          16 2 3 20 -1.</_>
+        <_>
+          17 2 1 20 3.</_></rects></_>
+    <_>
+      <rects>
+        <_>
+          0 13 24 8 -1.</_>
+        <_>
+          0 17 24 4 2.</_></rects></_>
+    <_>
+      <rects>
+        <_>
+          9 1 6 22 -1.</_>
+        <_>
+          12 1 3 11 2.</_>
+        <_>
+          9 12 3 11 2.</_></rects></_></features></cascade>
+</opencv_storage>
diff --git a/msca/face_det.py b/msca/face_det.py
new file mode 100644
index 0000000..75fb42d
--- /dev/null
+++ b/msca/face_det.py
@@ -0,0 +1,29 @@
+
+
+def face_detect(imgPath, cascPath=None):
+	try :
+		import cv2
+
+	if cascPath==None:
+		print('No cascadades.. No cool')
+		return
+
+	faceCascade = cv2.CascadeClassifier(cascPath)
+
+	image = cv2.imread(imgPath)
+	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+	faces = faceCascade.detectMultiScale(
+	    gray,
+	    scaleFactor=1.1,
+	    minNeighbors=5,
+	    minSize=(30, 30)
+	)
+
+
+	return image, faces
+
+def draw_squares(image, faces):
+	for (x, y, w, h) in faces:
+	    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
+	return image

From 6fd6f5d86570f274d563388612cbab98648c0022 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 2 Mar 2020 11:25:31 -0600
Subject: [PATCH 27/36] rebase

---
 build/lib/data/__init__.py                    |    0
 build/lib/msca/__init__.py                    |    4 +
 build/lib/msca/base.py                        |    0
 build/lib/msca/face_det.py                    |   29 +
 build/lib/msca/face_recon.py                  |    0
 build/lib/msca/imagenet.py                    |    0
 build/lib/msca/nqueens.py                     |   27 +
 .../Challenge 2 - Face Recognition.ipynb      |  270 +-
 lectures/Lec8.ipynb.txt                       |    0
 .../Lecture 6 - Matplotlib, Seaborn.ipynb     |  378 +-
 ...eature Extraction and Transformation.ipynb |  546 +--
 ...n Tree, Regression, Model Evaluation.ipynb | 3419 +++++++++++++++--
 lectures/Sample Lecture - Objects.ipynb       | 1063 +++++
 msca/base.py                                  |   15 -
 msca/face_det.py                              |    8 +-
 presentation/MSCA_37014_v1.pptx               |  Bin 1331506 -> 67791 bytes
 16 files changed, 5043 insertions(+), 716 deletions(-)
 create mode 100644 build/lib/data/__init__.py
 create mode 100644 build/lib/msca/__init__.py
 create mode 100644 build/lib/msca/base.py
 create mode 100644 build/lib/msca/face_det.py
 create mode 100644 build/lib/msca/face_recon.py
 create mode 100644 build/lib/msca/imagenet.py
 create mode 100644 build/lib/msca/nqueens.py
 create mode 100644 lectures/Lec8.ipynb.txt
 create mode 100644 lectures/Sample Lecture - Objects.ipynb

diff --git a/build/lib/data/__init__.py b/build/lib/data/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/build/lib/msca/__init__.py b/build/lib/msca/__init__.py
new file mode 100644
index 0000000..cbdcccb
--- /dev/null
+++ b/build/lib/msca/__init__.py
@@ -0,0 +1,4 @@
+from . import base
+from . import face_det
+from . import nqueens
+from . import imagenet
diff --git a/build/lib/msca/base.py b/build/lib/msca/base.py
new file mode 100644
index 0000000..e69de29
diff --git a/build/lib/msca/face_det.py b/build/lib/msca/face_det.py
new file mode 100644
index 0000000..75fb42d
--- /dev/null
+++ b/build/lib/msca/face_det.py
@@ -0,0 +1,29 @@
+
+
+def face_detect(imgPath, cascPath=None):
+	try :
+		import cv2
+
+	if cascPath==None:
+		print('No cascadades.. No cool')
+		return
+
+	faceCascade = cv2.CascadeClassifier(cascPath)
+
+	image = cv2.imread(imgPath)
+	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+	faces = faceCascade.detectMultiScale(
+	    gray,
+	    scaleFactor=1.1,
+	    minNeighbors=5,
+	    minSize=(30, 30)
+	)
+
+
+	return image, faces
+
+def draw_squares(image, faces):
+	for (x, y, w, h) in faces:
+	    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
+	return image
diff --git a/build/lib/msca/face_recon.py b/build/lib/msca/face_recon.py
new file mode 100644
index 0000000..e69de29
diff --git a/build/lib/msca/imagenet.py b/build/lib/msca/imagenet.py
new file mode 100644
index 0000000..e69de29
diff --git a/build/lib/msca/nqueens.py b/build/lib/msca/nqueens.py
new file mode 100644
index 0000000..6ef119b
--- /dev/null
+++ b/build/lib/msca/nqueens.py
@@ -0,0 +1,27 @@
+class raf_nq:
+    def __init__(self, size, show=False):
+        self.size = size
+        self.solutions = 0
+        self.solve()
+
+    def solve(self):
+        positions = [-1] * self.size
+        self.put_queen(positions, 0)
+        return self.solutions
+
+    def put_queen(self, positions, target_row):
+        if target_row == self.size:
+            self.solutions += 1
+        else:
+            for column in range(self.size):
+                if self.check_place(positions, target_row, column):
+                    positions[target_row] = column
+                    self.put_queen(positions, target_row + 1)
+
+    def check_place(self, positions, ocuppied_rows, column):
+        for i in range(ocuppied_rows):
+            if positions[i] == column or \
+                positions[i] - i == column - ocuppied_rows or \
+                positions[i] + i == column + ocuppied_rows:
+                return False
+        return True
diff --git a/challenges/Challenge 2 - Face Recognition.ipynb b/challenges/Challenge 2 - Face Recognition.ipynb
index 1b5094a..3fff9e8 100644
--- a/challenges/Challenge 2 - Face Recognition.ipynb	
+++ b/challenges/Challenge 2 - Face Recognition.ipynb	
@@ -22,9 +22,277 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import msca\n",
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def face_detect(image, cascPath=None):\n",
+    "\n",
+    "\n",
+    "    faceCascade = cv2.CascadeClassifier(cascPath)\n",
+    "\n",
+    "    faces = faceCascade.detectMultiScale(\n",
+    "        image,\n",
+    "        scaleFactor=1.1,\n",
+    "        minNeighbors=5,\n",
+    "        minSize=(30, 30)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    return faces\n",
+    "\n",
+    "def draw_squares(image, faces):\n",
+    "    for (x, y, w, h) in faces:\n",
+    "        cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
+    "    return image\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cass = '../data/haarcascade_frontalface_default.xml'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abba_dat = '../data/abba.png'\n",
+    "abba_img = cv2.imread(abba_dat,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(426, 500)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(abba_img,cmap='gray')\n",
+    "abba_img.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "faces = face_detect(abba_img,cass)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[168,  70,  66,  66],\n",
+       "       [ 56,  55,  75,  75],\n",
+       "       [267,  69,  87,  87],\n",
+       "       [354,  78,  74,  74]], dtype=int32)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "faces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_img = draw_squares(img,faces)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1c38bcfaf48>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(new_img,cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1c38854e788>"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "me = '../../../../Downloads/wow.png'\n",
+    "me_img = cv2.imread(me,0)\n",
+    "plt.imshow(me_img)\n",
+    "faces = face_detect(me_img,cass)\n",
+    "new_img = draw_squares(me_img,faces)\n",
+    "plt.imshow(new_img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1c388826d88>"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "me = '../../../../Downloads/vescovi1.jpg'\n",
+    "me_img = cv2.imread(me,0)\n",
+    "plt.imshow(me_img)\n",
+    "faces = face_detect(me_img,cass)\n",
+    "new_img = draw_squares(me_img,faces)\n",
+    "plt.imshow(new_img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1c3887c9848>"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": []
   },
   {
diff --git a/lectures/Lec8.ipynb.txt b/lectures/Lec8.ipynb.txt
new file mode 100644
index 0000000..e69de29
diff --git a/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb b/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb
index 0ce5e8c..65d263e 100644
--- a/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb	
+++ b/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb	
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,12 +46,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -64,10 +64,11 @@
    ],
    "source": [
     "x = np.linspace(0, 2, 100)\n",
-    "plt.plot(x, x, label='line')\n",
-    "plt.xlabel('x label')\n",
-    "plt.ylabel('y label')\n",
-    "plt.title(\"Simple Plot\")\n",
+    "plt.plot(x, x, label='Raf Line')\n",
+    "\n",
+    "plt.xlabel('X label')\n",
+    "plt.ylabel('Y label')\n",
+    "#plt.title(\"Simple Plot\")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
@@ -81,22 +82,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0d0b63210>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9bca048>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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PLyGvpB4To4Kx5tko+Pm4WUW2J8VchjFHLqNLvlevXpgwYQL8/f0BADNmzEBaWhqcnH68NlelUkEulxv0daurm6DXC4PzyGTW+bokcxmGuYB2rQ6HzhZA8f1deHm44LVnhmNMhBxaTTtUqnZJsxmCuQxjbC5HR4fHnhwb/Tv5adOm4cyZM2hoaIBOp8Pp06eRkJCA/Px8FBYWQqfT4ciRI4iNjTV2F0R253ZxPf5PygV8fa4QEyID8fuV4zAmwrATJaL/ZPSZfHR0NFauXIklS5agvb0dkyZNwuLFizFo0CCsXbsWGo0GcXFxSEhIMGVeIpukbtPiy5N5OJ5VDH8fN7z1fDSGD+JAMeq6Ll0nv3DhQixcuPCB+yZMmIBDhw51KRSRPbmeX41dihzUNKjx9Og+SI4dxIFiZDJcSUQSaWptx+fHc3H2WjmC/D2wftkohPXhQDEyLZY8kQSyciqxJ+MWmlraMWfCvYFiLs4cKEamx5InsqD6Jg0++eYWsnJU6Bfohbeej0a/QOu8nI9sA0ueyAKEEDh77d5AsTatHgvi7g0Uc3Lk0BkyL5Y8kZlV1bViV5oSNwpqEdanJ15KjEBwgKfUschOsOSJzEQvBI5nFePLk3mAA7B05hBMGxUCR44kIAtiyROZQWnVvYFit0vqMXyQP1bERyCg58+PJCAyJ5Y8kQlpdXoo/nUXh8/mw9XFiQPFSHIseSITKSxvxD+OZqOosgljI+RYMnMIenr2kDoW2TmWPFEXtWt1OHimAGn/ugtvDxf8MnkERg2Rdf5EIgtgyRN1wa2iOqQolKioacHkqGC88PRgeLq5SB2LqANLnsgIrRotvjx5B8d/KEGvnm74zQsxiBzoL3Usooew5IkMdPVONXanK1HboMHMMX2RHDsIrj04koCsE0ue6Ak1tbbj02O5OHejHMEBHvjdi6MxOKSn1LGIHoslT9QJIQTOXCnBX/ZdQbNai6SJAzB34gC4OHMkAVk/ljzRY9Q1abAnPQeXcqvQP8gbb70QwYFi1K2w5Il+hhACp6+W4fPjt6HV6fHy3GGYOEzOgWLU7bDkiX6isq4VuxRKZBfWYkhfX7ycGIHh4YFW+eHPRJ1hyRP9m14vcCyrGPtP3YGjgwNejA9HXExvDhSjbo0lTwSgRNWEFIUSeaUNiAoNwPL4cPj7cKAYdX8sebJrWp0eR88V4nBmAdxdnbEqaRjGDQvkQDGyGSx5slv5ZQ1IOZqNYlUznhoqx5IZQ+DDgWJkY1jyZHc07TocPJ2P9At30dOzB9YuGIGRYRwoRraJJU92JeduLVIUSlTWtiI2ujeenxYKDw4UIxvGkie70KrR4ovv7uC7SyWQ+bph3aIYDB3AgWJk+1jyZPOu3K7C7vQc1DVpEP9UXzwzmQPFyH6w5MlmNbS04bNjufj+ZgVCZJ54/dkRGNTbR+pYRBbFkiebI4TA+exK7P3mFlo1WsybdG+gmLMTRxKQ/WHJk02pbbw3UOzy7SoMDPbBy7Mj0EfmJXUsIsmw5MkmCCFw6kopUk/chk4n8Py0wZg1ti8cHfmmJrJvLHnq9iprW7BToYTybh0i+vnipcQIyP08pI5FZBW6XPL/9V//hdraWnz44YfIzs7Gxo0b0dzcjDFjxuDdd9+FszN/jpB56PUCGReK8NXpPDg5OWBFQjhio3tzJAHRf+jSb6LOnTuHAwcOdNxet24dNm/ejPT0dAghkJqa2uWARD+nuLIJW/dcROqJ2xg2wB+/XzkecTEhLHiinzC65Ovq6rB9+3asWbMGAFBSUgK1Wo2YmBgAQHJyMtLS0kyTkujftDo9vjqdh3d3XkBVvRpr5kdi7YIR8PN2lToakVUy+rWUzZs3480330RZWRkAoLKyEjLZj/M/ZDIZKioqup6Q6N/yShuQoshGiaoZ4yMDsXh6GLw9OFCM6HGMKvkvvvgCwcHBmDBhAvbv3w8A0Ov1D/xTWQhh1D+dAwKMv9xNJrPOz95kLsP8NJe6TYu9aUocOnUH/j5u2PyLcRg7LEjyXNbEWrMxl2HMkcuokj969ChUKhXmz5+P+vp6tLS0wMHBASqVquMxVVVVkMvlBn/t6uom6PXC4OfJZN5W+fFszGWYn+bKLqzFTkU2VHVqTB0ZguemhsLd1dni2a31eAHWm425DGNsLkdHh8eeHBtV8ikpKR1/3r9/P86fP48PPvgAc+fORVZWFkaPHo2DBw8iNjbWmC9PhBa1FqknbuPUlVLI/dzx2yUjEd7PT+pYRN2OSa9v3LZtGzZt2oSmpiZERkZi+fLlpvzyZCcu51Zhd7oS9c1tSBzXD/MnD0QPFw4UIzJGl0s+OTkZycnJAICIiAjs27evy6HIPjU0t2Fn2kWculyCPjJPrF0QhYHBHChG1BV8pxJJTgiB729W4NNjuVC36fDslIFIHN+fA8WITIAlT5KqaVBjd3oOrt6pRmhvH7y1dDTcnfiGJiJTYcmTJPRC4OTlUnxx4jb0QmDx9DBMH90HgYE+VnnlA1F3xZIni6uoaUGKQolbRXUYNsAPKxIiIPN1lzoWkU1iyZPF6PR6ZJwvwldn8uHs5IiXEyMwOSqY82aIzIglTxZRVNmEfxzNRmF5I0aG9cKyWeGcN0NkASx5Mqt2rR6HMwug+L4Qnm7OePWZ4RgTLuPZO5GFsOTJbG6X1CPlaDbKqlswcXgQFk0Pg5e7i9SxiOwKS55MTtOmw5en7uDbi8Xw83HFr5+LRlRogNSxiOwSS55M6kZBDXYplKiqV2PaqBAsjLs3UIyIpMHvPjKJFnU7Pjt+G2euliHQzx3rl47CkL6+UscisnsseeqyH26psCcjB43N7Zg9vj/mTRrAgWJEVoIlT0arb27D3m9u4aKyEv3kXvj1wmj0D7LOD2MgslcseTKYEAKZ18vx2be50LTrkBw7CAnj+nGgGJEVYsmTQarr1diVrsT1vBoMDumJl2dHIDjAU+pYRPQILHl6InohcOKHEuw7eQcQwJIZYXh6dB848k1NRFaNJU+dKqtuxk6FErnF9Ygc6I8V8eHoxYFiRN0CS54eSafXI+1fd3HwTAFcXRzxyuyhmDQiiCMJiLoRljz9rMLyRqQosnG3ogmjw2VYNnMIenpxoBhRd8OSpwe0a3U4dLYAiu/vwsvDBa89MxxjIuRSxyIiI7HkqUNucR1SjipRXtOCSSOC8MLTHChG1N2x5AnqNi2+PJmH41nF8Pdxw1svRGP4QA4UI7IFLHk7dz2/GrsUOahpUOPp0X2wIG4Q3HpwWRDZCn4326nGljZ8/PVNnL1WjiB/D6xfNgphfThQjMjWsOTt0EVlJf75bS4amtowd2J/JE0cABdnDhQjskUseTtS16TB3oxbyLqlwqCQnvj1wij0C+RAMSJbxpK3A0IInL12b6BYm1aPhVNDsWz2MNTUNEsdjYjMjCVv46rqWrErTYkbBbUI69MTLyXeGyjmxImRRHaBJW+j9HqBb38oxv6TeYADsGzWEEwdGcKBYkR2hiVvg0qrmpGiyMadkgYMH+SPFfERCOjpJnUsIpIAS96GaHV6KP51F4fP5sPVxQkr5w7FhEgOFCOyZ10q+R07dkChUAAA4uLi8M477yAzMxMffPABNBoNEhMT8eabb5okKD1eQXkD/vG1EsWqJoyNkGPJzCHo6dlD6lhEJDGjSz4zMxNnzpzBgQMH4ODggJUrV+LIkSPYtm0b9uzZg+DgYKxevRonT55EXFycKTPTf2hr1+HgmXykny+Ct6cLfpk8AqOGyKSORURWwuiSl8lkWL9+PXr0uHe2GBoaioKCAvTv3x99+/YFACQlJSEtLY0lbyY5d2uxU6FERW0rYqOD8fy0wfBw40AxIvqR0SUfFhbW8eeCggIoFAosW7YMMtmPZ5FyuRwVFRUGfd2AAC9jI0Ems8439pg6V4u6HTu/vglFZgEC/T3w+9UTEW3E2bu9HC9TsdZcgPVmYy7DmCNXl3/xmpubi9WrV+Odd96Bk5MTCgoKOrYJIQz+pV91dRP0emFwDpnMGypVo8HPMzdT57p6pwq703NQ26DBrLF98eyUQXDt4WTwPuzleJmKteYCrDcbcxnG2FyOjg6PPTnuUslnZWXhV7/6FTZs2IA5c+bg/PnzUKlUHdtVKhXkcn7ghCk0trThs29zce5GBXr38sSGF4cjNKSn1LGIyMoZXfJlZWV4/fXXsX37dkyYMAEAEB0djfz8fBQWFqJPnz44cuQIFixYYLKw9kgIgQvKSuz95hZa1FrMmzQAcyYMgIsz37FKRJ0zuuQ//vhjaDQafPjhhx33LVq0CB9++CHWrl0LjUaDuLg4JCQkmCSoPapt1OCTjBxcyq3CgCBvvL1oKPrKjf+dBRHZH6NLftOmTdi0adPPbjt06JDRgeje2fvpq2X4/PhtaHV6PD9tMGaO7QMnR569E5Fh+I5XK1NZ14pdCiWyC2sR3tcXL82OQKCfh9SxiKibYslbCb1e4FhWMfafugNHBwe8GB+OuJjeHChGRF3CkrcCJaompCiUyCttQFRoAJbHh8PfhwPFiKjrWPIS0ur0OHquEIczC+Du6oxVScMwblggB4oRkcmw5CWSX9aAlKPZKFY1Y9ywQCyeEQYfDw4UIyLTYslbmKZdh4On85F+4S58vVzxqwVRiAnrJXUsIrJRLHkLUhbWYmeaEpW1rYiL6Y3npg6Ghxv/CojIfNgwFtCq0eKLE7fx3eVSyH3dsW7xSAzt7yd1LCKyAyx5M7twsxz/L/Uy6po0iH+qL56ZMgiuLk5SxyIiO8GSN5PGljZ8eiwX39+sQIjME68/OwKDevtIHYuI7AxL3sSEEDiffW+gWKtGiyWzwjE1OhjOThxJQESWx5I3oZoGNT7JuIXLt6swMNgHL8+OwMhhwVY5u5qI7ANL3gT0QuDUlVJ8ceI2dDqBRU8PxowxfeHoyDc1EZG0WPJdVFnbgp0KJZR36xDRzxcvJUZAzoFiRGQlWPJG0un1+OZCMb46nQcnJwe8lBiBKVHBHElARFaFJW+E4sompCiykV/WiJjBvfBifDj8vF2ljkVE9BCWvAHatXp8fa4AX58rhIebM9bMj8TYCDnP3onIarHkn9Cd0nrsPKpESVUzJkQGYtH0MHhzoBgRWTmWfCc0bTocOJ2Hby4UwdfbFW8sjEL0YA4UI6LugSX/GDcLarBToURVvRrTRoZg4dRQuLvykBFR98HG+hkt6naknriNU1fKIPdzx2+XjER4Pw4UI6LuhyX/E5duqbA7IwcNzW1IHNcP8ycPRA8OFCOibool/28NzW3457FbOJ9diT4yT/xqQRQGBnOgGBF1b3Zf8kIIfH+zAp8ey4W6TYtnpwxE4vj+HChGRDbBrku+pkGN3ek5uHqnGqG9ffDS7KEI6eUpdSwiIpOxy5LXC4GTl0qQ+t0dCCGweHoYpo/uw4FiRGRz7K7ky2vuDRS7VVSHYQP8sCIhAjJfd6ljERGZhd2UvE6vR8b5Inx1Jh8uTo54eXYEJo/gQDEism12UfJ3KxqRolCisLwRI8PuDRTz9eJAMSKyfTZd8u1aHQ5nFkDx/V14ujnjtWeGY3S4jGfvRGQ3bLbkbxfXI0WRjbLqFkwcHoRF08Pg5e4idSwiIosyS8kfPnwYf/nLX6DVarFixQosXbrUHLv5Weo2LfafzMO3WcXw93HFW89HY/igAIvtn4jImpi85CsqKrB9+3bs378fPXr0wKJFizBu3DgMHjzY1Lt6yI38GuxKuzdQ7OlRIVgQx4FiRGTfTP62zszMTIwfPx6+vr7w8PBAfHw80tLSTL2bB2jadPifzy7h/35+Gc5Ojli/dBSWzQpnwROR3TN5C1ZWVkImk3XclsvluHr16hM/PyDAy+B9nrtWhuNZRVj4dBgWzwq3uoFiMpm31BF+FnMZxlpzAdabjbkMY45cJi95vV7/wNUrQgiDrmaprm6CXi8M2uegQE98+n4imhvVqK9rMei55iaTeUOlapQ6xkOYyzDWmguw3mzMZRhjczk6Ojz25NjkL9cEBQVBpVJ13FapVJDL5abezQMcHRzg4cYrZ4iIfsrkJT9x4kScO3cONTU1aG1tRUZGBmJjY029GyIiegImf/NyJ1QAAAZ3SURBVLkmMDAQb775JpYvX4729nYsXLgQUVFRpt4NERE9AbNcfpKUlISkpCRzfGkiIjIAPxmDiMiGseSJiGwYS56IyIZZ3VtCu/LpTNb6yU7MZRjmMpy1ZmMuwxiTq7PnOAghDHvnERERdRt8uYaIyIax5ImIbBhLnojIhrHkiYhsGEueiMiGseSJiGwYS56IyIax5ImIbBhLnojIhnWLkj98+DBmz56NWbNmYe/evQ9tz87ORnJyMuLj47Fx40ZotVoAQGlpKZYuXYqEhAS8+uqraG5utmiuY8eOYf78+Zg3bx5ee+011NfXAwAOHDiAyZMnY/78+Zg/fz62b99u0Vw7duzAtGnTOvZ//zGPOo6WyJWdnd2RZ/78+ZgyZQrmzp0LwPzHCwCampowd+5cFBcXP7RNqvXVWS6p1ldnuaRaX4/LJeX62rFjB+bMmYM5c+bgD3/4w0Pbzb6+hJUrLy8X06ZNE7W1taK5uVkkJSWJ3NzcBx4zZ84ccenSJSGEEL/73e/E3r17hRBCrFq1Shw5ckQIIcSOHTvEH/7wB4vlamxsFJMmTRLl5eVCCCH++Mc/ivfff18IIcR7770nDh8+bLIshuQSQojVq1eLH3744aHnPuo4WirXfS0tLWLOnDniwoULQgjzHi8hhLh8+bKYO3euiIyMFEVFRQ9tl2J9dZZLqvXVWS4hpFlfT5LrPkuur7Nnz4oXXnhBaDQa0dbWJpYvXy4yMjIeeIy515fVn8lnZmZi/Pjx8PX1hYeHB+Lj45GWltaxvaSkBGq1GjExMQCA5ORkpKWlob29HRcuXEB8fPwD91sqV3t7O7Zs2YLAwEAAQHh4OMrKygAA165dw4EDB5CUlIS333674wzMErkA4Pr16/jrX/+KpKQkvPfee9BoNI88jpbMdd9f//pXjB07FmPGjAFg3uMFAKmpqdiyZcvPfhaxVOurs1xSra/OcgHSrK8nyXWfJdeXTCbD+vXr0aNHD7i4uCA0NBSlpaUd2y2xvqy+5CsrKyGTyTpuy+VyVFRUPHK7TCZDRUUFamtr4eXlBWdn5wfut1QuPz8/zJw5EwCgVqvx0UcfYcaMGR1ZXnvtNRw6dAjBwcF47733LJarubkZQ4cOxbp163DgwAE0NDTgz3/+8yOPo6Vy3dfY2IjU1FT88pe/fCCLuY4XAGzdurXjG76z3JZaX53lkmp9dZZLqvXVWa77LL2+wsLCOgq8oKAACoUCcXFxHdstsb6sbtTwT+n1ejg4/DhKUwjxwO1Hbf/p4wA8dNucue5rbGzE66+/joiICDz77LMAgD/96U8d21euXNnxzWqJXJ6envjb3/7WcfuVV17Bhg0bEBsb+0T/P+bKdd+hQ4cwY8YMBAQEdNxnzuPVGanW15Oy9PrqjFTr60lJtb5yc3OxevVqvPPOOxgwYEDH/ZZYX1Z/Jh8UFASVStVxW6VSPfDPsZ9ur6qqglwuh7+/PxobG6HT6X72eebOBdz7Kb1kyRKEh4dj69atAO59U+7cubPjMUIIODk5WSxXaWkp9u3b98D+nZ2dH3kcLZXrvmPHjmH27Nkdt819vDoj1fp6ElKsr85Itb6elBTrKysrCy+99BJ+85vfdPwgvs8S68vqS37ixIk4d+4campq0NraioyMDMTGxnZsDwkJgaurK7KysgAABw8eRGxsLFxcXDBmzBgcPXoUAPDVV1898Dxz59LpdFizZg0SExOxcePGjp/CHh4e+Pvf/44rV64AAD755BOTnjl0lsvNzQ3//d//jaKiIgghsHfvXsycOfORx9FSuYB732A3btzAyJEjO+4z9/HqjFTrqzNSra/OSLW+noQU66usrAyvv/46tm3bhjlz5jy03SLry6hf11rYoUOHxJw5c8SsWbPERx99JIQQYuXKleLq1atCCCGys7PFggULRHx8vHjrrbeERqMRQghRXFwsli1bJhITE8Urr7wi6urqLJYrIyNDhIeHi3nz5nX8t2HDBiGEEBcuXBDPPPOMSEhIEGvWrBENDQ0WyyWEEGlpaR3b169f33G8HnUcLZWrqqpKTJw48aHnmft43Tdt2rSOqzKsYX09LpeU6+txuYSQbn11lkuK9fX++++LmJiYB/6e/vnPf1p0ffGToYiIbJjVv1xDRETGY8kTEdkwljwRkQ1jyRMR2TCWPBGRDWPJExHZMJY8EZENY8kTEdmw/w9mH58bqCIGyQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -120,22 +121,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fe0c0b995d0>"
+       "<matplotlib.collections.PathCollection at 0x14ef9cb06c8>"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -153,22 +154,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0d107d110>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9cf5c88>"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -192,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,24 +202,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([12.,  7., 15.,  7., 10.,  7.,  6., 13., 14.,  9.]),\n",
+       "(array([13., 15., 10., 10.,  9.,  8., 11., 11.,  7.,  6.]),\n",
        " array([0. , 0.9, 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, 9. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -233,22 +234,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0c0a71b90>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef89d9148>"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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M74UrnPxZBzdzeSsTn88bc0fZ5SEdjUYDnU7nXNbpdFCrXY+zEhwcjP7+fti//2Fy935k4vUNWNCkNSB+WiCEAu6emTMefP7NC7Wy75uO0EApztRp8beyRrR3GdmORghnuPxtT01NRXl5OfR6PUwmE0pLS5GWlubygUUiERYvXoyPP/4YAFBYWOjW/cjEq7veDT4PmB3NzfPu74ZSLsGqxVF4YGEEbHYGpWea8OW5VvQNTK6hGRiGgWnQhs5eE5q0Blxu6kZDcy+utfahWWtAZ68JFhtdi0DGx+UhnbCwMOzcuRN5eXmwWq1Yt24dkpKSsGXLFmzfvh3z588f9b579+7Fnj17cODAAYSHh+N3v/udR8OT8bNY7Who6cX08ADOXVXrKTweD9FhCkSE+uPiNT0uXtPjRns/ZkYEYN7MELbjjchgtKKj24iObhN6+gfRYxiEze76dFOZRAh1sB/CQ/wRpfKfkMlqyNTBYzh8EjMdw/cMRiDAl5U3AAC1jd04U6fF2mUxCAn0/IeAY1kQr3JOcQhM3HHp4UyDNly8pkf9jR7YHQzmzQzGmnujMTsmCPxhnyt54/VjGAadvWZUXe3CPy+0o11vxMD3p5WKRXwEK6QIUkigkIng7yeCn0SAQIUfzGYL7A4GNrsDRrMNfQMW6PsH0d5lhNliB48HRKnkiJsWiMhQf7c+M1syJwz+d/hHgos/6wA3c3HlGD7tDvgQhmFwqbkHoYFSr5c9m/wkQixOUCNxRjDqb/Tgamsf/u//noNKKcX9SRG4d44aYUF3fqaKKwzDQNdrRv31btQ39aD+Rje6+gYBABKRAGHBfkicIUNYsAxKuXjEolbIpej/8QHYwKGP32MYxNXWPlxp6UOT1oCQACmS40IQ4WbxE99Ahe9DdD0m9BosWDZPw3YUVvhJhEiOC8WmzATUNXbjq/Ot+PAfV/HhP64iSiXHgtgQpCRFIFQuhkR055O2my02NGkNaGzvR2NbH+qbeqD/vuDlfiIkRCuxemkQYjQKNGn777qQeTweghRS3DNbioVxKlxt7UPVlS6crGxBRKg/UhLDIPcT3dXXIFMDFb4PudTUC5GQj+ka9s8HZpNELERSbCiSYkOh7zPjfEMnzjd04pN/Xsffy6+Dz+dBEyxDpMofaqUfggKkCPQXQyoWOP8QOBgGg1Y7Bkw29Bst6Oozo7PXjBadAVq9CbcORAbIxJgVGYCVi6chLioQmmCZs+AdDNCs8+yQz3w+D7FRgZgREYBLN3rw3WUdPvr6GhbFqzA7Wkl7+z6OCt9HDFrsaGzvR1xUIGuzWXHFoNU+5LMEuZ8I980Px71zwtBvsqGxrRfd/YO4eFWPM25OqM7jAf5SEZQKCZJiQxASIEVwgHTIB+PNWsOQMf0XTODcAwI+D3OmB2FamBz/vNiBb2u16Og2IXWexudff19Ghe8jrrb2weFgEBcV6HpjHyUS8hETHoBghdh5m93uwIDZBtOgDVa7A1abAzzcPIwiFPAhEQsgFQkgkwo5OR6R3E+ElfdE4uI1Pb671Inu/kH8dFEkAvzFru9MphwqfB/R0NKLkMCbe53EfQIBHwH+4kldkDweD/NmhiA00A9fnmtFyTc38NN7ohDqQx/ck5vovZ0PuNHej+7+QcRGTr5B0ojnaEJkWL00GkIBH6Xf3kBrJw1X7muo8H1AWXUr+DxguoYK39cFym/OHqaQiXHqbAvqb3SzHYl4ERX+FGd3OPDNxXZEqeWQiO/8VEMydcikQjy4JAoKmQhvF12k0vchVPhT3IWrevQNWDArkj6sJT+QioV4cMk0hARI8fqxKlxv59aVqWRiUOFPcWUX2iGXiRAR6s92FMIxfhIhnnx0PvylQrx+7DzNEewDqPCnMKPZhu8ud2LpXA0EHDxlkLAvUC7BzscWwGJ14PWj52E0W9mORCYQFf4UdvaSDja7AynzwtmOQjgsUiXHk7nz0a434u2PakYdsJBMflT4U9g3tR1QKaWYMQnnrCXeNScmCBsejEf11S4Ufn2N7ThkglDhT1F9AxbUNnbj3jlhNH4KccsDyRH4SVI4/lbWiMp6nes7kEmHCn+KqqjXwsEwWDo3jO0oZJLg8Xj4eXo8ZoQH4M8f10DbY2I7EvEwKvwp6puaDkSG+iNKNfpkCIQMJxIKsC0nEQAPbxddhM3uYDsS8SC3Cr+4uBiZmZlIT09HQUHBbetra2uRm5uLjIwMPPfcc7DZbo4w2NzcjI0bNyInJwe/+MUv0NLS4tn0ZET6PjMuN/fiXtq7J3cgVOmHX65JwLW2Pnz4j6tsxyEe5LLwOzo6kJ+fj0OHDqGwsBCHDx9GQ0PDkG127dqFF198ESdOnADDMDhy5AgA4L/+67+wdu1aFBUVIT09Hfn5+RPzLMgQ39ZqAQBL56hZTkImq8UJajyQHIFPvrmBi416tuMQD3FZ+GVlZUhJSYFSqYRMJkNGRgZKSkqc61taWmA2m5GcnAwAyM3Nda53OBwwGG6O/20ymSCV0uh83lB5SYvoMDnUEzhtH5n6/mVlHDTBMrzzcS2MZvfmBSDc5rLwtVotVKofJmpQq9Xo6OgYdb1KpXKu37FjB95991385Cc/wZ///Gds2bLFk9nJCLr7B3GlpQ/3zKa9e3J3JCIB/jVrDrr7B/HBZ5fYjkM8wOV4+A6HY8hpfQzDDFkea/3TTz+Nf//3f8eqVatw4sQJPPnkk/joo4/cPk1wrNnXgZszwXMRm7m+vdQJAHgwZbozh1ZvhELO/rsrkUh4Ww42co2U48e8lclVjuEmIpdMJoEqePR3giqVAo+19uPIZ5fwwJLo2y7io99B93Ehk8vC12g0qKiocC7rdDqo1eoh63W6H87Z7ezshFqthl6vx9WrV7Fq1SoAQEZGBvbu3Yvu7m4EBwe7Fa6ryzDqVX8qlQI6HfcGfGI71z/ONiE8RAYpHz/kEAjQb2B/nBSr1TYkh0IuZSXX8Bw/5s1MY+UYbqJyGY2D0NntY26zamEEyqta8Yej5xChlMJPcrM22P5ZHw0Xc3krE5/PG3NH2eUhndTUVJSXl0Ov18NkMqG0tBRpaWnO9ZGRkZBIJKisrAQAFBUVIS0tDUFBQZBIJM4/FpWVlfD393e77Mn4GUxW1F3vwaIJnCuV+B6hgI9NqxPQa7Dg+Jd01s5k5nIPPywsDDt37kReXh6sVivWrVuHpKQkbNmyBdu3b8f8+fOxf/9+PP/88zAYDEhMTEReXh54PB7efPNNvPzyyzCbzfD398cbb7zhjefks843dMLBMFT4xONmRgTgp/dE4fPKZiybp8FMGq5jUnJrTtvs7GxkZ2cPue3gwYPO/yckJODYsWO33S8pKQlHjx69y4jEXZX1OgQHSDBdw/6xQjL15KbNxNlLOvxPSR1e2LSY7TjkDtCVtlOE2WLDhWt6LIpX0dg5ZEL4SYTYsCoeTVoDPqtoZjsOuQNU+FNE9VU9bHYH7qHDOWQCLYoPRXJsKAq/vooOvZHtOGScqPCniLOXdFDIRIiLUrIdhUxhtwZY44GH/z5eBYahsfMnEyr8KcBqc+B8QycWxoWCTzNbkQkWHCDFI2kzUVHbQcMoTzJU+FNA7XU9zBY7FsXT1bXEO1bdE4Xp4QE4cqoBVtvY5/ET7nDrLB3CbZX1OvhJBJgTE8R2FDLJ8Pg8DAze2Tg5j/00Dv+3oBLF5deRcW/0XeWQiIQQ0u7nhKPCn+TsDge+u9yJBbNCIaLfGDJOg1Y7zl+6s8MyCrkU0WFylPzzOqQiAWTSO6+TJXPCIJRQHU00aohJ7nJTLwwmK11sRVhxz2wVHA7guzv8o0G8iwp/kqu8pINIyMf8mSFsRyE+SCETY870IFxp7UNnL02JyHVU+JMYwzA4e0mHeTOCIREL2I5DfFTSrBBIxQKcqdXSaZocR4U/iTW296O7f5AO5xBWiYR8LIxXQddjRmMbt0apJENR4U9ilfU6CPg8LIgNZTsK8XGxkQEIDpCg8pKOJj7nMCr8SYphGFTWa5EQrYTcT8R2HOLjeDweliSoYTTbUHu9m+04ZBRU+JNUa+cAOrpNWERTGRKOCAuWIUrljwtXb14ISLiHCn+SqrykAw/Awjg6nEO4Y2G8CjabAxeudrEdhYyACn+SOluvw6zIQCjlErajEOIUpJBgZmQA6q73wGCysh2HDEOFPwnpeky4oTXQ2TmEk5JjQwEecP5yJ9tRyDBU+JPQ2e+valw0mwqfcI+/nwhzYpS40tqH7v5BtuOQH3Gr8IuLi5GZmYn09HQUFBTctr62tha5ubnIyMjAc889B5vt5mBMWq0Wv/71r/Hwww9j/fr1aG6mWXI8ofKSDtFqOdRKP7ajEDKieTNCIBbynTsnhBtcFn5HRwfy8/Nx6NAhFBYW4vDhw2hoaBiyza5du/Diiy/ixIkTYBgGR44cAQDs3r0bK1asQGFhIXJycrB///6JeRY+pMcwiCvNvbR3TzhNIhZg3qwQtOgG0N5FM2NxhcvCLysrQ0pKCpRKJWQyGTIyMlBSUuJc39LSArPZjOTkZABAbm4uSkpKoNfrUVdXh/Xr1wMAHn30UfzmN7+ZoKfhO7673AkGoKkMCeclRCshkwpx9pKOhlzgCJfjkWq1WqhUP5SLWq1GVVXVqOtVKhU6OjrQ1NSEiIgI7Nu3DxUVFVCpVHjhhRfGFS4kRD7mepVKMa7H85aJzFV9TY+IUH8smKNxe7Jyrd4IhVw6YZncJRIJb8vBRq6RcvyYtzK5yjHcROQab4bhXN13aaIGpyqb0dVvwYyIwFG3k8kkUAXL7jjHcFzsBi5kcln4DodjSLEwDDNkebT1NpsNNTU1eOqpp/DMM8/g6NGj2LNnD95//323w3V1GeBwjLxnoFIpoNNxb9yOicw1YLaiuqET6fdOQ2enwf07CgToN5gnJNN4WK22ITkUcikruYbn+DFvZhorx3ATlWs8GYZzJ1NkiAwKmQjl1W0IUYhH3UkxGgehs3vmYi0udoO3MvH5vDF3lF0e0tFoNNDpfvjgRafTQa1Wj7q+s7MTarUaKpUK/v7+WLFiBQAgKytryDsDMn7nGzphdzC4h6YyJJME//uxnrr7B9HYzq0S9kUuCz81NRXl5eXQ6/UwmUwoLS1FWlqac31kZCQkEgkqKysBAEVFRUhLS0N0dDQ0Gg2+/PJLAMCpU6eQmJg4QU/DN1TW6xCkkGB6OPtvDQlx14xwBZRyMc43dI36jp14h8vCDwsLw86dO5GXl4eHH34YWVlZSEpKwpYtW1BdXQ0A2L9/P1577TWsXr0aRqMReXl5AIA33ngDf/zjH5GVlYX33nsPr7766sQ+myls0GLHhWt6LIpXge/msXtCuIDH4yE5LhR9AxZca+tjO45Pc2sSyezsbGRnZw+57eDBg87/JyQk4NixY7fdb+bMmeM6Zk9GV321C1abg87OIZPSNLUcwQESnG/owvTwAAj4tNPCBrrSdpI4e0kHuZ8IcdNGP9OBEK7i8XhYGBcKg8mKhuZetuP4LCr8ScBmd+D8lU4kx4VCwKeXjExOEaH+UCmlqLrSRZOksITaYxKovd4N06CdDueQSe3mXr4KpkEbLjX1sB3HJ1HhTwIVdVpIxQLMnR7EdhRC7oomRAZNiAwXruphtdFevrdR4XOcze7A2Us6JMeFQiQUsB2HkLu2MDYUZosddTQVotdR4XNc3Y1uDJhtWEJTGZIpQhXkh0iVPy5e08NipakQvYkKn+NuHc6ZNzOY7SiEeExyXCgsNgdqGmkv35uo8DnMZnegsl6H5Fg6nEOmlpAAKWLC5Khp1MNssbEdx2dQ4XPYrcM5ixPocA6ZehbEhcJmZ3DxGu3lewsVPodV1GlvTiQxgw7nkKlHKZdgRrgCdde70TdgYTuOT6DC56ibZ+d0Ijk2FGIRHc4hU9OC2FA4GAal395gO4pPoMLnqPobPTCYrFhCh3PIFBbgL8asyECcrm6Dvo/9ORumOip8jjpDh3OIj0iaFQKGAf5W1sh2lCmPCp+D7I7vL7aiwznEB8j9REidr8FXVW3Q9pjYjjOlUeFzUN33h3MW08VWxEdk3BsNPp+H4q+vsR1lSqPC56BbZ+fMp4utiI8IlH5M+u4AABafSURBVEuwYmEkyi62o61rgO04UxYVPsfYHTcvtlowK4QO5xCfkpkSA7FQgCLay58wbhV+cXExMjMzkZ6ejoKCgtvW19bWIjc3FxkZGXjuuedgsw29cq6mpgbz5s3zTOIpro7OziE+KsBfjFWLo/BtrRbNWgPbcaYkl4Xf0dGB/Px8HDp0CIWFhTh8+DAaGhqGbLNr1y68+OKLOHHiBBiGwZEjR5zrTCYTXn75ZVitVs+nn4L+ebEdfhIBkmaFsB2FEK/LuDcafhIBCmkvf0K4LPyysjKkpKRAqVRCJpMhIyMDJSUlzvUtLS0wm81ITk4GAOTm5g5Zv2/fPmzatGkCok89FqsdlfU63BOvprFziE+S+4mQsSQaZy/p0NhOE557msvC12q1UKl+mGlJrVajo6Nj1PUqlcq5/uTJkzCbzVi9erUnM09Z5xo6YbbYsSwxjO0ohLDmwSXT4C8V4sN/0F6+pwldbeBwOMDj/TDDPMMwQ5ZHW6/T6XDgwAG8++67dxwuJEQ+5nqVSnHHjz2R7jTXd8U1CA6Q4r57oiHg81zfwU1avREKudRjj3enRCLhbTnYyDVSjh/zViZXOYabiFzjzTCcpzLJZBKogmXO5XUr4/E/f6+BzmDB3BnjP7zJxW7gQiaXha/RaFBRUeFc1ul0UKvVQ9brdDrncmdnJ9RqNb744gv09PRg48aNznU5OTkoKCiAXD52kd/S1WWAw8GMuE6lUkCn63frcbzpTnMZTFZU1HZg1eIo6Ls8/IGVQIB+A/uXrVuttiE5FHIpK7mG5/gxb2YaK8dwE5VrPBmG82Qmo3EQOvsPk6GkzFah8Asx/lRYjac3LhqyU+kKF7vBW5n4fN6YO8ouD+mkpqaivLwcer0eJpMJpaWlSEtLc66PjIyERCJBZWUlAKCoqAhpaWl47LHH8Nlnn6GoqAhFRUXOde6Wva+pqNPC7mCQMlfDdhRCWCcRC/DQfdNxqbkXVVe62I4zZbgs/LCwMOzcuRN5eXl4+OGHkZWVhaSkJGzZsgXV1dUAgP379+O1117D6tWrYTQakZeXN+HBp5qyC+2ICPVHdBj9QSQEAH6yIALqID8c+/LKqO/0yfi4PKQDANnZ2cjOzh5y28GDB53/T0hIwLFjx8Z8jPr6+juI5xvaugbQ0NKLx1bMGtdbV0KmMqGAj9y0mfjvoosov9iO++aHsx1p0qMrbTngdHU7+DweUhPpcA4hP7Y4QY2YMAUKv7oGq83BdpxJjwqfZQ4Hg7ILbZg/MxiBcgnbcQjhFD6Ph3UPzEJXnxmnvmthO86kR4XPsgvX9OgxWHB/Er1dJWQkiTOCMScmCH8ra4RpkCY8vxtU+Cz7uroNcj8RFsSGsh2FEM5a98AsGExWlHxDUyHeDSp8FvUbLTh3WYeUxDAIBfRSEDKaGeEBWJygRumZJvQYBtmOM2lRy7Do6+o22OwMli+IYDsKIZz36PKZsNkdOP6Pq2xHmbSo8FniYBh8+V0r4qMCEamic+8JcSUsSIYHF0/D6ao2XG/n1pW0kwUVPktqGvXQ9pjwwKJItqMQMmlkpU6HXCbCB59dAsPQxVjjRYXPklNnW6CQiXBPPE10Qoi7ZFIhHkmbiUvNvaio17m+AxmCCp8F+j4zzjV04v6kcIiE9BIQMh5pSRGIUslx5PMGWG1213cgTtQ2LPjiXCvAAA8k0+EcQsaLz+fh8VVx6Ooz48S3TWzHmVSo8L3MYrXji+9akBwXCpXSj+04hExKc2KCsChehb+XX0d3P52m6S4qfC8ru9AOg8mK9CXT2I5CyKT2sxWzYHc4cPzLK2xHmTSo8L3IwTAoPdOEGI0C8dOUbMchZFJTB8nw4JJpOH2hHZebe9iOMylQ4XtR9ZUutOuNyLh3Gg2DTIgHZKdOR3CABO+fqIfNTqNpukKF70Unvr2BIIUEi2fTqZiEeIJULMSGVfFo1g3gs4pmtuNwHhW+lzQ096LuRg/Sl0yjcXMI8aCFcaFIjg1F0dfX0NXL/tzNXEbN4yUfnb4GhUxEp2IS4mE8Hg8bVsWBAYP3TtTTFbhjcKvwi4uLkZmZifT0dBQUFNy2vra2Frm5ucjIyMBzzz0Hm+3mmNWVlZVYt24dcnJysGnTJrS0+OYEBldae3Hhmh6r742GRCxgOw4hU06o0g+PLp+F6qtdOFVJh3ZG47LwOzo6kJ+fj0OHDqGwsBCHDx9GQ0PDkG127dqFF198ESdOnADDMDhy5Ijz9ldeeQVFRUXIzs7GK6+8MjHPguOKTzdC7ifCCho3h5AJs/KeKMRGBuJgYTV6aQjlEbks/LKyMqSkpECpVEImkyEjIwMlJSXO9S0tLTCbzUhOTgYA5ObmoqSkBBaLBTt27EBCQgIAYPbs2Whra5ugp8Fd19r6UHWlC+lLpkEqdmvOeELIHeDzePhlZgIGrXa8X0qDq43EZeFrtVqoVCrnslqtRkdHx6jrVSoVOjo6IBaLkZOTAwBwOBx48803sWrVKk9m5zyGYXDk8wYoZCKsvCeK7TiETHnhIf74+eo5OHtJh9PV7WzH4RyXu5wOh2PIOeMMwwxZdrXeYrFgz549sNls2Lp167jChYSMPU68SqUY1+N5y61c315sR31TD57ITUJ0VBBrebR6IxRyKWtf/xaRSHhbDjZyjZTjx7yVyVWO4SYi13gzDOepTDKZBKpgmUceK2e5HGdq2/HByctYlhwJTYi/Rx73bnGhr1wWvkajQUVFhXNZp9NBrVYPWa/T/TBMaWdnp3P9wMAAtm3bBqVSiQMHDkAkEo0rXFeXAQ7HyG/LVCoFdDruTYJwK5fd4cAfi6oRFizDolnB7GYVCNBvYP90NavVNiSHQi5lJdfwHD/mzUxj5RhuonKNJ8NwnsxkNA5CZ/fMyJcqlQJ56fHY++dv8dv3zuDpDQsh4LN7QqK3+orP5425o+zyu5Camory8nLo9XqYTCaUlpYiLS3NuT4yMhISiQSVlZUAgKKiIuf6Xbt2ISYmBq+//jrEYvHdPpdJ5avzbWjrMuKxB2bRefeEeFlooB9+nj4bDc29KPr6GttxOMPlHn5YWBh27tyJvLw8WK1WrFu3DklJSdiyZQu2b9+O+fPnY//+/Xj++edhMBiQmJiIvLw81NTU4OTJk4iNjcUjjzwC4Obx/4MHD074k2Jbn9GCv355BfHTlFgYF8p2HEJ80rJEDWqvd+PvZdcRH6XEvJkhbEdinVunjWRnZyM7O3vIbT8u7oSEBBw7dmzI+rlz56K+vt4DESefo583wGyx4xcZs2nMHEJYtPHBeDS29eH/Fdfg//xyCYID2P8si010rMHDqhp0OH2hHauXRiMylBsfFhHiqyQiAbY9PA9WuwN/+LAaFqtvz5BFhe9BFqsdbx07D5VSiuzU6WzHIYTg5qmav86ai8a2frz7SZ1Pn59Phe9BR041oEU3gLzVCRCLaAgFQrhiYbwKj6TNxD9rOvD38utsx2ENXfrpIecaOvH52RbkpM1C4vRgtuMQQoZZuywGrZ0DOP6PqwgOkCB1XjjbkbyO9vA9oHfAgnc+rkWUSo68zDlsxyGEjIDH4+GXmXMwJyYIf/57HaqudLIdyeuo8O+Sze7AgQ+rYbbYsfWhuXQohxAOEwn5eDJ3Pqap5Xjrwwuov9HNdiSvosK/CwzD4C+l9bjU3ItfZiYgUjX2UBCEEPb5SYT4zc8WICRQivyj51F33XdKnwr/LnxW0Yx/nG9DVmoMUuZq2I5DCHFToL8YuzcsQmigH14/eh4XG/VsR/IKKvw7dLq6Df978jIWxavw8E9msh2HEDJOgf5i7H58IdRBfnj9yHmUX5j6o2tS4d+Bb2s78OePazFnehC2PjQXfLqalpBJKcBfjD0bFyEuKhAH/1aD4tPXpvR5+lT443S6ug3/76MaxEUG4qncJIiE9CEtIZOZTCrCzp8lIyUxDB9+dQ1vfXgBRrON7VgTggrfTQzDoOjra/jT32sxO1qJHY8toPlpCZkiREI+tmTNxc9WxOK7y5349/85g+vt3Bt+/W5R4bvBNGjD2x9dRNHX13DfPA12/mwB/CR0zRohUwmPx8PqpdHYvWEhLFY7XnmvAoVfXYXN7mA7msdQa7lwtbUPb390AV29g8hNm4m1y2JoBExCprD4aUq8vHkpPvjsMj463YjKeh0eXxWHuVPgCnoq/FEYzVZ8+NU1fH62GcEKCZ7euBBxUUq2YxFCvMBfKsLmrLlYnKDGoU8vYf//nkNybChy02YiSj15r7ehwh/GYrXjq6o2FJ++hn6jFQ8sjMSjy2dCJh3f9IyEkMkvOTYUidOD8GlFM/5W1ogX//wtFsaFYk1KDGZFBEy6d/tU+N/rMQzidHUbPq1oRt+ABfFRgdj5s3jEaNifeJgQwh6RUIDMlBikLYjAycpmfHqmCd9d7kS0Wo7lCyOxeLYKCtnkmMLVpwvfYLKi6konKup0qLrSBQfDYO70IGTnJGJ2dBDb8QghHCL3EyHn/hlIXzIN39R04NR3LXj/RD0KSi9hzvQgJMeGYt6MYKiD/Di75+9W4RcXF+PAgQOw2WzYtGkTNm7cOGR9bW0tnnvuOQwMDGDx4sV46aWXIBQK0drail27dqGrqwszZszA/v374e/PzixQDgcDXY8JTVoDGlp6cbm5F43tfWAYQCkXI+Peabg/KRzhITRLFSFkdH4SIR5YGInlyRG40WHAmTotKuq0KPj0EgAgOECCWRGBmBURgIhQf2iCZQgO4cZxf5eF39HRgfz8fBw/fhxisRjr16/H0qVLERsb69xm165deOWVV5CcnIxnn30WR44cwYYNG/DSSy9hw4YNWLt2Lf7whz/grbfewq5duyb0CTkYBt9c7EBL5wC6+wfRY7j5r6vPDIv15ulVQgEfM8MVyFo2HclxoYjRKOhqWULIuPB4PMRoFIjRKPDo8pnQ9phw4aoel5p6cLW1F2fqtM5tRUI+1EF+UCv9IPcTwd9PBPn3/6TfX89zq4N4PB7mxARBJvX8ARiXj1hWVoaUlBQolTfPUMnIyEBJSQmefPJJAEBLSwvMZjOSk5MBALm5ufj973+Pxx57DGfOnMEf/vAH5+0///nPx1X4fP7YJTzSeqPJho+/vQ6HnUGATIwAuRgRof5QKiTQBPlBE+KP8BAZhIKJuwTBVW5vY/g8TnzoLBTwh+Twkwhht3k/1/AcP+bNTGPlGG6ico0nw3CezCQU8D36e+P930EewkP8ER7ijweXTANw85CxttuIzl4z+s12NLX3Qd9vRpveiAGzDfYxzu//6aIorF4aPe4Urp63y8LXarVQqVTOZbVajaqqqlHXq1QqdHR0oLu7G3K5HEKhcMjt4xEUNPbhlZAR3iaFAPjvp1eN6+t42ki52Lb2J7PYjgAAmBnFjc9GKAe3MngaF34HQwDEcOx763I31+FwDPkAgmGYIcujrR++HQDOfpBBCCG+wGXhazQa6HQ657JOp4NarR51fWdnJ9RqNYKDg9Hf3w+73T7i/QghhHiXy8JPTU1FeXk59Ho9TCYTSktLkZaW5lwfGRkJiUSCyspKAEBRURHS0tIgEomwePFifPzxxwCAwsLCIfcjhBDiXTzGjcGfi4uL8fbbb8NqtWLdunXYsmULtmzZgu3bt2P+/Pmoq6vD888/D4PBgMTERLz22msQi8VoaWnBnj170NXVhfDwcPzud79DYGCgN54XIYSQYdwqfEIIIZMfDY9MCCE+ggqfEEJ8BBU+IYT4CCp8QgjxEZO+8GtqajBv3jy2YzhVVlZi3bp1yMnJwaZNm9DS0sJqnuLiYmRmZiI9PR0FBQWsZrnlzTffxNq1a7F27Vr853/+J9txhvjtb3+LPXv2sB3D6fPPP0dubi7WrFmDV155he04TkVFRc7X8Le//S2rWQwGA7KystDc3Azg5nAw2dnZSE9PR35+PmdyHT58GFlZWcjOzsYzzzwDi8Xi/VDMJGY0Gpn169cz8fHxbEdxWrFiBVNbW8swDMMcPXqUeeKJJ1jL0t7ezqxYsYLp7u5mBgYGmOzsbOby5cus5WEYhjl9+jTzL//yL8zg4CBjsViYvLw8prS0lNVMt5SVlTFLly5lnn76abajMAzDMDdu3GDuv/9+pq2tjbFYLMzjjz/OfPHFF2zHYoxGI7NkyRKmq6uLsVqtzLp165jTp0+zkuXcuXNMVlYWk5iYyDQ1NTEmk4lZvnw5c+PGDcZqtTK/+tWvWPmeDc919epV5sEHH2T6+/sZh8PB7N69m3nnnXe8nmtS7+Hv27cPmzZtYjuGk8ViwY4dO5CQkAAAmD17Ntra2ljL8+OB72QymXPgOzapVCrs2bMHYrEYIpEIs2bNQmtrK6uZAKCnpwf5+fl44okn2I7i9OmnnyIzMxMajQYikQj5+flYsGAB27Fgt9vhcDhgMplgs9lgs9kgkUhYyXLkyBHs3bvXeRV/VVUVYmJiMG3aNAiFQmRnZ7PyMz88l1gsxt69eyGXy8Hj8RAfH8/Kz/2knQDl5MmTMJvNWL16NdtRnMRiMXJycgDcHGPozTffxKpV7A3k5mrgOzbExcU5/9/Y2IhPPvkEH3zwAYuJbnrxxRexc+dOVv9AD3f9+nWIRCI88cQTaGtrwwMPPIDf/OY3bMeCXC7Hjh07sGbNGvj5+WHJkiVYtGgRK1n+4z/+Y8jySD/z4x200ROG54qMjERkZCQAQK/Xo6CgAK+99prXc3G+8D/55JPbvjEzZ86EwWDAu+++y04ojJ7r3XffhcViwZ49e2Cz2bB161aWEroe+I5Nly9fxtatW7F7925Mnz6d1SxHjx5FeHg4li1bhuPHj7Oa5cfsdjsqKirw/vvvQyaTYdu2bfjwww+Rm5vLaq66ujr89a9/xalTp6BQKPBv//Zv+NOf/oTNmzezmgvg9s88cHN+kc2bN+PRRx/F0qVLvf71OV/4a9aswZo1a4bcdvToUbz99ttDZt7KyclBQUEB5HLvDIs6Ui4AGBgYwLZt26BUKnHgwAGIROyNQ6/RaFBRUeFc5soAdpWVldi+fTueffZZrF27lu04+Pjjj6HT6ZCTk4Pe3l4YjUa8+uqrePbZZ1nNFRoaimXLliE4OBgAsGrVKlRVVbFe+F9//TWWLVuGkJAQADfnujh06BAnCt/VYI9sunLlCjZv3oxf/OIX+NWvfsVOCK9/ajABuPSh7bZt25jnn3+esdvtbEdxfmjb1dXFGI1G5qGHHmLOnz/PaqbW1lZm6dKlTFlZGas5RvPXv/6VMx/anjt3jsnIyGB6e3sZm83GbN26lTly5AjbsZivvvqKeeihh5iBgQHG4XAwL7zwAvP73/+e1UwrVqxgmpqaGLPZzKSlpTGNjY2MzWZj/vVf/5X5+OOPWc/V39/PLF++nPnwww9Zy8IwDMP5PfzJpKamBidPnkRsbCweeeQRADePIR48eJCVPGFhYdi5cyfy8vKcA98lJSWxkuWWP/3pTxgcHMS+ffuct61fvx6PP/44i6m4acGCBdi8eTM2bNgAq9WK++67D48++ijbsXD//fejpqYGubm5EIlEmD9/Pn7961+zHQsAIJFIsG/fPjz11FMYHBzE8uXLOfE537Fjx9DZ2Yl33nkH77zzDgDgpz/9KXbs2OHVHDR4GiGE+IhJfVomIYQQ91HhE0KIj6DCJ4QQH0GFTwghPoIKnxBCfAQVPiGE+AgqfEII8RFU+IQQ4iP+PzdTOGnFLFewAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -263,22 +264,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0908e4250>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9a80208>"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -293,22 +294,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe09091c710>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9ac2bc8>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -323,22 +324,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0d0e58a50>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9ecf2c8>"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -360,22 +361,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0b0b1be10>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9fcf608>"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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YMNatWwfAmjVrmq0nIiLBw+eeQXx8PFlZWWRmZuJ2u5k6dSopKSnMmjWL+fPnc/ToUXbu3InH4+Htt98GYNCgQTz88MMsWrSI7Oxsnn76aRITE3n88cfbfINERKT1fJYBQHp6Ounp6c1ue+655wBITk5m9+7dJ12va9euvPzyy2cYUURE2po+gSwiIioDERFRGYiICCoDERFBZSAiIqgMREQElYGIiKAyEBERVAYiIoLKQEREUBmIiAgqAxERQWUgIiKoDEREBJWBiIigMhAREVQGIiKCykBERFAZiIgILSyDvLw8JkyYwLhx41i2bNkpl7vnnntYvXp103Rubi4jR44kIyODjIwMcnJyzjyxiIj4ndXXAkVFReTk5LB69WpCQ0OZNm0aw4cPJykpqdkyixYtYuPGjaSmpjbdvmPHDrKzs5k0aVLbpBcREb/wuWdQUFBAamoqMTEx2Gw20tLSyM/Pb7ZMXl4eV155JePHj292+/bt28nNzSU9PZ0FCxZQWVnp3/QiIuIXPvcMiouLsdvtTdMOh4Nt27Y1W2bmzJkAbN68udntdrudW2+9lSFDhvD444/z4IMP8rvf/c4fuaWNeMwN1Hsazngcq8fkhzTHmSzg8lT7ZSx/5hJpT3yWgdfrxWT6v39AhmE0m/4xS5cubfp55syZjB07ttUB4+KiWr1OW7PbowMd4QT+yuSsKWXv0X1nPE6fkF5ER4f7IREYZg97K77xy1j+ymWLDMUe6b/nQXt+TvmTMrUdn2WQkJDApk2bmqadTicOh8PnwFVVVaxatYpbbrkFOF4iFoul1QFLS6vxeo1Wr9dW7PZonM6qQMdoxp+ZXEYDVVV1Zz5QJ/wzDuCO9vhtLH/lckU34HT55z5v788pf1GmljGbTaf1ItrnOYMRI0awceNGysrKqK2tZf369YwaNcrnwDabjeeff54vvvgCgFdeeeW09gxERKTt+dwziI+PJysri8zMTNxuN1OnTiUlJYVZs2Yxf/58kpOTT7qexWLhiSeeYPHixdTV1dGzZ08effRRv2+AiIicOZ9lAJCenk56enqz25577rkTlnvkkUeaTQ8bNozc3NwziCciImeDPoEsIiIqAxERURmIiAgqAxERQWUgIiKoDEREBJWBiIigMhAREVQGIiKCykBERFAZiIgIKgMREUFlICIiqAxERASVgYiIoDIQERFUBiIigspARERQGYiICC38DuS8vDyefvppGhsbufnmm5kxY8ZJl7vnnntITU1lypQpABQWFrJw4UJKS0vp1asXjz32GJGRkf5LLyLNeLwevqsu5JvKgxx1FeN0lVBRX0mdp556TwNWs4UwSxgdQqNwRNhJiHSQFNOLHtHdsJgtgY4vAeSzDIqKisjJyWH16tWEhoYybdo0hg8fTlJSUrNlFi1axMaNG0lNTW26fcmSJUyfPp2JEyeydOlSnnrqKRYuXNg2WyJyniqtLWd76U6+LNnN3oqvcXvdAERYI3BEdCYhMp5waxhhllAavR7qPfVU1h9jV9lXfHp0EwDhlnAGde5PasIw+nVK+rFfJ+2UzzIoKCggNTWVmJgYANLS0sjPz+fOO+9sWiYvL48rr7yyaRkAt9vN559/ztKlSwGYMmUKN954o8qgHfMYXho8Dbi9bo5WFVNeV4HJZMJismA1WwmzhGE2mQIds12ocbv4x9EtbC7ayjfHDgLgiOjMiC4/ISmmF7069CAmrCMmH/d3VUM1e8r3s7tsL1ud29lUtJXYsBiuGTCOlA6DCbWEnI3NkSDgswyKi4ux2+1N0w6Hg23btjVbZubMmQBs3ry56bby8nKioqKwWo//CrvdTlFRkV9CS2DVN9ZTUldGRX0lFfWVVDfUUN3oosHT0LTM3745cT0TJiKs4f/6L4LIEBsxYR2ICetIx9AOOkzRAgePfceHhwvYXLQVt7eRrlGJZFw4nosdg3DY7L4H+IHo0CiGxg9maPxg/qPfNWwv2ckHhz7mhX+upEPoW0zoNZafdvkJZpNOL7Z3PsvA6/U2e3VhGIbPVxunWq4l6/1QXFxUq9dpa3Z7dKAjnMBfmZw1DURHhze7zeP1cKSqmG8rD1N4rIjyusqmedFhUcSERRPfoTORIRGEWcMItYTQNTqRohonhmHQ6G2kwevG1VBLjbsWV4OLanc1R2qK8Bge4HhRdAyPprOtEwlRdhKi7cSGH39lGxJiOSHTmfDHWLbIUOyR/nse/Njj1+Bx8+mhLby99wP2lh0gzBrG6F6XkZY0igtiuvktA0CX+J8ybsAIdjr3smL7Wv761Wo+L97MbUOnkRTX06+/63S05397geazDBISEti0aVPTtNPpxOFw+By4U6dOVFVV4fF4sFgsLV7vh0pLq/F6jVav11bs9miczqpAx2jGn5lcRgNVVXUYhoGztpRvjn3LoapC3F43FpMFe0QcKZ0HYo+IIzasIyGnOIwwOOEitnz7gxsjmk96DYNqdzUV9ceoqK+goq6S7yqPsK/sAAAh5hDsEZ1Iib8IPOYWHfbwqRNUVdWd2RiAK7oBp8s/9/mpHr/S2nI+LvyUgsJ/UO2uId5mZ2qfyaQmDiXCGgFu2uy5ONDRlzuTb2dT0VZy973Jfe8+ytU9r2R8zysDtgfX3v/t+YvZbDqtF9E+y2DEiBE8+eSTlJWVERERwfr163nooYd8DhwSEsKwYcNYt24d6enprFmzhlGjRrU6oJxd9Y0N7Cnfz56K/VQ1VGM1WegW3ZXu0V1IsDmwmlv0BrQWMZtMdAiNpkNoND2iuwLH9yhr3C6ctSU4a0tx1paS//UHAIRZwkiw2UmIjCfB5sAWEvEjo5+bvIaXr8r28eHhAnaU7AIgpfMARnUbQb/YpDMvw1YwmUxcmnAJgzr357U9a3nrwLvsLPuKWwfOoHNEp7OWQ84On/+y4+PjycrKIjMzE7fbzdSpU0lJSWHWrFnMnz+f5OTkU667aNEisrOzefrpp0lMTOTxxx/3a3jxH5fbxYZDH/H+dx9T11hPXHgswxOG0j26KyF+LABfTCYTUaGRRIVG0qvjBQD0iEvkg68/5WhNMUddxXxb9R0AHUKjSYh0kGBz4LDZz2pOf3O5XXx6dDMffbeR4toSokIiGXfBGEZ2HU6n8NiAZouwRpA54HoGdb6I5btX8ejnf+DWQTPo36lPQHOJf5kMwwieYzAnocNEvp1JpnpPA+8d/JD3Dn5EnaeOQfb+JEYknPErvyEXXMSWb3ed0RjfS+7Sl+2Fe4Djew4V9ZUcdRVztKYYZ20JHsOLGRNxEXEk2OzERzqIC4896UlPf+Ua0mUQNtOZnc/yGl72lO9nc9k/+cd3W2n0NtKrQw9GdRvBJY6UgJbbqZ5Txa4Snt3+F47WFHNdn3TGdB8Z8EyBFIyZ2uwwkbRPXsPLZ0e3kLc/n8qGY1xsH8SEXmOJjYxmS+GOQMc7JZPJRGx4DLHhMVzUqS8erwdnbSlHXcUU1RSzvXQX20t3YTVZcdg6E29zkBBpp2Noh7N6iOVUDMPgSE0R/yzexqdHN1NWV05kqI2fdvkJlyVeSvd/HS4LVg5bZxYMvYOXdq7g9b1rKasr59qkiXq3UTugMjgPHa4+wl+/Ws3Xld/Ss0MPbht0I71jegLgMqoDG66VLGbL8UNFkQ6wQ72nnmJXCUdriilyOSmsOQpOCLWEYg/vRIW3nIY6D53CY7GepROhHq+Hryu/ZVvJl2wr2UlJbSkmTPSLTSKj93iuvCiVyrIzP6l9toRbw5mZfBOv713LhkMfcayhipsu+g+/nk+Ss0+P3nnE4/Xw1oH3ePvbDURYw7nxov8gNWFoULxi9pcwSxjdo7s2vcKucbsochVT7CqlpK6Ut/a+D4AZEx3/9RmH7//rGNaBcEvYGd8ftY21HK4+yoFjB9lfcYD9Fd9Q0+jCarLQNzaJq3qMIrnzAGLCOgL864Nd504ZAJhNZn7eJ4OYsI68sf8t6hrrmTnoxlO+u0yCn8rgPFHsKuEvO//KgWMHuTR+CFP7pBMV2v6vExUZYuPCjj25sGNPAPolXsCGvZ/irC09fu6hprjpE7wAVpMVW0gEtn99KM5mjcBqtmI1W7CYLFjMFswmM9ZiM2bDistdS5W7mmP1xyipK8fpKqG8vqJpPHtEHMn2AQyM68+ATn0Jt/rv8xKBZjKZGHfBGCKs4fz1q1ye2f4Xbk++WZ9aPkepDNo5wzDYeORzXtu7FovJwq0DZzA0fnCgYwVMZKiNrlGJdI1KbLqtrrGeivpKKhuOUeN2UeN24Wp0UV5dSb2n/qTjfHT4/342cfwdUJ3D4+gd05MukQl0jUqke3Q3Ooa1jw8k/Zifdb0Mi8nK8t2v88y2F5kz+Bfn9Du7zld6xNoxl9vFst2vs9W5g74xvckccD2x4TG+VzzPhFvDSLD+67zDD3gMLx5vI42GB4/Xg8fw4DG8DHL0JdrSAZvVhi0k4rw/gTqiy6WYTCZe2bWSF79czq0DZ+jyIucYlUE7VVh9lGe2/4Xyugqu6T2BK3uMOu//YJ0Oi8mMxRJK6A9uT4yKP+O3lrY3lyUOo66xjtf3rmX57lXMuGiqnnPnEJVBO/SF80v+svNVQi2h3D1kdtPxcpG2Nqb7SGoba/nbN+8Qbg1jap/J7eoNCu2ZyqAd8Rpe3j6wgTe/Wc8F0d2ZlXyTDgvJWTe+51XUNtax4dBHRFgjmHThuEBHkhZQGbQT9Z4GXtq5gq3O7fwkYQjT+12nt/lJQJhMJqYkTaKusY63DrxLZIjtrH5SWU6PyqAdOFZfzR/++SzfHjvElKRJXNH9Z9o1l4AymUzc0P86ahprWbU3j9jwGC62Dwp0LPkROrtzjiutLeO/3vsfvqsuZGbyTVzZY5SKQIKC2WTmlgHTuKBDd1788lW+qTzoeyUJGJXBOey7qkIe27yUY3VV/PLiWXrlJUEn1BLKnJRb6Bgazf9ue4GS2tJAR5JTUBmco/aU7yNny/9iNpl58MoFJMX0CnQkkZOKDo1i3uBbMQyDp774MzVuV6AjyUmoDM5B25xfsnTrn4gN78iCoXfQvWOXQEcS+VHxkQ5uT7mZ0toyntn2F9wed6AjyQ+oDM4xW4q38dyOl+ka1YWsIXP11lE5ZyTF9CJzwPXsr/yGl3etxGt4Ax1J/o3eTXQO+cfRLby0cwW9Ol7AvMG3EtGOLnom54eh8RdTWlfOG/vfIi6iExm9xwc6kvyLyuAcUVD4D5bvXkWfmAuZnXIL4dawQEcSOS1je1xOSW0Z6799n7jwWEZ2TQ10JKGFh4ny8vKYMGEC48aNY9myZSfM37VrF1OmTCEtLY377ruPxsZGAHJzcxk5ciQZGRlkZGSQk5Pj3/Tnib9/V8Cy3a/Tv1Mf5g6+VUUg5zSTycT1fa9hQFw/VuxZw44S/3w9qpwZn2VQVFRETk4Oy5cvZ82aNaxYsYJ9+/Y1W2bhwoU88MADvP322xiGwcqVKwHYsWMH2dnZvPHGG7zxxhtkZWW1zVa0Y+8d/Dsr9qwhufNFzE65RdeKl3bBYrZw28Ab6RqVyJ92vMK3xw4FOtJ5z2cZFBQUkJqaSkxMDDabjbS0NPLz85vmHz58mLq6Oi6++GIApkyZ0jR/+/bt5Obmkp6ezoIFC6isrGyjzWif8g9sYPW+N7nEnszMQTfpGvHSroRbw5ibcivRoVE8/YU+gxBoPsuguLgYu93eNO1wOCgqKjrlfLvd3jTfbrczb9481q5dS2JiIg8++KA/s7dbhmHw5tdvk/d1PpfGX8IvBk7X98tKu9QxLJp5g2/DY3hY+sWfqHbXBDrSectnGXi93maXNzAMo9n0j81funQpQ4ce/47dmTNn8tFHH/kze7tkGAZr9q/jrQPvcVnipWQOuF5fEiLtWkKkg9kpt1BWV8Ez216kQZ9BCAifLzcTEhLYtGlT07TT6cThcDSb73Q6m6ZLSkpwOBxUVVWxatUqbrnlFuD4HzmLpfV/1OLigu8LROz2tvkqQ6/h5cV/vsa7Bz9kXO9R3Dr0+hZ/OYi/MjlrGoiO9s9bVv01TkiIxW9jgX9y2SJDsUf673nQVs+pM3E2M9ntKZjCf0FOwfO8uv81/r/LZmE2n/jcP4za2nwAABBoSURBVN/vp7bkswxGjBjBk08+SVlZGREREaxfv56HHnqoaX7Xrl0JCwtj8+bNDB06lDfeeINRo0Zhs9l4/vnnueSSSxg8eDCvvPIKY8eObXXA0tJqvF6j1eu1Fbs9Gqezyu/jeg0vf/1qNZ8U/oMruv+MyT0mUlrSsl1mf2ZyGQ1UVdWd+UCd8M84gDva47ex/JXLFd2A0+Wf+7ytnlNnIhCZeof3YUrSRFbte5NnNr7K1L6TA57Jl2DMZDabTutFtM8yiI+PJysri8zMTNxuN1OnTiUlJYVZs2Yxf/58kpOTeeyxx7j//vuprq5m4MCBZGZmYrFYeOKJJ1i8eDF1dXX07NmTRx999LQ2rr3zeD0s2/06nx3dTNoFV5B+YZquPCrnpSt6jKKsvoL3D31MbHgMV/YYFehI540WnZVMT08nPT292W3PPfdc08/9+/fn9ddfP2G9YcOGkZube4YR2ze3t5EXv3yVrc7tTOo1jvG9rgp0JJGAmpI0iYq6SlbvexNbiI3LEocFOtJ5QW9RCaB6TwPPbX+JXWV7uC5pElfoVZAIZpOZmwdMo3ZbHct2vUaI2cqw+IsDHavd04XqAqS2sZY/bn2e3WV7mdF/qopA5N+EWEKYnXIzvWN68pedf+UL545AR2r3VAYBUNVQze+3PMO3xw7xi4HTGdHlJ4GOJBJ0Qi2hzE35BT2iu/HnHcvYeuTLQEdq11QGZ1lpbRk5W/6Xo65iZqfczND4wYGOJBK0wq3h3DH4NhIj4/mfT55hT/k+3yvJaVEZnEXfHjvE/2z+I8caqrhj8EwGxvUPdCSRoGcLieDOi2cRH9mZp794gd1lewMdqV1SGZwl25xf8sSW/yXUHMKCofPoE3thoCOJnDOiQiN5YMzddI6I4+ltL7C9ZGegI7U7KoOz4INDn/Ds9pdIiIxnwbA7SYiMD3QkkXNOTHgH7h4yh66RiTy7/SX+cXRLoCO1KyqDNuTxenh9z1pe2/sGyZ0HcPeQOXQIbR8fXRcJhMgQG7+8ZBa9Ox5/l9H6b9/HMILnCgXnMn3OoI1U1Ffy5x3L2V/5DWO6jWRKn0ktvs6QiJxahDWcOy6eycs7V/DG/rcoq6vg530m64KOZ0hl0Ab2lO/jzzuWU++p5+YB0/hJwpBARxJpV0LMVm4ZeAOx4TG8e/BDimqKuS35RqJCIgMd7Zyll6p+5DW8vH1gA3/453PYQmzcc+l8FYFIGzGbzFybNJHMi67n68oDPPr5kxyqOhzoWOcs7Rn4SUV9Jct2v87O0q8Y6hjM9P5T9V3FImfB8MShOGx2nt/xMo9t+iNT+qQzqutluthjK6kMzpBhGGwu2sqKPWtwexu5vu81/ExPRJGzqlfHHvzq0rt5adcKVu5Zw+6yvdzQf4resNEKKoMzUFJbyoo9a9hZ+hW9OvTgpgHXE2+z+15RRPwuKjSSOSm3sOHQR+R9/Ta//ux3XN/3GoY4BuvFWQuoDE5DvaeB9w5+yPpv38dsMnNd0iQu7z5S7xYSCTCzycxVPUYzKK4/L+1cyZ+/XM7GI5v4ed8MvVDzQWXQCh6vhw1fF/DqF29Q2XCMi+3JTO2TTmx4TKCjici/SYiM5/8fOo+/H97Im1+v5+HPHmd0txGk9bxC7zg6BZVBC3i8Hj47uoW3D7xHSV0ZF3Tozq2DZpAU0yvQ0UTkFCxmC2O6j2SIYzBrv36L9w99TEHh51zR42dc3u2nRIbYAh0xqKgMfkRVQzWfFH7G37/bSGXDMXpEd+WeYXPoEdJLxyBFzhEdw6K56aL/4Koeo8n7+m3WffMO7x78kJ92+Qmjuo7AYesc6IhBQWXwAx6vh51lX/Hpkc1sL9mJx/BwUae+zOg+lQGd+uFwdAi6L8AWEd8SI+O5PTmTw9VHePfgh3z4XQHvH/qY/rF9uCxxGMn2gYRZQgMdM2BaVAZ5eXk8/fTTNDY2cvPNNzNjxoxm83ft2sV9991HTU0Nw4YNY8mSJVitVgoLC1m4cCGlpaX06tWLxx57jMjI4DteV91Qw56K/ewo2cW2kp3UNtYSFRLJ6G4jGNHlJyTqwnIi7UbXqERuHjCNjN7j2Vi4iU8KP+OFna8Sag5hUOeLGBDXn/6xSefduUCfZVBUVEROTg6rV68mNDSUadOmMXz4cJKSkpqWWbhwIb/+9a+5+OKLuffee1m5ciXTp09nyZIlTJ8+nYkTJ7J06VKeeuopFi5c2KYb5IthGFQ2HOPAsUPsKd/P3vL9FNYcBcBmjSCl8wAucSQzoFM/XetEpB2LCevI+F5XktZzDPsrvmFz8Ta2Fm9nS/E2AOJtdvrF9qFfbG+6RXclLjy2XR8e9lkGBQUFpKamEhNzvCXT0tLIz8/nzjvvBODw4cPU1dVx8cXHv7B6ypQp/OEPf+DnP/85n3/+OUuXLm26/cYbbzxrZVDbWEd5XQUV9ZWU1JZxpOYohTVHOVJdRE2jC4AQcwi9O/ZkaPzF9I3tzQXR3VQAIucZs8lMn9je9IntzfV9r6Gw5ii7y/ayu3wvnx75nL8fLgAg3BJOl6gEukUlkhgZT6fwWHqHdIXGECKsEQHeijPnswyKi4ux2//v/bkOh4Nt27adcr7dbqeoqIjy8nKioqKwWq3Nbj9de8u/xllbgtvbSKO38V//d1PbWIersZbaxlpc7lpq3C4q6iup89Q3Wz/CGk5iZAKXOJJJjEqge1RXLujQDatZp01E5DiTyUTXqES6RiVyZY9RNHobOVRVyOHqQg5XH+Fw9RH+cfSf1Hnqjq/wrz+FEdZwOoR2IDIkApvVRmSIDZs1gjBrGCFmK1azlVBzCFZzCBaTudnvCRY+/xJ6vd5mu0aGYTSbPtX8Hy4HnNYultl8fKzX9q2hwdNwwvxwaxjhlnDCreHERcTSrUMiHUKj6RjW4fj/Qzs0/eyvXTyzOfh2Ff2VyWJYsIWGn/k4Zv+MA2D141j+ymUxWzD78ZBBe35O+dPZzhRqDqF37AX0jr2g6TbDMKhyV1NZfwxPSAOHy4qpqK+k2u2i1l1LraeOsvoyDtfU0eBpwODk37eQGBnP3MG3+j3z6d5HPssgISGBTZs2NU07nU4cDkez+U6ns2m6pKQEh8NBp06dqKqqwuPxYLFYTlivpWJjj59wzpmwqNXrtpW4uKhARziB/zJF0a1z6x+nk+nW0X+venp27ua3sfyZy1/a93PKf4IlU2eigePPo6FdA5vFX3xeP2HEiBFs3LiRsrIyamtrWb9+PaNGjWqa37VrV8LCwti8eTMAb7zxBqNGjSIkJIRhw4axbt06ANasWdNsPRERCR4mowXfGZeXl8czzzyD2+1m6tSpzJo1i1mzZjF//nySk5PZvXs3999/P9XV1QwcOJDf/OY3hIaGcvjwYbKzsyktLSUxMZHHH3+cjh07no3tEhGRVmhRGYiISPumy2yKiIjKQEREVAYiIoLKQEREUBmIiAgqAxER4Rwpg507dzJo0KBAxwBg8+bNTJ06lYyMDG6++WYOHz4c0Dx5eXlMmDCBcePGsWzZsoBm+d4f//hHJk6cyMSJE3n00UcDHaeZ3/72t2RnZwc6BgAbNmxgypQpjB8/nl//+teBjgMc/9Do94/db3/724Bmqa6uZtKkSXz33XfA8YtmpqenM27cOHJycoIi04oVK5g0aRLp6en86le/oqHhxEvmBCLX91555RVuuummlg1iBDmXy2VMmzbN6Nu3b6CjGIZhGGPGjDF27dplGIZhvPbaa8acOXMCluXo0aPGmDFjjPLycqOmpsZIT0839u7dG7A8hmEYn3zyiXH99dcb9fX1RkNDg5GZmWmsX78+oJm+V1BQYAwfPtz4z//8z0BHMQ4ePGiMHDnSOHLkiNHQ0GDccMMNxgcffBDQTC6Xy7j00kuN0tJSw+12G1OnTjU++eSTgGTZunWrMWnSJGPgwIHGoUOHjNraWmP06NHGwYMHDbfbbdx6661n/f76Yaavv/7aGDt2rFFVVWV4vV7jnnvuMV544YWzmulkub63d+9e42c/+5lx4403tmicoN8zeOSRR7j55psDHQOAhoYG7rrrLvr37w9Av379OHLkSMDy/PvlxW02W9PlxQPJbreTnZ1NaGgoISEh9O7dm8LCwoBmAqioqCAnJ4c5c+YEOgoA77zzDhMmTCAhIYGQkBBycnIYPHhwQDN5PB68Xi+1tbU0NjbS2NhIWFhYQLKsXLmSRYsWNV3PbNu2bVxwwQV0794dq9VKenr6WX+u/zBTaGgoixYtIioqCpPJRN++fQPyXP9hLjj+t+qBBx5g/vz5LR4nqK/f/N5771FXV8fVV18d6CjA8Qc/IyMDOH611j/+8Y9cddVVAcvj6/LigdCnT5+mnw8cOMBbb73Fq6++GsBExz3wwANkZWUFtLz/3bfffktISAhz5szhyJEjXH755dx9990BzRQVFcVdd93F+PHjiYiI4NJLL2XIkCEByfLwww83mz7Zc/1MLonvj0xdu3ala9fjV6krKytj2bJl/OY3vzmrmU6WC+B3v/sd1113Hd26tfwCj0FRBm+99dYJd+KFF15IdXU1L774YlBlevHFF2loaCA7O5vGxkZmz54dkHzg+/LigbR3715mz57NPffcQ8+ePQOa5bXXXiMxMZHLLruM1atXBzTL9zweD5s2beLll1/GZrMxd+5ccnNzmTJlSsAy7d69m1WrVvH+++8THR3NggUL+NOf/sTMmTMDlul7wfxcLyoqYubMmVx33XUMHz480HH45JNPOHLkCL/61a/47LPPWrxeUJTB+PHjGT9+fLPbXnvtNZ555plm37eckZHBsmXLiIpq+8vYniwTQE1NDXPnziUmJoann36akJCQNs9yKr4uLx4omzdvZv78+dx7771MnDgx0HFYt24dTqeTjIwMKisrcblc/Pd//zf33ntvwDJ17tyZyy67jE6dOgFw1VVXsW3btoCWwccff8xll11GXFwccPzbCZcvXx4UZfDDS+UHy3N9//79zJw5k5tuuolbb/X/dxOcjjfffJO9e/eSkZGBy+WipKSEu+++myeeeOLHV2ybUxr+FywnkOfOnWvcf//9hsfjCXSUphPIpaWlhsvlMiZPnmx88cUXAc1UWFhoDB8+3CgoKAhojlNZtWpVUJxA3rp1q5GWlmZUVlYajY2NxuzZs42VK1cGNNNHH31kTJ482aipqTG8Xq/xX//1X8Yf/vCHgGYaM2aMcejQIaOurs4YNWqUceDAAaOxsdG47bbbjHXr1gU0U1VVlTF69GgjNzc3IDl+6Ptc/+7TTz9t8QnkoNgzOFfs3LmT9957j6SkJK699lrg+LHL5557LiB54uPjycrKIjMzs+ny4ikpKQHJ8r0//elP1NfX88gjjzTdNm3aNG644YYApgo+gwcPZubMmUyfPh23281Pf/pTrrvuuoBmGjlyJDt37mTKlCmEhISQnJzM7bffHtBM3wsLC+ORRx7hl7/8JfX19YwePTrg5xJff/11SkpKeOGFF3jhhRcAuOKKK7jrrrsCmut06RLWIiJybnzoTERE2pbKQEREVAYiIqIyEBERVAYiIoLKQEREUBmIiAgqAxERAf4fJJTld9PMm/4AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -391,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -401,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -410,13 +411,13 @@
        "<BarContainer object of 4 artists>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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HrU9UykxAIWYAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -431,22 +432,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe090855310>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14efa00b608>"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Nnz5ds2fPjvdIjvERuABgALdZAMAAYg4ABhBzADCAmAOAAcQcAAwg5gBgADEHAAOIOQAY8L/EyVNvSHPYlwAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -468,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -477,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -574,7 +575,7 @@
        "4       24.59  3.61  Female     No  Sun  Dinner     4"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,12 +586,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x14efa0b8088>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -600,12 +611,12 @@
     }
    ],
    "source": [
-    "sns.catplot(x=\"day\", y=\"total_bill\", data=tips)"
+    "sns.catplot(x=\"sex\", y=\"total_bill\", data=tips)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -614,22 +625,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:893: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  return n/db/n.sum(), bin_edges\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0b0c6c750>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14efa1d96c8>"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -651,22 +670,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fe0885a0690>"
+       "<seaborn.axisgrid.FacetGrid at 0x14efa222848>"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAcH0lEQVR4nO3de3BU9f3/8ddmQ0QKJoCJIo1UhalWx0v9cUmJCVCBcFnupUgLrbUIloYZ7LSkKLaKloAXBirKF8faVv22BeUSGM0gImAgla8whdFRvmDJhYuwXHYhAnFvvz9StuXbZtnd5HzO2c3z8Y85u3v2894jeeWz53w+n+OKRCIRAQAsl2F3AQDQVhC4AGAIgQsAhhC4AGAIgQsAhhC4AGBIpt0FJOrkyQaFw4xkA+BMubmdmn2OHi4AGELgAoAhBC4AGELgAoAhBC4AGELgAoAhBC4AGELgAoAhBC7gID7faZWXPyG/32d3KbAAgQs4yPr1a7R//z5VVKy2uxRYgMAFHMLnO62qqq2KRCKqqtpGLzcNEbiAQ6xfvya6Tkg4HKaXm4YIXMAhqqu3KxQKSpJCoaCqq7fbXBFam6WrhU2ZMkWnTp1SZmZTM0888YTq6ur04osvKhgM6gc/+IG+973vWVkCkDIKCvpr27YtCoWCcrszVVDQ3+6S0MosC9xIJKKamhq999570cA9duyYZs+erdWrVysrK0uTJk1S37591bNnT6vKAFKGxzNWVVVbFQpJGRkZGjVqnN0loZVZdkrh73//uyTpRz/6kUaNGqXXXntNO3bsUL9+/ZSTk6MOHTpo6NChqqystKoEIKXk5HRWYWGxXC6XCguLlJ2dY3dJaGWW9XDPnDmjgoICzZs3T4FAQFOnTtWwYcOUm5sbfU1eXp727t1rVQlAyvF4xurw4UP0btOUZYF711136a677opuT5gwQQsWLNBDDz0UfSwSicjlciX0vl27dmy1GgGnyc3tpGeffdruMmARywL3ww8/VCAQUEFBgaSmcO3evbu8Xm/0NV6vV3l5eQm9L7fYAeBkttxi5+zZs1q0aJEaGxvV0NCgNWvW6Omnn1Z1dbVOnTql8+fPa+PGjSoqKrKqBABwFMt6uAMHDtSePXs0ZswYhcNhTZ48WXfffbdmz56tqVOnKhAIaMKECbr99tutKgEAHMUViURS6vs5pxQAOBl37QUAByBwAQepq6vRzJkPqL6+1u5SHC8Vl7IkcAEHWbFimc6fP6//+q/n7S7F8VJxKUsCF3CIuroaHTlyWJJ05MhherkxpOpSlgQu4BArViy7ZJtebvNSdSlLAhdwiIu92+a28U+pupQlgQs4xHXXdY+5jX8qKOgvt7tpGkEqLWVJ4AIO8eCDMy/Znj79pzZV4nwez1hlZDStw5JKS1kSuIBDXH/916K92uuu6678/B42V+RcqbqUJYELOMiDD87UlVdeSe82Dh7PWPXq9fWU6d1KTO0FgFbF1F4AcAACFwAMIXABwBACFwAMIXABwBACFwAMIXABwBACF0BKYgFyADCEBcgBwAAWIAcAQ1iAHAAMYQFyADCEBcgBtFgqXnm3AwuQA2ixVLzybgcWIAfQIql65d0uqbgAOYELOESqXnm3S05OZ5WVPZYyvVuJwAUcI1WvvCN+BC7gEKl65R3xI3BbgCvKaE2peuUd8SNwW4ArymhNqXrlHfEjcJPEFWVYIRWvvCN+BG6SuKIMK6TilXfEj8BNEleUASSKwE0SV5QBJIrATRJXlAEkisBNEleUASQq0+4CUpnHM1aHDx+idwsgLq5IJBKxu4hEnDzZEB0dAABOk5vbqdnnOKUAAIYQuC3A1F4AiSBwW4CpvQASQeAmiam9ABJF4CaJqb0AEkXgJompvQASReAmiam9ABJF4CaJqb2JYUQHwEyzpF2c2rtly7tM7Y3Dv47omDLlR3aXA4fYvn2bqqq2JrXvxT/eyf7uFRYWq3//oqT2TZblPdyFCxeqrKxMkvTJJ59o3LhxGjp0qB555BEFg0Grm7cUi0XHhxEdsILf75ff77e7jIRYOrW3urpas2fP1oABA1ReXq6RI0fqySef1J133qm5c+fqtttu0+TJkxN6T6b2pp5XX/2dtm3bolAoKLc7U0VFA+jlosUWLpwvSZozZ57NlVzKlqm9Pp9Pixcv1owZMyRJhw8f1oULF3TnnXdKksaNG6fKykqrmoeDMKIDaGLZOdzHHntMs2fP1tGjRyVJx48fV25ubvT53NxcHTt2LOH37dq1Y6vVCDMGDhygd955R8FgUJmZmRo0aGDMXgAQj3bt3JJi9yidxpLAXbVqlbp166aCggKtXt00ISAcDsvlckVfE4lELtmOF6cUUs/gwSO1adMmSZLLlaHBg0fK6z1rc1VIdYFASJIc928p1h8ASwL3rbfektfr1ejRo+X3+3Xu3Dm5XC55vd7oa06cOKG8vDwrmofDMKIDaGJJ4L7yyivRn1evXq2dO3dqwYIFGjlypHbt2qW7775b69atU1GR2SEZsE9bW6w92eFOqTjUCfEzOg73mWee0aOPPqqGhgbdeuutmjp1qsnmYaOLt/9GbBeHOfEtID1xxwfAQZw61MmJnHqsuOMDADgAgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4LeDznVZ5+RPRW1sDQCwEbgusX79G+/fvU0XFartLAZACCNwk+XynVVW1VZFIRFVV2+jlArgsAjdJ69evUSgUliSFQiF6uQAui8BNUnX1doXDIUlSOBxSdfV2mysC4HQEbpK++c3/F3MbAP4vAreVuFwuu0sA4HAEbpJ27/7wku1du/7HpkoApAoCN0kFBf3ldrslSW63WwUF/W2uCIDTEbhJ8njGKiOj6fBlZLg1atQ4mysC4HQEbpJycjqrsLBYLpdLhYVFys7OsbskAA6XaXcBqczjGavDhw/RuwUQF3q4AGAIgdsCrKUAIBEEbpJYSwFAogjcJK1fv0bhcESSFA6H6eUCuCwCN0nV1dsVCgUlSaFQkLUUAFwWgZukpokPTYM83O5MJj4AuCwCN0lNEx+a1k/IyMhgaBiAyyJwk8TEBwCJYuJDCzDxAUAiCNwWyMnprLKyx+wuA0CK4JQCABhC4AKAIQQuABhC4AKAIQQuABhC4AKAIZYG7pIlSzR8+HCNGDFCr7zyiiRpx44d8ng8GjJkiBYvXmxl8wDgKJaNw925c6f++te/qqKiQsFgUMOHD1dBQYHmzp2rV199Vd26ddP06dO1detWFRcXW1UGADhGzMC966675HK5/u3xSCQil8ul3bt3N7tvnz599Mc//lGZmZk6duyYQqGQzpw5ox49eig/P1+S5PF4VFlZSeACaBNiBu6GDRta9Obt2rXT0qVL9bvf/U4lJSU6fvy4cnNzo8/n5eXp2LFjCb1n164dW1QT4GTt2rklSbm5nWyuxPlS8VjFDNyPP/445s7du3e/bAOzZs3StGnTNGPGDNXU1FzSY77YU07EyZMN0YW/W8P27dtUVbU1qX0v3uUh2YVrCguL1b9/UVL7Ij0FAiFJktd71uZKnM+pxyrWH4CYgfvqq682+5zL5dKQIUOaff6zzz7Tl19+qVtuuUVXXnmlhgwZosrKSrnd7uhrvF6v8vLyYpXgaH6/X1LygQugbUk6cC/n0KFDWrp0qf70pz9Jkt59911NmjRJixYtUm1trb761a9qw4YNGj9+fNJttIb+/YuS7mUuXDhfkjRnzrzWLAlAmooZuE899ZQeeeQRzZgx4z8+v3z58mb3LS4u1t69ezVmzBi53W4NGTJEI0aMUJcuXVRaWqrGxkYVFxerpKSkZZ8AAFJEzMAtKCiQJA0dOjSpNy8tLVVpaem/vWdFRUVS7wd7cb4baJmYgTto0CBJ0tixY3X69Gn97W9/U2Zmpu644w5dddVVRgpEeuB8NxDnxIctW7Zozpw56tWrl0KhkOrr67V48WL17t3b6vrgIJzvBlomrsBdsmSJXnvtNfXq1UtS03CxefPmafXq1ZYWBwDpJK61FFwuVzRsJenWW29VJNJ6Y2EBoC2IGbg+n08+n0+33XabXn75ZX3xxRc6f/68Xn/9dfXr189UjQCQFmKeUujXr59cLle0N/v0009Ht10ul+bMmWOkSABIBzED99NPP73sG2zYsEEjR45stYIAIF21eD3cl19+uTXqAIC01+LA5eIZAMSnxYGb6GpfANBWcU8zADCEwAUAQziHCwCGtDhwPR5Pa9QBAGkv5jjcy4Xp+vXr9cADD7RqQQCQrmIG7rx5rOwEAK0lZuD26dMn+rPP59P58+cViUQUCoVUV1dneXEAkE7iXp5xxYoVkiS3261AIKCePXtq/fr1lhYHAOkkrotm69at03vvvaehQ4dq48aNWrBggXr27Gl1bQCQVuLq4Xbp0kV5eXm68cYb9emnn2rMmDF66aWXrK4NQAr47//+o+rra423W1fX1ObFu4mYlJ/fQ5MnT014v7gCNzMzU3V1dbrxxhv14YcfqrCwUI2NjQk3BiD91NfX6uD/fqqr3W6j7V4RDkuSzn6232i7J0KhpPeNK3CnT5+uefPm6cUXX9SSJUu0du1aDRgwIOlGAaSXq91uje7UNm4Quu6sL+l94wrcb3zjG/rDH/4gSVq7dq1qa2uVkcGsYABIRFy32Jk2bZr8fr98Pp8aGxt19dVXa9asWaZqBIC0ELOH+7Of/Uzbt2+XJPXt2/efO2VmaujQodZWBtjMjotBqXghCPGLGbgX7+bwy1/+UgsWLDBSEOAU9fW1+t+/75M7O8tYm2F30wWZz04eNNamJIX8Xxptr62K6xzuggULtGfPHr3//vsKBAIqLCxU7969ra4NsJ07O0vZRdfZXYbl/NuO2F1CmxDXla+1a9dq1qxZ8vv9+uKLL/Twww9r5cqVVtcGAGklrh7u73//e61atUp5eXmSpGnTpumBBx7QxIkTLS0OANJJXD3ccDgcDVtJuuaaaxgWBgAJiis1c3JytGnTpuj2pk2blJ2dbVlRAJCO4jqlUFpaqrlz52r+/KahKu3atdOyZcssLQwA0k3MwPX5mqawzZ8/X6tWrdKBAwfkcrnUvXt3/fCHP1RlZaWRIgEgHcQ98aGgoEBS000jmfgAAIlj4gMAGBLXRTPCFgBajrFdAGAIgQsAhhC4AGAIgQsAhhC4AGBIXDPNkD64wypgHwK3jamvr1XNgU91bUez/+s7qOkOqxc+P2C03c8bgkbbA2IhcNugaztm6v7bu9hdhhGv7D1ldwlAFOdwAcAQAhcADCFwAcAQAhcADLE0cJ9//nmNGDFCI0aM0KJFiyRJO3bskMfj0ZAhQ7R48WIrmwcAR7EscHfs2KGqqiqtWbNGa9eu1ccff6wNGzZo7ty5euGFF/TWW2/po48+0tatW60qAQAcxbJhYbm5uSorK1NWVpYk6aabblJNTY169Oih/Px8SZLH41FlZaWKi4tb3J4dA/oZzA8gEZYFbq9evaI/19TU6O2339b3v/995ebmRh/Py8vTsWPHWqW9+vpa7dt/QO72Oa3yfvEIh9ySpAP1J4y1KUmhCz6j7QFoHZZPfNi/f7+mT5+uX/ziF3K73aqpqYk+F4lE5HK5Enq/rl07/sfH27Vzy90+Rx16fLsl5aaEc7Xvql07t3JzOyW8b7t2bl2woCYna8mxaks4TvFL9lhZGri7du3SrFmzNHfuXI0YMUI7d+6U1+uNPu/1epWXl5fQe5482aBwOPJvjwcCoRbXm0oCgZC83rNJ7dfWcKzik+xxOnHipE4Fg1p3tm188zoRDCp84mSzxypWEFt20ezo0aOaOXOmnnnmGY0YMUKSdMcdd+jgwYOqra1VKBTShg0bVFRUZFUJAOAolvVwX375ZTU2Nqq8vDz62KRJk1ReXq7S0lI1NjaquLhYJSUlVpUAwIDs7BxlnPBqdCdz10/stO6sT52yk/uslgXuo48+qkcfffQ/PldRUWFVswDgWMw0AwBDCFwAMITABQBDCFwAMIQ7PgDN8Pt9Cvoa5d92xO5SLBf0Ncqf2TbG0dqJHi4AGEIPF2hGdnaOTgRPK7voOrtLsZx/2xFlJzm2FPGjhwsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGAIgQsAhhC4AGBI2kx88Pt9Cl3w6Vztu3aXYrnQBZ/8/rT5Xwe0GfRwAcCQtOkmZWfnyHsm2Gbu2ss0TCD10MMFAEMIXAAwJG1OKSA+fr9PpxuCemXvKbtLMeLzhqA6+1nnFc5ADxcADKGH28ZkZ+foivMndP/tXewuxYhX9p5Sey4wWu5EKKR1Z81+kzgXDkuSOmSY7TeeCIXUKcl9CVwALZKf38OWdk/X1UqSrrnebPudlPxnJnABtMjkyVNtaXfhwvmSpDlz5tnSfjI4hwsAhhC4AGAIgQsAhhC4AGAIF82AGEL+L+XfdsRYe+ELIUlSRnu3sTalps+prkabbJMIXKAZdgx3qvvHUKfruxpuu6t9w7vaEgIXaIYdw51ScagT4sc5XAAwhMAFAEMIXAAwhMAFAEMIXAAwJK1GKZi+a284eEGSlJHZ3libUtPnlK422iaAlkubwLV1zGS+6fC7mjGTQApKm8BlzGT8PrfhFjsNXzYtFt0xy+xZrM8bgvqa0RaB5qVN4CI+dvWMj//j28DV15pt/2tiBhWcg8BtY1gsGrAPoxQAwBACFwAMIXABwBACFwAMIXABwBACFwAMsTxwGxoaNHLkSB06dEiStGPHDnk8Hg0ZMkSLFy+2unkAcAxLA3fPnj267777VFNTI0m6cOGC5s6dqxdeeEFvvfWWPvroI23dutXKEgDAMSwN3JUrV+pXv/qV8vLyJEl79+5Vjx49lJ+fr8zMTHk8HlVWVlpZAgA4hqUzzZ566qlLto8fP67c3Nzodl5eno4dO2ZlCQDgGEan9obDYblcruh2JBK5ZDseXbt2bO2yktauXdOtrHNzO9lcifNxrOLDcYpfKh4ro4F77bXXyuv1Rre9Xm/0dEO8Tp5sUDgcae3SkhIIhCRJXu9ZmytxPo5VfDhO8XPqsYr1B8DosLA77rhDBw8eVG1trUKhkDZs2KCioiKTJQCAbYz2cK+44gqVl5ertLRUjY2NKi4uVklJickSAMA2RgJ38+bN0Z8LCgpUUVFholkAcBRmmgGAIQQuABhC4AKAIQQuABhC4AKAIQQuABhC4AKAIQQuABhidKaZE23fvk1VVcmtyVtXVytJWrhwflL7FxYWq39/pjYDbUWbD9yWyM7OtrsEACmkzQdu//5F9DIBm7S1b5htPnABpKZU/IZJ4AKwTVv7hskoBQAwhMAFAEMIXAAwhMAFAEMIXAAwhFEKiFtbGzMJtDYCF0ak4phJoLURuIhbWxszCbQ2zuECgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCGuSCQSsbuIRJw82aBwOKVKRhuU7NrBF9cNvv76Hkm1y7rB9svN7dTscyzPCDgI6wanN3q4ANCKYvVwOYcLAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgSMqtpZCR4bK7BABISsqtpQAAqYpTCgBgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgSMpN7TXh8ccf1+7duxUIBFRXV6ebbrpJknTmzBmNGzdOpaWlNlfobIcOHVJJSUn0uF20fPlydevWLbo9bdo0Pfnkk7rmmmtMl+gIlZWVWrFihYLBoCKRiEaPHq0f//jHzb5+5cqV6tChg0aOHGmwSnsleowcL4Jm1dfXRwYOHBjdXrp0aWTp0qU2VpQa/u9xw7/7/PPPIwMGDIicOnUqEolEIg0NDZGxY8dGNm3a1Ow+c+bMibz55pumSrRdMsfI6TilkKC9e/dq0qRJGjhwoH77299KklavXq2ysrLoa6ZMmaIPPvhAH3zwgSZMmKBx48Zpzpw5dpXsGGVlZZoxY4aGDRumzZs3a9CgQTp06JDdZdni9OnTCgQCunDhgiTpK1/5isrLy9WzZ0+9/fbbmjhxokaNGqWSkhLt3r1bO3bs0ObNm7V06VK9//77NldvRqxj9K//dj744ANNmTJFUtPv3qJFi/Td735XgwcP1tatW22r/z/hlEKCTp48qT//+c9qaGjQoEGDdP/998d8fU1Njd577z116tTJUIXOcPz4cY0ePTq67fF4JEk5OTlavny5JOnJJ5+0pTYnuPnmm/Xtb39b9957r2655Rb17dtXHo9H+fn5euyxx7R8+XJ16dJFb7zxhlasWKHly5dr0KBB6tOnj+655x67yzeiuWPUo0ePmPsFAgH95S9/0ebNm7VkyRIVFxcbqvjyCNwE3XPPPcrKylKXLl3UuXNn+f3+mK+/4YYb2lzYSlJeXp7WrVt3yWNlZWW6/fbbbarIeR5//HH95Cc/UVVVlaqqqjRx4kQ988wzWrZsmTZv3qyDBw9q586dyshou19EmztGsVz8g9SrVy/5fD4TZcaNwE1QZuY/D5nL5VIkEon+96JAIBD9uX379kbrczqOR5MtW7bo3LlzGj58uMaPH6/x48dr5cqVev311/Xcc89p1KhR6t27t77+9a/r9ddft7tcWzR3jN544w1Jiv7OBYPBS/a74oorJDX9fjpN2/3T2Yo6d+6szz77TJFIRPX19dq3b5/dJcHh2rdvr2effTZ6HjISieiTTz5RVlaWXC6XZsyYob59++qdd95RKBSSJLnd7ujPbUFzx+iWW25R586ddeDAAUnSu+++a2eZCaGH2wq+9a1v6c0331RJSYluuOEG3X333XaXBIfr16+ffvrTn2rGjBnRb0T33HOPli1bprKyMg0bNkwul0uFhYXatWuXpKZ/Z88995w6deqkkpISO8s3orljNHPmTH3zm9/U/Pnz9fzzz6uwsNDmSuPHHR8AwBBOKQCAIQQuABhC4AKAIQQuABhC4AKAIQQu2ozKysronHvADgQuABhC4CKtLVmyRPfee68mTJigd955R5J08OBB3X///Zo4caIGDhyohx56SI2NjaqoqNCkSZOi+x45ckSFhYX68ssv7SofaYbARdratGmTNm7cqLVr10ZXeJOaFvIeM2aMVq5cqY0bN+rQoUPasmWLSkpKVFdXp/3790uSVq1apbFjxyorK8vOj4E0QuAibVVXV2vw4MHq2LGjMjMzNX78eEnSz3/+c3Xp0kUvvfSSfv3rX+v48eM6d+6csrKy9J3vfEerVq1SKBTSmjVrNHHiRJs/BdIJaykgrf3rzHW32y1JevjhhxUKhTRs2DANGDBAR48ejb5u0qRJmjBhgvr06aNevXopPz/flrqRnujhIm0VFRWpsrJSZ86cUTgcjq7PW1VVpZkzZ2r48OGSpD179kRX4erWrZvuvPNO/eY3v9F9991nW+1IT/RwkbaKi4u1b98+jR8/XldddZVuvvlmnT59WrNnz9bMmTPVoUMHdezYUb1791ZdXV10v3Hjxmn+/PmOulMA0gOrhQH/IhwO64knntB1112nBx980O5ykGY4pQD8Q0NDg/r27aujR49q6tSpdpeDNEQPFwAMoYcLAIYQuABgCIELAIYQuABgCIELAIYQuABgyP8H1Sjv80sWVFUAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -688,22 +707,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fe0823217d0>"
+       "<seaborn.axisgrid.FacetGrid at 0x14efb428708>"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -713,17 +732,17 @@
     }
    ],
    "source": [
-    "sns.relplot(x=\"total_bill\", y=\"tip\", data=tips)"
+    "sns.relplot(x=\"day\", y=\"tip\", data=tips)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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5ARs9ejSHHnoot956K/fddx+e5zFmzBh++9vfApBKpWhoaGh6bluGZfYalNavX88FF1zA0KFDsSyLTz75hOLiYqZOnQrAc8891+qLTpw4kWuuuYa6ujqi0ShvvvkmkydPbvHzq6riHb4HT1crLs6hoqK+u4vRLp1Rh5ygi1exGhUIQU4JDen2ZcIK2dDw1pNoJwlA9ZxHCQ45hvo6F2hZHSwzQNnXbif+/isE+xyMLj4oq947+Sxlh2wLqnfddRelpaVceOGFHHPMMUybNo1oNLrX540dO5annnqKb3zjG6xZs4Z3332Xm2++mSVLljBs2DAuvvhizjjjDGbPns2oUaO44YYbWLFiBQceeCAPPPAAmzZt4o477mhzuff6jf/BD37Q5pM3Z9SoUfzP//wPX/nKV0in0xx33HHMmDGjw68jepaI5VL72l9o+OgNAHpN+QaBoSfipNt+A+Irk0DpgTQu+Q8AgaK+aN26ASHX0zQYeQSOOQfP1ySl2070ABdccAFXXXUVs2bNwjRN7rzzTn7yk5/s9XnXX389N954I7NmzQLg1ltvpaSkpOn/A4EAN998M9deey3PP/88t99+O5dffjm+71NaWsrdd9/drnIr3UwSsmXLljFo0CA+/vjj3T5x+PDh7bpwe0hLKTt0dB1iVoryP1+N11ALQPiAkeScchkJt32tpZyAS2rFe3jxGmIjTqDeDTZ9fuR9yA77Sh1E+zX7bb/rrrv43e9+x1lnnUXv3r136GdMJBLMnz+/Swoo9h8uFtERE6lb8HdAERv9BVxs2ruTa71jYR8wFlNBXdrr9GSwQoi2azYo3XnnndTU1DBo0CAefvhhtNYopUin05x//vldWUaxDzJNA6XYYa+jpGsSPeI0YiMnoiwbhyBpt2MCiMyUE6JnaDYoXXXVVcydOxelFOPGjWv6u2manHTSSV1SOLFvMiyTJWtrqI07HHlIKbjbWi+ZiQ154HZvGYUQ3aPZoDRz5kwgs3j2pz/9aZcVSOzbbNtk9nvr+OPznwBw2EGFXHneaPC6p0vNa6wjR9di2EFSBEi2cg2TEKJj7XUEWQKS6Eg+sHhlddPxsnW1aKA7EiSELZfqN56i/p2XQRmUfPka7JJDpatPiG4kW1eILqW05swJg7CtzEdvxsTB3fYhtJRH/IM5mQPtE1/0b0zpNxSiW0lQEl3KdX3KCsL89prJPHjdZCYf0Q/dTclNPW0QPnBk03F48FF4SPed2Df85Cc/4bLLLtvhb2+99RaTJ0/usB0fOkP7FoAI0Qa+56PIfPh0N40lASQ8m+JTLiUx+iTMcAw/UkhjWrJ/i33DVVddxdSpU3n11VeZPHkyjY2N3Hzzzdx+++3EYtmbqk1aSmK/pTWY0TxSBYNpCJbR2M5FukK0VW08xWerN1MbT3XYOaPRKLfeeiu33HILjY2N3HfffUyaNIljjjmGRYsWce655zJt2jQuuugi1q1bB8Af/vAHpk6dyrRp07j55ps7rCytId9Csd/r6dlBRM/2+rtrue+J97FMhetpLjv7cE4Y076k11sde+yxjB8/nuuuu47ly5fz5JNP4jgON9xwA7///e8pKytjzpw53HjjjfzmN7/hoYce4o033sAwDK677joqKiooLi7ukLK0lAQlIYToJrXxFPc98T5O2sNJZ/523xPvc/iQYvJiHbPx6bXXXsuJJ57Ir3/9a0KhEIsXL2bt2rVccsklQCYLeCqVIhAIcNhhh/HlL3+ZyZMnc/HFF3d5QAIJSkII0W02VTdimaopIAFYpmJTdWOHBaVYLEZubi59+/YFMvvZHXDAAU1bWXieR1VVFQC/+93veO+993jjjTf45je/yS9+8QuOPPLIDilHS8mYkhCdQCmFZRko2aJW7EFprwjuTpN9XE9T2qvz9usaPHgwFRUVvPvuuwA8/vjjXH311ZSXl3PqqacybNgwLr/8csaOHctnn33WaeVojrSURI+gVCbFldY+XjfO2GsJ24SImcCrrcTMLSLhB3E8CU5iV3mxIJedffguY0od1UranVAoxC9/+Utuu+02HMchNzeXO+64g5KSEmbMmMH06dOJRCIcdNBBTJs2rdPK0Zxmt67IZtmwdYUyDTwNac8nZJtot3VZAPaVVP1dUQfTNEgD7yzexMDeuZTmh9u1tsk0DSJGEgXY0RhVNU6HlRUgP+iQWvsp9YtmE+wzmJzDv0CN03k/MvJZyg7t2bqiNp5iU3Ujpb0inRqQegJpKbWBZRks2xjnlpkL8DVMO3EwU489AHxZ49IZPBTX/fotKmoSANxyyTgGFEXx2hCYDEMR1XVs+utP8BpqKD7tuwT7jCDldmBPtudS/o97wfdILH+fYNkg7NLDJH2RaFZeLLjfB6OtZEypDXzgxbkr2NpY+9eClbRyM1PRCp7vNwUkgCWrNmOabXvBbUtR+9aTuLXlaNeh4vlfE1Qdm1pIaw3+tgDkOwnZw0mIFpKg1AamUowZtm174MMGFXVLQtFsYdsmgYDJ7sb0DUMRCFhYVus/ak3PNQ2OHdkbgFjY5rhRfZr2YTJNg0DAwjBa9g5oFFZeUdOxGc3HUDu2uPZUnz3931auEaLgxK9gRHIJDz6C4AGj6MqUs0pBIGBi23tOmWRZrXvt2qIlr5cQ25MxpTZSpsGmzQnqEw4H9ytAuy6teSX3lT70RO1mnOXv4tVXER05ibgXanpvTFMR1XHiH84mUHogZtkwGlqYNcE0DZKe5tX/rubgAQUM7puH4/rYloGpNZ7nEzJdjNp1JFcuInroeJJ2AekWTCjIDySpW/gSXnwzuUechO95JGID8X2d2Tp9+Tt48c1ER0zcoT6GbTHvw/UkUi6TjuwPrt9sCyhkedjKxUAT/+h17ILemH0P3bJfVMfa/rNkGIocK0X8g9kYkVxCg4+m3tn1mso0WLahjs9WbWbikf2JWEabukP3JBZwcVa+j1dTTnTUJBr8cLOTVPaV74NoPxlTaiPt+ZTlh+hdEMJL75+ZpbXWJD+ew+bX/gpA45IFFH75euJ+AICokWLjn67Ha6gBoHj6D7HKRuyw22xzPKW4+ldvUBvPTEK4/htHM7RvLq7r4ZGZcm2nalj/6E0A1L39PH0vuZ9ab+/98hoDL9WIsoNUPP8AJedcj+9qAgGTxg9epuaNxzL1+ew/FE6/jrgfwLRMnnrtc557awUAHy6t4rKzRzW7D1TSNbFtjw1/uQ6vPrNVR9EZl2P3O6JTx5bChkPFMz8ntW4JAHnHVhEcczopZ1s5TdNgVUUDtz70NgAvzV/JvVee2KHlCAQsUkveoPqVhwBoWDyXonNvIu4FOvQ6Yt8j3Xft4Ps666cndybtuzibVjQdp6vWs0NPkPaaAhJAumJVi7uKPE83BSSAVRvqdniuYSjSmzduu5Tr4Kdbljcs7gXJP/bLhA8+mrLzfkxChzPnxCddvnJbeSvXNXU7eVqzauO2O/m1FfXsrbGutN8UkACcTSs7fd2SgU+6el3Tcbp8JcrfMQgahmL1pm11qW9Mt+hGoTWU0jgbl28rR/UG+bERLSKfE9FmhmmTN246RjCz0K9g4vk4/rZxDBebnCNPBsCMFRA97ATSLczCbRpw8rgDAOiVG2LC6L47PNfzfAJ9h2AXZlaphwcdAXa4Ref2PE1tOkiq1xDsvOKmLr+0p8g79suoQOY8BZMuwNGZ+pgKvnrSMIK2iaHgwlMOZW9zLVwsco85PfP8aD45h0/BbeXSgdZysCk48XxAoewQeePPxt2pQ8R1PcYOL6O4IFPPKUf1x2rjxJHmuK5P7tgzMEJRAApOOK/ptRRiT2RMqZvsK33omzfHiagUaJ80NomdthOPWGksXFAmDX6wdS1L08DXYCiFid5lzMMwFFEzhdIevrJocO1WjettrcP274NtQVilQOtd6mOYBh6KTAcgLVorFbFcLNJtq38b6xCyPAIqDSgSOsjuepdN08BTCt/XmdZtJ+xpZZmKiLH1s2GR2MN44r7yfcg2a9euZfLkyTz00EMcd9xxTX+fNGkSf/nLX+jXr2MSv3YkGVMS7eK6mjqaHydodG3A3nLUyh9kz29qyu+ufeH7mnq/Y8co0i6k2f241NZ9oKDlNclsh7H1a9Y1N1JJ1yS5l80Ktwb4zuwqcT1NnYwhtYjXUEu6tgI7rxgzmteh57Ztmx//+Mc8++yzWb2P0lbSfSeymmUZnTpluaczTIPquiTaMDBN+Tr3RPUfvcnqX13Khkf/j9W/upT6j9/s0POXlJRw7LHHcuedd+7yf7/97W855ZRTmDp1KnfccQee1/0LvOVTLLKSYSjyAg7G6v8SblxL2Or5MxwzSVrNDguyhmmwuqKB7/98Djf9YQFJT0sA72G8hloqX3gA7TroVCPadah8/gG8htoOvc61117LW2+9xdy5c5v+9sYbbzB79myefvppnnnmGVatWsVjjz3WoddtCwlKIitFTYdNf/s/qp6/n41/uR4qlrZpAW62sE3IoQ61/C2iXjUBs/1jOJ6Gnz3yDjXxFGvL4/z5xU8wpLXUo6RrK8DcaRTFNDN/70CxWIxbbrmFH//4x8TjcQAWLFjAqaeeSjgcxrIsZsyYwfz58zv0um0hY0oiKyk80lXbpjYnV31EoOywDp+63FUiZop1M3+IdpJgmPT91n04RNt93ljYpq4hM3U+LxaUzAk9jJ1XDN5OvQCel/l7Bxs/fvwO3Xj+bnJ1um7390jIbZXISh4m0eHjAVB2iNiIiT06oamXiGcCEoDvkd68od1dbZaCGy8ay7gRvfnS2IGcM3kIbg9+jfZHZjSPotO+g7ICqGAYZQUoOu07HT7ZYaut3Xjl5eWMHTuWF154gWQyieu6PP3004wdO7ZTrtsa0lISWakhbZN74oXkjT8HZQVI6CC+2/XLAJSi1dPMd8cI5xIoPRBn0wqs/FICxQNIOO07cSbVkuKqr4wh0ejgtjLVlcgOOcOPJ3LAyE6bfbe9rd14F110ESeeeCJ1dXXMmDED13UZP348559/fqddu6VknVI32VfWZeyrdQiYmrCRwmuowYgVEncD7frMbV1TheegzAANfqjDcs3ty+9DT5KN65R6ImkpCbETw1AE09Ws/ePV4LkEeg+iaPo17VoTtW1NVSCz9wk9c2xMiM4mY0pC7MQ0DRJL320agHY2LEP53T8ALMT+QIKSyHpKqS5df+N5PqGDRoGRyYoQKBmINqRTQYiuIN+0/YxpGvi+3+kD4oahUIp253qL2i7EK9BOErOw/273Bupovq9JBwvpe8n9uHWVWAW9ibsBuipNkBD7M2kp7SdMU4Fl8unaWhpdDZ3Y8ojYLuHGdQQql5AbcNu0dsY0FcoyqUn41NXWU/HMz6md83CXZXZIeQZ1XoRkzkDqHLvHT6wRoqeQltJ+wlMG1/9mHhuqGjAU3P294ymMtW9G2e4EbIP00rep/ueDAESGHE3OlIu3JGZtGaUgrRVX3/8m1XVJRh1cyP+efDnJf/2K3AlfoSs/thKMhOha0lLaT7iuz4aqBgB8DR8ur2pTAk/TNDBtEzuw+yzUBh7J5e82HSdWfoipWjfTzDQNPlhaSXVdZrHpos+rSIeLiI44cZe9gdrCtiDHShGzUs1uZy6E6B4SlPYTlqkYfmAvAEIBk6MPKW31OhnTNGhwPH7zzIc89upSaup33enV1Sax0V8Elflo5YyagtvKzd18X3Ngn9ym45yITSS/gMgRp5F027dRnG1CoG41G//0QyoevZF09QbJri1EFpHFs92kqxcLKqXANGhMuYSDFobW+K1dvGka/PBXb1FVm2nBzJg4mGnHH4Tj7DjOE7R8QqTw3TS+HaEh3YbWjWlQXZ/is9WbOWJYKSFTdchi05iZouKRG3DrMgkvw4NGk3PS/+6yOWFPsq8sPN0X6iDar1vHlO688042b97MHXfc0Z3F2C9orcH1CJsKXG+3Szcjlotl+Hi+otEL7LZrK55IN/27Jp7KLDQNWth+EgOftDZIuBYpwkAY0rucomU8n6JYgNIRvXFdv8OyH2ilMCI5sCUomeFctJKWkhDZotu+jfPnz+eZZ57prsuLneTYaeJzHmLdry6h5sV7yQnsGk0U8F2/nBIAACAASURBVMOvHkFuNMABvXM574tDWbx6M7PfXUt9Q4JNj9yI8+ErRDpohpzva9Jpr0PHfZI6SPH0HxA95FhyDp9Cr8lfI+VKam0hskW3tJRqamq45557uPTSS/n000+7owhdzzTQZNbumLpjU8wo08BvOrfeY6vCMA38LXO0DQXa12jPh2Q9DZ/Mw+7Vh7yjp6I8B9O2cX2FApTvoz2fwX1yuef7EzAMxdJ1Nfg+DO5fwMcbEgw78SJq/n4bfUecSHMfLcM08LZsKm7Ssi7EkOURUC6gaNRB3O0Ss9qWQqvM3kJKKbSr9xjEPE8TN6JEJn0LrRRWLIZOZFe3kVIQsdKY+PgYNHodP0tSiGzVLUHpxhtv5IorrmDDhg3dcfmuZxo8OXspL81fSZ+iKLdecmyHnVqZBnM/2sDMZz8mLxrkp985jrCldvsjZpqK5Zvi3PbHtzFNxdXnHwlac1CfXAw7jArFKD79e5Q/+yvCp1zB7bM+ZenaGsYOL+OSaSMy3X6ejwJsy6KsV5S7H3mH1RvrmTC6L4edNAgjGAHVzMw802BtVSM/+cMCAG78n7H0K4zsMTCFLA9v2XzW/XMmRiSHsvNvocHIx/c1pmmg8bn3iQ94Z0klwwYWcN2FR+I5e96+wfc1SR+ydTFszEqz+Z+/I7F0IcE+B1M0/WrqnJZPqReiJ+vy7rsnn3yS3r17M27cuK6+dLdJe5qX5q8EYH1lA28v3thh59YoHnruE7TOjPHMmrMUy9p9UEj78Id/fITr+aQcjydnf8a6ygZ8FEk/SO+v3kxi1UegfZaVOyxdWwPAgo830pDctUtu2bpaVm/MtDLeeG8djgdlF9yKNkxyrQQ5dnqH9ECur3nouY9xXB/H9Zn57Ee4e2kBBJRH9St/AjR+Yx1182ZhW5lz2qZP9eZ63llSCcCnqzazZlN9z98SPFVPYunCzD/Xf066YlXPr5MQLdTlLaUXX3yRiooKzjjjDGpra2lsbOT222/nRz/6UYvPUVgY68QSdryq2gThoEUilflh77tllk5HzNbZXJ+kOD/MpupGAAaU5ZCXF97tY+ONDmWFEdZVZLZDLuuV2fk0YBvk5uQBefjxzdSmGijpFWl6nmUaRMM2Rfk7nrdP0badU4MBk1A4hG1bbHr6bpKrPyZQdhBl516PFc0HoCGRpqwwyvJ1tQD0LooRiwaJhptvBbgNNVh5xbibM61qu2QAuXmZ62rfpzHlYhoKz9coBYV54VZ/PrJt1pRblwLDgi1JYAN5RcT2Uqdsq0Nb7At1EO3XrVPCZ82axdtvv93q2Xc9bUq4YRrUp1xenr+KQw/sxSEDCyjuFe2QKbCmaeBoeO7N5fQuijJ2eBna3X33lVIKLIOX5q3CNBTHjepDJGhiopty1EUtF3fdR6SMCGtUX95eXM7EI/pRGAugd3rNw9EgHy6rZNHnlXzh6AHkRywiqXI2PPTDpseUnncTybyD8LcEDWVZvDx/JQBfGncAei8b05mmQVQ1UL/wBaz8UkJDxu2Q/y4c0KypbOT19zcybkQf+hVH8VuxZXo2TkUOmhq7cSPxj14ncvDR+AUDSHrN3z9mYx1aa1+pg2g/STPUBXzPJydg8pUpBzfNKOsonudjG4pzJg1Ga42zh/EUrTXK9Tj9uAOwLAPP83EcD2+7qNDgWtj9jiBoKAZpzeC+ec1OyY5FAgzrl8/wgQW4rofnacxwzra7fGVg5RU33UBoDTrtctqxBwDssr6pufrVqwiBY85Fa039TvVLOIp+JXl87eReOI7bo7dM3yrlKdxIX4LHno/j+bitCLJC9HSyeLabZOudYcjWBHDQvodnBEm4ZrMtmd3VIWD6BJKVNC75D+HBR+BGS0l5rVuYqpQibKYx8XCwSbmdN/SZre9Da0gdsoO0lDqGtJT2I2HLxVIenjZpdHd9623bwKxdybq//QQ8l+hx5xAZfRJJp+VBwfEMvFAp1phpJL22LXrNsR2qX/w1qY3LyRs3jfDQCT0644IQouVkKft+IhZwScx/gg1/uIL4nD+SE9y168w2fOr+82zTjquJhc9h+a1PyeB5Gsdx2xSQLMsgufw9Esvfx2+sY/Orf8bWu+bYy2qWQUoDlolhyqw5IVpDgtJ+QiXqqH/vX+hUIw2fzMWrXrfLNGMfk0C/YU3HdtkgdDNrjjqLYSiMUHS7P1hNO8D2CKbBQ899wrfvnM2375pNbcKT6dxCtIJ03+0nVCC4w7EZiu6S+SCZ8ogNn4jVqx/phlpiBx9BoxeA3WbK66ByKYjZaUg1YAbDeIk4qqCU/OPPIbVuCbljzyTlBzrt+h3N1/DWovUApByPOe+uYcaEQS2a1CGEkKC033BUiOLpP6Dhg9cIDx2LF8xH7+Z3Mu6Y2GXDsQ2DeieN7uCUSDuLWmmqn7+P5MoPUVaAsnOup/yFBwgPHkPhKd+hwQ/1qNlnhlL0L81hzabMoP3IwUX4fs8pvxDdTYJSD6UUe1zfs7Oka2L3Hkm07FA8ZZHyNWHTwTRNFB5oTVIH8DFwPB9T73h+0zSIGEkUmrQ2SWw3UUIpMC0D7XsEbQPtuqB9HAKZaeKmImi4oDUpbTetiQIwfJfkyg8B0K5D44pFBEoPoH7hS+QeeRpuD2olAZgGXPO1I/ng80r6lsQYUBLDbWbdWE9iWCae72MZCq8H3SSInkeCUg9jmApfGVTWJinKD2H4e07Aur102ieNgWVpIk4V6fLlmGWDqF34Is7G5cS+cAmf10Z4/N+fMbh/PudMHoKfdlFKEVUNbPrrjbi1FeSNP4vIyJMAtiyINXnw2Y+o2JzkW2cMp9fq2aSWv0fhSReTjpaS9jVryuvplRsiFtIktwQrAG2Y2IV9SVdvoOiUSwiWHoTvpoiNOAHfDHRmz2GnSKZ9rrzndfqWxIg3pjl7yhCOGVbco1p7OzNsiyde/YzP19Rw9pQhDO6Tm0niK0QnkKDUw7govv+z12hIuhQXhLnru+NbfY4QSape+i1Fp1xCYvXH1L/3CgCmb3P7n/+L72s+X1PD0AEFjBlciGEYxN97Fbc2swdR7VtPEhs5GQDbNnlqznLeWpRJA3Tj7xfwy2+MJPXmY2x68nZKv3YH1/1+Eas21hOwDO757lEU5Sgatnz0GrwgJefdhN9QTWLpu1S+8BsAek2+EL/0kGbrEAiYKKVwnI7d2qK9LNNAGYoV6+sAKCkIt6pFm20sy+Ddzyt4cd5KAH765//y4LWTZYaU6DTy2coiSilidnpLMlNnl1lbppn5sduaHLVic2KHTfdaImD6WHjkjT0DIxCG7cY7NOzwA59pzSi01ti9+jT93YjkNm13jlJ4253D83Wm+QTgZwLGqi1JWx3X54Nl1ajtth/3fU2dE4BwLxo+mdv09/jHb2I2Mx3dsE3mLFrPU3OW4RkKM4umXZv43Pnd8Uw5egCXnzua/iWxDtugsLtsv1A9m24AxL5JWkpZJMd2qHr2HpKrPyFQMpDis2/YYcsCz9Mc0Du3Kblrr9wQsbDdbK67nSmlCHr1rJl5FXguRVO/R+jAkUQPHY+zaQVhUlx53hge//dnDOqXxxHDSnDTmQAYGzCSwlO+jbNxBblHnUqDHyQIGH6aL08cxPqKBipqElw67TCMNa9hFw+g6JRvkzLC9CuJsbY8jmUajBzWh6RnAzuW2cUiMvQYaufNAiA6bBye2jVRayBg8sKC1fztX0sAeGdJOTdfdAzZsg2F72kKIjZfP3kYuoNTSnUH1/UZM7SEE8f0ZenaWs6ePARTZcurLfZFkmaom+wurUrMq2TDzB80HZec+2NS+YN3qKthKjwMNlY10KcoiqH9HSYO7IllGRjL51L18u+2/EXR/7u/wVcG2vdJEcTVJq6vMQ0Fvr9D15NtmxiGIp328H1NYUGQ+KdvZxa4HjWdwEGjiUSjaDeN0j4pQvgatGmyoTJOSUEY2zTwtvxQRywXSztgmCT8YGYyREMVKAMdKaAhves9k2WbPPjcx7z1fmbaddA2+c3Vk8Br24//vpLepjProBRgGHi+xjJUizZmbK195X0Q7SctpSxihnJQpo320qAM7LwSkjvdM/ieRuHRt1cY3/VozU+x52kiAw9D2UF0OoVd3A8Pg3onQMRyCekEGCZuIIzvadKoHQLeznf9frKRmjcew4tX4732B/RnQwid/kMS3tYWzpbn+i59CsKZoKh9MA1slSb10atseuNvmang5/+ERLgPfrA088xmeiW1rzlnyhAWfVZBPJHmf844DIWWO/dOpDXg+RhkPn9CdCYJSlkk4QfofeFPafzsP4QOGk3KiKCbiTq7aykGTU3QSIHv4RpBGndqaRiGol7lE/rGbwkamqjtE0/bhCyX1IevsGnuU5SdfS2ptZ+Rrl5P3nFnkbALdth+fHvaDhIdO4PGt58hXbWOQMmBaLX7j1TESpP8eA7JjcvJGzcdI5pP5dynMudxHeoW/IPIlEtI7iXKep5PbtDi3itPRGuNATITTIh9iASlLOJ4CjdQjHX46SS9lk/1BrAsE7N2Oev+dgv4LvkTziV02BdIuiamaaAUuFpxza/eoqImweB++Vz/9aPQvodtelQu+DuRQaNJrV9KzVtPApBY+SFl3/gZ9WxbKxSwNEEcEkaYv76ynI1VUb469SaK6pcR7HMwdaldJx3Ytkl65UI2v/ZXABqXvUe/b91DoHQgqfVLM+ftOwQfk53HmnZn6+ui6NyxDdNSpH2FAixFj5+wIERPIEEpy/j+nvdEao6lXOrffq5pt9K6/zxL2WETSZk2qyoaaExmtiavqEkAsHRtDY0pl7Cp8LUiUHoQyg7hNdRsK0sijtruZz9ggVW9nFRtOVa/kZxyWJSlFTY3PbSQu//3eFKp5jYXBLe+qulYOwl8H4qnX0PjZ//BjBVg9R5KvBX1Vgq0YWQ2LtSg2zim1BzTMqiqd7j1j28D8ONvHkOvqC2BSYhOJlPC9xG+Mgn2P7TpONhnMFhB3vu8khsfnM/9Ty6iV26oaZp5JGQRDmbuSRJ+gKIzryQybCy5R5+OXdQPZQUoPPlSHL3tviVIitr/PIudW0TFzMtoeOQqDix/jRnH9d3jWpx02iM2YiKB0gNRpk3B5AtJY1Pr2HDwCbhlI4jvZlLDnhiWxe//8REX//RV/vzyJ5h265O2WpZBPJHGsnb9GqQ9za+fWkR1XZLquiQPPL0IV2uUaeCbBkk/s2hYCNGxpKXUg9mmJmw4gCLhBwgdMoGSon74jfUEDxxFUlt8uKwSgIZEmpfmr+TeK07g05XVjBpSRMyrwwxYNHoB4m6AhoLhLPm8mnFn3YhtgKMtktttsKdRhPofQv0Hr4GfaZkkP3iVCRdPw9nDZAOtod4NUvjl6zAAR5tN+yO1Zcq0UooNVXHmf7QRgDnvrGPahIPIDdktXqiqTIOPV9fw4tyVjB5azMQx/XaYWm8Yitzotm7L3GgA2zZZtbGem36/AN/XnDzuAM6aNBik9SREh5Gg1ENZliJQt4bKN/6GGckjd+yZONHeBEsOxDAUvrIxUZz3xWGU9IpQXZticL88csM2E0cUUv38vVTFq7F69aFg0tdIBvL42Z/msWJ9Hb8yFL+5ehKxoEnA0Pi+xnV90maE3KNOpXHxXBoWz0NZAXKP/BLBUBgnqQmFbJSCVMrdZSKG72uSRqal5no+7RsN0uREtq1hUgrCgcx0dc/TKJUZx/J9cF2v6VjrbUHQ1XDXwwvRGj5cVsnQAQWZGY1bym1ozffOOpyHX16MUorzvzQMpTUvz1/V9JhX3l7NWZMPbkc9mmdZxpbp95nXavv67I5SCts2mt4rIXoq8+abb765uwvRWomE06NTtwBEo0EaG502Pz+kHPyK5QRLDwTfI1jcn0AwQPzdlzHtAEkVosE1eeP9dRQXhDlhdB8OsCuJ5YTBS+E31BA99FjI602FWcarC9cy6cj+hIIWS9fUcPzI3pSGkqQ+/DeW20CooIjXPihnyeoaBgwbRsHwceQfMxU3vhm3cg2R0j68+PY6kokUJXkWlmHg6W2THiKWi1G1DHfpfGLFZXhmZg1Tm1+/kMGg3hGUYXPBFw6gX3GUtJvJeuEpg38vXEt9Ik1ZcQytDF5ZuIbahjRlRTHwNa6vef6tFU3nO3ZEbwpzg02fK60zEynGDC3h8IOL0J6fyRyhFP/5ONNCG3VwEcccWtq6zLgtoEyDJWtrWbiknAP75aGV4pX/rqHRcSkpjLLzCxeNBqlrdHhl4RrSPhTmhzu8TJ2tvd+HbBCNBvf+ILFX0lLqoQzLxjctKv7+SwDiH75OnwtvIzr0KDZ/OJfaof25528LWF/ZAMDXTzuUY4eWUvfm4+Qdfw6h/oey8fHbiF54H1fdOxfX8zEU3Pm/x/P56hoO6gUbHrmpKd9d7pe+w38+DrPo8wqWrK7hf79QQtVrj9D46XwAwhuW41rHc/tfl3DtuYdw9LBiHC/z8TJNA2rWUf74rQDULXyR3t/8OXVe5kucucs30brlGRDSacXYQ0o4ckgRlqmIOybg46G46fcLWFseB+CSaSNYXxnnuTdXNL0OU8b0Rfmar540jJfmr2TE4CIG98vHS++4l4fWGne78jiOx+GDi7j7e8dTE09xcL98tOt26AxAyzL4eFUNdz68EIDB/fP5/d8/YsOW9/E7M0Zy7PDSHSbDbK5Lcu0Dc6mqTQJw9QVHcNjAXvtEdnKx/5GJDj2Uo23c6o1Nx15DDVprvLoq/HA+yjDYUNXQ9P9rNsWpb3RwG2ozXTzxapRlUxtPbelOy9yAJ1Nprp/eFxo3NwUkAK98OfmxzBjL6k0NaCBdsbrp//2q1RTlZILQ8o0J/HRmC/NAwCQYtEjXbCurn4g3bblumgY5ZiPphU/C0jfJCbRsMzytNbUJRYNjUpswts2KU4r1FfGmxy1dW4OT3tadtWxtLVqDdn2mHNmPX15xAl8/eRh+uoXX9XyKcwIM6ZODn3Y7vEFiGAbL19U2HYcDFhu3ex+Xrq3dlltwC8/XTQEJYPm6OtntVvRYEpR6qHTaIzJ8AmYsH4DY6C+izQB2yUDs+g2EDI9zpgwBIBq2+cLRAyiMKnIOn0zSDxAcOBIzmk++leLQAwoAOLBPLn3DCeJ/+xG+kyRv7BkAGOEcco44mc/WbMY0FF+dNAADRa+J52cSsxoWkePOYe6nNeTnBJk8uhSsIIaVSZz6yL+WkBowltABIzPlGX58ZlsKIGIk2fS3/6NuwT+o/ueDJBa/SSDQjga8rznvpGFN9T5zwiCG9M+8RpGQxZcnbkvbpD2fgtxQqxff+r5ucWqn1nJdjylHDyA/lmlFxhvTnDtlKACxsM3pxx+0Q+sNIBgwmTr+QADyY0GmHD1AWkmix5Lcd92kI3J9maYiaqRAe7jYNLoWgYBFiAQeikYdxPUyeewCBoRUgpRnkXKN7Tbt82kgjOMqLBNyTAfDT2+5G1do30crk4QKk/ZA4RM2PUw8DDcBykCZFgnCJN3MJndR08MhwCsL1/KnFz4BoLRXhLu+M44ojXhYNLiZiQq5tsO6By6FLTvcxkZOJDThm6TasFZrK2UaeDozg87Ex9dq27HecVFyNuZcM00DD4WnNVsToHsazN2UH7bUoboh8xilMGndwutskI3vQ2tJ7ruOIWNKPYxhKJRSeF4mEWudt+POrI7j4rB1ZpqHCeCDAzjbZWbwPJ/6pudqTDTahTrXYvcfC2/LI8E1TNJL51P9z98DED1sArEJX0P5Fr4P9a5BIJDZ2G+ripoEHgZ1bnjH8mqTXpMvpPrff8SI5JI79kwa2jl7TG/J04a/rdQ7Hme3rQFl+1VQW9/HZsvv+U2P7wl1FKI5EpR6kIjlYqRqwPcw80qpS6h27W9jmYqImQLXwTeDNLgtW+djapf6z95uOk4sf5+8CV9l+4+T6/p8eeLBvLO4nNqGFBefcRhK77qWKemahIeMp+/QsbCldec3k2tPCLHvk6DUQwQCJrr8c7Bt6t55GbuwD7ljTqY2teueQy2hlCKi69kw81r8RJzIkKPJ/8LF1Kf3fj5tBYmNPJHEikUAxA4dj9ppYN33NRHb4BeXT0BrMNDNjt0kXJNE032+BCQh9mcSlHoIQ/tYsTw2PnIzfqoRADNWiD14QpuyIliWQeMnCzIz4YDGz96mcMqFKLX31pL2NQpFn6//FO06uDUVu31cVyVOFULsOyQodQOlFL6TJGT5OL7ZokkbrlYEgxH8VGLb3+KbsVRbp/5qgr0HNR2ZOYVAZuJBXTq4x25B19OE8kpY//CNoH1iwydgDBjVxnIIIcQ2MvuuiymlyLFS1Mz+E9p1KJjyTRqItWi2VCzg4iz9D9Wv/gW7V29KzrwCx8ohkW7bzP68QIp0xWrS5asIHzgys/bn7eeInngRifSeg13A9IlaLq6TRFth4i3o9muOZWWyfbuu16HrfkzLwPPB3MO2E/vKrC+pQ/eT2XcdQ9YpdbGg6bL53zNpWDyPxs8XUvn3nxMi2ezjTVMRsdKEbB+HIFZuEaUzfkjeMaez8fHbsXVmkWpLG0zbPy7uhQmUHkB40Bi8ZCPls36e6c7Tew+Qjmdg5RZSr3PbFZBitou18QP0p6+Sa6U6bNGnYVs89upSfvH4e5TXpTJZJYQQWU++qV1ModHOti4430kCGmVktkSoTXooy8yk3rEg4lRiNlRg1a4hohKkeh3MWreAxl5DMPsegmEY5KQ3kWclyAulaW43BdM0ULZJdaMLlokdsNBa4ztJvPoqKp67H2XZFH7hmwQsiKXLyQuksM3Oa5EGAiappQuomPUzNr/6J8qfvJ2IkeqQ8855dw0vzV/JR8uq+PHv5uG1uZtz/6IMA08Z1KU82ZpDdAsZU+pijg7Q64sXUz7rLnTaoej0y3CMMJvrHK7+1Zu4nuawQYVcee5owkYKt7acmnmzSK3/nMj0G/nZv2pZsnoztmXwi8vOp/adF6ib/wzKDtH7KzcSzSmk1ts1MaSL4vs/m0M8kaakIMyNF40lL2LRsOgtUuuWUDjlQrTnogIhNj1+C86mlSgrQO9v3o1n5DfbXWqZaksg0Vu2umj5D5lCk1r32bbXpmINHdNQUiS223Aw7fporZGwtGeWZbJ8Yz3/N3MBWsOEw/vy9VMPka05RJeSllIX8zyfBiOP3l+9meKv/IRkqAyUwfwPN+BuSV3z0bIqfJ350TbCMVLrPwfAD+WzZPVmIPND+9/F5aQ3ZRKN6nSS5NpPcWvKM9mst2OaipUbaokn0gCUb05Q25DC8zxCBx1OYsUHlD/zC2rmPwPax9m0MnNO1yG58sM9dn2F09VsmHkl6359Kd6yBQStls8EdD3IPfp0VCAEQN5xM0jr9n8k02mXL40byKiDiygpCHPNBUci9/x7pwx4deGapnG9tz5Y3/J+YSE6iLSUuoHn+VjRfDY31pPJpuBz5KGlPP3a5/gaBvXLw1DgqBCBaD528QDSFasxnHoGluWwamM9hqEYPbQEvXFLIlHTIth3CEZOIV5a73Q9zcDeuYQCJknHIz8nmNm0Tnm4kRL6XPxL3NpyAsUD8ZXC7tWbdPUGMCxCA4bT0Mydsu+lqZ33FH4yM61887//RJ/BR5FqYQjwPJ9ksIg+F98Lvo+rMqmS2ktrMDyf7599OL4GS4Evd/t7p+G4kX148/11AIwZWtzjtsAQPZ/Mvusmu8w2MhSOp6muTzKwLAc8TSrlkhPUWNrBqVqH1as39cRYUx6npCBCyDKIGQnc6rVYucVgBWn0Auxu2ZJhGnjA2vJ6+hbHCDi1BMMRGtIWSkEs4OPXbkCZNlYsH6dyHYHi/qQJkEzvfvZaUVGUzXNnUfP63wCwi/pTdPaPibuBXR6brfaVWV8dVgfDoMFxqYs79C2Oojt4RmRz9pX3QbSftJSyha8JmYqhRYraNx/GCITJPeIU6h0bCGLkD8ZL+4BH/8JIJlO161FLACM3s97Id5r/9fj/9u48Our6XPz4+7vMTGYmE7JCAkJQWSoo4FIFRBHQiERAuS7A76K91GO911turS2up+31aqXWilKrvW2tu7X2KlKsIFRBWRQEEUQQwxoCIRASkkxm+y6f3x8DY5GwNcHMwPM6x3OcZL6Zz4d8M898tudxHZegx6FvoYPdsBFvYVcaLQ+g8BmK5k/+RsOS1wHIGTSO0PmjiGxcSXz7ekLfLieeVcTXqztomk7gnBHoWUHshlpyzh9J2PUhR2UzmOuS7dEJ5ftx/4lD2UK0lgSlNJKlJ6id9Tix7euBZI0k/yU3EbO05O45PZm1++tlE440atQ0DZ9Xw6s72Lu2sGN/oT1fSQ/yx91F2PWgK4toxYrUNXbdDuzdm9j79tMANH/xEZ1vfQKLQzdQNCVMgj0H41c2ljJOWEkH8c1RilblVBSiNWSjQxrRlIvduBcAM6cI1fNS4q6O7jWYtWQLv5+9jmZLHXbjgdeEkBkn20xgGDq6rpHjieON1RJZPZ9IxVdJVOPVG9FJTsk5mofsfsNT3wueNRiroTb1WFkxcFsugpfttQkveZXqZ39E84d/JvsIRfo0LXlQVgrQCSEOR4JSGkngpWDkrRjZeWRdez+/mFvP9x/7gI/X7SZhOSz6dAf3Pb0Ep4XNzV4TzLrN1Dw/ldrXHiBIE37Tpunj2UQqVhBeu4hgz2+DnhwcB3p9G2f/Tre4reHpfTFdvjeDzrfOwOjcl0DPb+PtWApo5Fw0Bls7dJQEQHgv4VXzcCONhFfNg/DeFjdsGYZGyIiib1tO0N6Lz5CpISHEoWT6Lo1YjoZWcCadb3mMV9+vomL7PgCefn01D/37xcz6YDNNkUSL13qJUzNrOm60Cae5gX0LXiL3iltwmhswczth1W4nvPYDunzn52B4UN5swvZXmRgilgnkJB/YybpNBdffhw5YSj/srjjNhFPIMAAAHPFJREFU4zvkcUszP0EjTvUf78KNNIKm0/m7j2KZBRm/YUUI0bbaLSg9+eSTzJkzB4ChQ4cyderU9mpKWknYGugGOdlfvdln+Ux0XcPnNbj9X/ons0IccqWGnpWNG03uYNIDHbB0L6ELynEjDXQYOJbE7m04iRhWTkcS1pHb4bqKsHv0XXSOJ5uC8tuJrFtMoM8QHE8IWpjBU1Y8GZAAlEt81xb0boUSlIQQB2mXoLR06VIWL17MzJkz0TSNW265hfnz53PFFVe0R3PSTiLhMKRfCfVNMSp3NTGx7Fvkhbw89eNh6NBiXaIYWXS68T7qF7yI5s8hetYo/u+dCiaW9cafU4in0+kolXxeohWlxr8uYpt4ul9EqPS85Dkj6zDngcwsfF2+RXzHF+iBHLK69aHpH/ph/EMJc9duffs0DbJNC+JhdJ+fmOsj7shalhDprl3OKVVUVNDc3MyAAQMAeOCBBygtLeXmm28+puvT5ZySrmsEjAQ6LrYyDpriCpgWpubiohNxvKn2GoaOq2kowNA1XDc56vF7dEwnktzsoHQitodglobruqDrRKzkNbqmoWuKA1WKsg0bzbWJKg+2MgDFyvW7WbVxL1cN7s76LXWc26uIwg5eglocHQWahu1qNNve1C4rXdf2t0tDQx02tUzAtDE1J7luZCdQ6Fh6FtGjpBfSDT3ZZFyCpkvESu7U0zQNDB3LUWytbuCzjbXcMPxMDNcmZh99ydMwdRy1/98TlTokGzQtGv/+eyJfLgfdpPhfHyAWPO2rUuOGhoOObmio/b8DTR2+EGE6O1nO+JwMfRCtZ/zsZz/72Tf9ogUFBRQXFwOwdetWpk2bxp133klubu4xXR+NJtLioHnIY1H7+s/Zt+BFiO4jdMbZJFyDbI9N08Ln2Pv2UySq1pF/9iDijoFhaOxttvjRjEW8+f4mSks6ULUnzK69EfI7ZBF5ezr185+B5nryep5N5NO5NK+axzqnFN308quXP+H5t9cTjtq4Ch567mO+3TMX3YrwygfVPPrKKj75spYJZb3o0TWf+55ewqov9/D35ZVccWEp4dfuY9+i1/B0KMLavZnskq7Enf1v/KbJQ88u57m/raOhOcGA3h3ha4E/6LFpXvoqe2fPILplDYHT+1H9/D0ESk5H61By2A8KuqGzdls99zy1lPnLq7ioXzeyPAZKKXSPya/+9AnP/PVzmqM2Q887jdXrd9AruwE9kIPjHn50Yxg6e5ri/OjXi/jros2c07OQglByTctv2NTN/d/kE5WLZph4uw/AcVx0XSPmwF2/Wcyr8zfQIdvH2k17CccsOhdlZ1wWg2DQR+Qwa42Z4mTpg2i9dt19V1FRweTJk5k6dSrdu3dvz6YcN8PQiVd9QWJXMvdc89oP0OxkCYqYq+GeO47A9Q+gEjHiOyv2T09pPP+39URiNrajeHHOOnxeg85FQd5dXoluJtdwmtctRtkJ9i36CxT3ZO32ZjZU1rO1Orkm8/ePK+mUH6ApYvHK+zuxfR2Y+1ElAJW7mli1bgcaioSd/NTvKti1qy55plW51C14CV/Hbjj1O9G05Bmo9Vv3smlHAwALP6ki1sIUn+7ECH/6dwCsPZXEqjbgLepKeM17mBx+kcpW8LuZn+G4inDU4sW561FacnS2Y0+YtZuS2+A/2bAbXdewFTStmo/3CD8TwFHwx9mfE43bWLbL72au5cDsoYuOr3Ov1HP9PS9IjZJMj8HMhRupa4yhFLw4Zz0XnNWJ377xGZK2VYj21W4bHVauXMmUKVO49957KS8vP65rCwqyT1Crjk8s1jH1/5rhwfT68Bk+npn1Ge+trKKkIMj/TPoxXq9NKD9IPOHQuTDI6opk+fCOeQHi+9/8OxcFcXY3J3+YYaLpOnogB23vNr7VZQBZwUDqtXweA9NMfp7onO/DY4DPa6R+VqeCIDlGnL6n5/P5ljq6dgpxekmIxsbk2SOzQ0fcWARfSQmF+cl/y+Lmrz6lekydLJ9JYa7/oP7aTVZyd52VLC/hye2IE4sQGnA5wQ4hsvWWp/AawnE65gVo3P8aXYqy6RDyY5o6MSe5/qP2rycV5GTR9awcrAW1eLweivIOPyUSjduUFAZZt6Vuf78DBPxeQsFkcO90w10kdm3BzCnACBWQnRVMXdu101f3UEGun1jCpmOeH49HJzc/QKY5GaaOToY+iNZrlzWl6upqrr32WqZPn86gQYOO+/p0WVPymzaq5ktimz8lu/8IEv6ONDs635v2buo5/zGuL0PP6UhzPPkJXPeazPtoG+GoRdnAUmzbpaYuQmGOl9zGL9GqPiPUfxjRynVkdelFeO0HGP1GEvMVsrFqH2s21jJyYCkVVfsIh+MM7xvCb7jsimcxZ9kOzj69A/1Py2Lfq/eTVfafaJ16JrNBJCJQsQgVriN07uW48Sh2sJiYsz+QGDobdzTwyRe7ueLCbhSEfIckMfUYCl+8lvCq+WSV9sXXqTuOlcDNyjtiIlVd13A0nTc/2ERutpcRF3RNpbDRdJ3ttWGWrKnmsvNOoyRgwYYFZA+4grDjP0q2CtBMk7kfbSVuOYwZcgaa6x50ja5rKKUOmZHTTYPFn+2kuraZkYNO5+N1u7hkQBe8R6hSm65OlvWYk6EPovXaJSg9+OCDvP7663Tr1i31tfHjxzNhwoRjuj5dghKAx2Mkp5xsN/lmZhj8568WEIkl90X//N8H0yXfj+OoZBVZu57m1e+SdUZ/vJ17YSmThKOIJZzkCEgD13XxE8Vuqseb1xHXdUCB8mXvP5iqsG2FpukodeBNWEPXwdBcrO3riG35lOA5w0j4i7AcjUDAg2HoKKVwHEUiYWPbB7/5mmZy3SvVlxYYho5p6riuIjc3QG1t0zEtwWiahtdroBQkEgfvGTdNPTm96bjouo6mJXcgHuut6fWa+6+xj2s5yOs1yMnx09QUQymFbbtpc18dj5PlDf1k6INoPckS3sZ0QyMcd3lvxXb6nJ7PGZ1zUjvZAkaC8MLniG5ZjRtrJqtrHzqMvoOI7QFDx3ZU8g1aKRzHJcdrUfvmY8Sr1mPmlVD8//6bhsTRzw4deJO3beeYc9GZpo6PGJpSxMhK1XY6kpPljUT60P5Olj6I1pOMDm3MdRTZXp1xl56B67qp0YhpgEez8Xc/hw4XlrPvo1moRDxZWc3QeX7OF7z/SRXZfg+/nHIJfkNDxZuJVyWTs9r11cR3bcIo6nPUQGPb7iGjoCMxDB2/s489rz+Csi2Krv0hsaziYwpMQgjRliQonQCuqw6ZogoaCXb+4ce4sWY0w0Pn7zyM7vWjsHCVh/c/qQIgHLV4/5Mqrh5Uim4G0DxZyYSomo4nvzO2lkyyquw4mFk0/8MZqH+WV0tQP+8PWLXJNtT+dQYFN/wEu4Ws4EIIcSJJUPqGONEm3Fhyd51yLKx9uwmvX0Kw90C8nQdQlOdnT30UgIv7FpKlorhA51t+ReSLj8gqPZuEESKoR6l+4T6cxlq8nbpTdP29NLYwpacZOpabPJxqai1ngThAoaF5v9pxpnuzQLZGCyHagQSlb4juz8FT0AVr7w6MUD56fmcS597Aik319Muz+MXtQ/hobTXnnplLTv16dv7pacxQPh1vvB+975VE7GRu8MSuL/F07YMnrxvs+gIVawb94KCkGzofrqvhf2d+hqbBHePPo/+Z+TiHmdKLOyb5V0xGM0yUHSdvxL/RTBaQWbvQhBCZT4LSNyRse+k44adoVhTN9NLg+vnhb98nbjmY72zhqanDueScEvxE2fnCk8nRVN1OGpb+H1mX/Buuq9A0sDv2ZlllgBXr9lF23tmcl5ULXzsIb7uK2Ys2A8nzP7MXb+bsM/IP2zalFI2Wj8Cw7wKKsGMk0xsJIcQ3TOoptTGvBwKGhc9z8NeVAqw4O569i6rf3UH93n3E95/VsR3F7roIHhI4hgffiFvxnXUJAGaoAI9uk+VJTsVV77P449yNrNlYy6OvraPZOvRX6DF1zjmjAIDB/UqYeGUvsjyHPO1r7VPELI2YpR+0RqVpkOVxCRgWppEeU3pZHpVW7RFCtB0ZKbWBA0Xt/IaNvXEZ+z5bSKD3ReSePZRGK7kRwes1iG5YjUokUxHl+TUG9CziXwZ3olOujw4hnebdVby31eDj9SaXnz+KC86+nOyiQva8+Uu8BV3IGTKByq+l/7EcF+NrlVwNTWPipZ248sLObNsT49V5FQzoVcjIgd1Rx5mBO+S1afjgFazaHeQN+1f0nK4kDl9c9oTL8dk0LHoVa892ci+biN6hW7LchxDipCAjpVbQdY2cLAfNgNqmODHNS9PaRcR3VlC/4CWsvVUEzQRZhoOnYTtZJWckt4ADQTfM/Tf2oPDjp4m++F80vvtHfHlFvPTOl2yorOc3M9eTyCtlxzM/Jl65jqZV84l+uZTS4hDDL+hKXsjH9SN64vce+rnCth2CHkW2F57486dsqKznz3+voGp3+LhKkXu9BpG17xNe/R7xHRuoefV/yNLaL2mmx2MQWb+Y8Kd/J75jA7tffbBd2yOEaHsyUmqFoBGnsSnOnb9bQ31TnFDAw+O3/SeJF34Aro2yE2hOAo8VZ+cL9xDqP5zOk/4HJ9qImdeJ6MaVqYSukQ3LyLlkfCoPHCTT3Sj7q6SkyrbQlWLSlb2ZWNYbQ6PFkY/rKiJGB/AdvFXcctwWS5Uf0T9coFwHWigv+E3RNA7+93ClpLoQJxsZKbWGY1HXGKe+KZmgtClisbPBwde5BznnjwTXBcODVV8DQGTjSnb/7SnMwm4YgWy8nUpTP+pAQtfyQaUUFwSYUNabpuY4HUbdjplXQqDXhQTOGoJlOSjHRXfdI27zth2FR9f5t6v7UFIQpOyibpzROeeYMzwETRtvpIasLr0omfQAnsJudBz3I+LqKItTJ1Ai4RA4+zICPS/AzCuh6No727U9Qoi2J2mGWiHHk6AxkuCH/7uafeE4Qb+HJ++8jByacONRdH+IsOMjaCSwdqxD82SBUniLz8STk09kXz1qz2Yimz4h1G8YBPKJ4iVhg2f/OSO/oTDdGEoziDrmcZf60Q0dW5EcVR1jolGvR0NtXEzdO78HILv/CDoMuZGY8h60ntQeqWE0Lbl2pykHS/O2ej3pZElvI31of5JmqG3I9F0rhB0foZDB4/91MbvqY3TKD6Ipl6iRgxHIJmEnp+ASuhenoZa6915E94fIu/xmsnsPxNJ8mCV9yCruS8RycCwXUOiAYzvoQNyFeCsyK7iOmyyhfhzXGMqmccNHqcfRjSsJXXxDu25wOEAp9mckl1tXiJORTN+1gusqmmIGytXo1MGPsh0CuoXa8B7N7/4Of2wXHhN0N0GkYgVGKJ/iG+8lsf0L6ha8hN+qI7L4JbxWQ3t35SC2ZpJ9zrDU40CfITgSBIQQ3wB5p2kDB2ZATVPH3vkFdfP/CECkYgWdb32CGD6C/Yajok3UL/wT0a1rAHDCdXiLStn92kMU3vhTwhw9A/g3wbIU/q796XLbr1GWhcrKIWy1XMDvVGMYOmjJMiFWC9V5hRCtIyOlNqRpGnbT3tRjZcXAdUjYYJSeR+BbA7Gb61Pfd5rq0b1+7HA9WjvuamtJ1DZodEM0GfmELfnsAmCYOuGEw29nreX19zejeyRQC9HWJCi1Idt2CH5rEN7i00E3yL3kBmw9uR4UtQ2aCVIw8nvovkBybemyiTRXrKCw/D9IHGWUpBs6av9/hmQyaBeO0rjv6aV8+Fk1b76/ibkfVeLzScAWoi3JX1QbUgqabB8F/3IPOmApg8g/THvZtkILnUbxLdMxTQPHhYIxPyChPMSPsItMN3Q27mzk0ZdXEgp4+e9bBxH0aMe8vVu0nXD0q3NS+5pix70bUghxZKfMSEnXk+W4DePYu6xpGh6PgWm2fI1pGng8xkEHUl1XEba8NFpeovah0zuWDU2WDzM7j8a4SZPlPWJASjZe4/1VVWQHPOzZF+WFt9enMkP8s3Rdw286+E37uLI8nMp0FD+ceB5Bv4fS4hDXDe+JfZxpm4QQR3ZKjJQOlCj/6JMd9OtRSFFOFuooWbANQ8dS8N7KKrp1CtGjc85B53xCXpvEtpU4kUZyzhpCk936YnstN0Snpj5KcUGQUYNP5//eq6CgQxa6Dv/s26Fh6AQJUzf3d6DpFFx5K0340+LsVzpzHZc+3XKZccdQ0MBEyWhViDZ2SgQlV9O5c8ZC4gkHTYNf3zmMoEfnSOeGHTTueWoxe/YlC+9NnXQ+Z5fmY9sOPq9O86fzaFj8GgCRDcvJG/NDmt22zS6gaRpNUZu7f7M4WYJi0WYe+8FQAj4TqxWHhnzEqP3rE8R3bABg75ynCV31faLuKXE7tIrruKnyhzJGEqLtnRLTd9G4TXz/9l2lYGft0ROTukqlAhLAxqqG1DWasrFqNqe+Z9VWop+Agni6rrFjT1Nq3SKWSBb6c63Wn2JVzsE59TRZHBFCpIFTIigFfCb9ehQC0KUom56n5eIcJeWOrkH5xacDkBP0csWF3VLrB7Yy6TD4OjQzuWMu99LxJGj7HGyO49K7NJ+SwiAAA3oW4fO2fhtyHB+Fo7+Pt2Mp3k6nUzDq31uVNUIIIdrKKZH7TtNA6TqOqzB0HV25x3a9sf8aQ8dAHVRO3GMo/HoClItFy5sajuRYc30ZhoaDjuMm6yZprtsmO74MQyOLZG2nGP6jBumWnCz5yqQP7e9k6YNovVNiEUEpwHExABzn2CfaDlxjO4esH1iOhuWc+NFFciHd2d921WZHbB1H0ZwaHUnpcyFEejglpu+EEEJkBglKQggh0oYEJSGEEGlDgpIQQoi0IUFJCCFE2pCgJIQQIm1IUBJCCJE2JCgJIYRIGxKUhBBCpA0JSkIIIdKGBCUhhBBpQ4KSEEKItCFBSQghRNqQoCSEECJtSFASQgiRNiQoCSGESBvtFpRmz57NqFGjKCsr4+WXX26vZgghhEgj7VJ5tqamhunTp/PGG2/g9XoZP348F110ET169GiP5gghhEgT7TJSWrp0KQMHDiQ3N5dAIMCVV17J3Llz26MpQggh0ki7BKXdu3dTVFSUetyxY0dqamraoylCCCHSSLtM37mui6ZpqcdKqYMeH42uH/tz09nJ0A/pQ3qQPoiTRbsEpeLiYlasWJF6vGfPHjp27HjM1+flBU9Es75xBQXZ7d2EVpM+pAfpgzhZtMv03eDBg/nwww+pq6sjGo0yb948Lr300vZoihBCiDTSLiOlTp06cccdd3DTTTdhWRbXXXcd/fr1a4+mCCGESCOaUkq1dyOEEEIIkIwOQggh0ogEJSGEEGlDgpIQQoi0IUFJCCFE2pCgJIQQIm1IUBJCCJE2MiooZXK5i3A4zNVXX01VVRWQTEo7evRoysrKmD59eju37uiefPJJysvLKS8v55FHHgEyrw9PPPEEo0aNory8nGeffRbIvD4c8Itf/IK7774bgPXr1zNu3DiuvPJK7rvvPmzbbufWHd2kSZMoLy9n7NixjB07ltWrV2fc3/d7773HuHHjuOqqq3jwwQeBzL2f0orKELt27VLDhg1T9fX1qrm5WY0ePVpVVFS0d7OOyaeffqquvvpq1bdvX7V9+3YVjUbV0KFDVWVlpbIsS02ePFktXLiwvZt5WEuWLFE33nijisfjKpFIqJtuuknNnj07o/qwbNkyNX78eGVZlopGo2rYsGFq/fr1GdWHA5YuXaouuugidddddymllCovL1erVq1SSil1zz33qJdffrk9m3dUruuqIUOGKMuyUl/LtL/vyspKNWTIEFVdXa0SiYSaMGGCWrhwYUbeT+kmY0ZKmVzu4rXXXuOnP/1pKr/fmjVrKC0tpWvXrpimyejRo9O6L0VFRdx99914vV48Hg9nnnkmW7duzag+XHjhhbzwwguYpsnevXtxHIfGxsaM6gPAvn37mD59OrfddhsAO3bsIBaLMWDAAADGjRuX9n3YvHkzAJMnT2bMmDG89NJLGff3PX/+fEaNGkVxcTEej4fp06fj9/sz7n5KRxkTlDK53MVDDz3EBRdckHqcaX3p2bNn6k1v69atzJkzB03TMqoPAB6PhxkzZlBeXs6gQYMy7vcA8JOf/IQ77riDnJwc4NB7qaioKO370NjYyKBBg/jNb37Dc889x6uvvsrOnTsz6nexbds2HMfhtttuY+zYsbzyyisZeT+lo4wJSq0td5FOMrUvFRUVTJ48malTp9K1a9eM7MOUKVP48MMPqa6uZuvWrRnVh7/85S+UlJQwaNCg1Ncy8V4699xzeeSRRwiFQuTn53PdddcxY8aMjOqH4zh8+OGH/PznP+fPf/4za9asYfv27RnVh3TVLglZ/xmtLXeRToqLi9mzZ0/qcSb0ZeXKlUyZMoV7772X8vJyli9fnlF92LRpE4lEgrPOOgu/309ZWRlz587FMIzUc9K9D2+//TZ79uxh7NixNDQ0EIlE0DTtoN9DbW1tWvcBYMWKFViWlQquSim6dOmSUfdTYWEhgwYNIj8/H4DLL7884+6ndJUxI6WTqdxF//792bJlS2oK4K233krrvlRXV3P77bfz6KOPUl5eDmReH6qqqrj//vtJJBIkEgneffddxo8fn1F9ePbZZ3nrrbeYNWsWU6ZMYfjw4Tz88MP4fD5WrlwJwKxZs9K6DwBNTU088sgjxONxwuEwM2fO5Je//GVG/X0PGzaMxYsX09jYiOM4LFq0iJEjR2bU/ZSuMmakdDKVu/D5fEybNo3vf//7xONxhg4dysiRI9u7WYf1zDPPEI/HmTZtWupr48ePz6g+DB06lDVr1nDNNddgGAZlZWWUl5eTn5+fMX04nEcffZT777+fcDhM3759uemmm9q7SUc0bNgwVq9ezTXXXIPrukycOJHzzz8/o/6++/fvzy233MLEiROxLIuLL76YCRMmcMYZZ2T8/dTepHSFEEKItJEx03dCCCFOfhKUhBBCpA0JSkIIIdKGBCUhhBBpQ4KSEEKItCFBSQghRNqQoCSEECJtSFASGWPy5MnU1dW1+jnLli3j6quvPurr9e7du8Wf9e6776bq50yaNIm5c+dSVVXFueeee9SfKYQ4sozJ6CDEkiVL2uQ5rTVixAhGjBhxwl9HiFORjJRERrjnnnsAuPnmm1m+fDmTJk1i9OjRjBkzhjfffPOQ51RXV7NgwQLGjx/PuHHjuOyyy3j88ceP+3Uff/xxrr32WsaOHcuCBQsAeOONN/je977XRj0TQvwjGSmJjPDwww/zxhtv8Pzzz3PDDTcwdepUysrKqKmp4frrr6e0tPSg5+Tl5TF16lSmTZtG9+7dqampYdiwYcedF+60007jgQce4Msvv2TSpEnMmTPnBPVQCAESlESG2bRpE/F4nLKyMiCZqLesrIxFixYdtKajaRq//e1vWbhwIW+99RabNm1CKUU0Gj2u15swYQIAvXr14swzz2TVqlVt1xkhxCFk+k5kFE3TDimcppTCtu2DvhaJRLj22mv5/PPP6dOnD1OnTsU0TY43/7Cuf/Un4roupimf44Q4kSQoiYxhGAZdunTBNE3mzZsHQE1NDe+88w6DBw9OPce2bbZt20Y4HOYHP/gBw4cPZ9myZSQSCVzXPa7XnDlzJgCff/45lZWV9O/fv207JYQ4iHzsExlj5MiRfOc73+Gpp57iwQcf5Ne//jWO43D77bczcODA1HMmTZrEE088wWWXXcZVV12F1+ulV69e9OjRg23btuH1eo/5Nbdv384111yDpmk89thj5ObmnqjuCSGQekpCCCHSiIyUxCnrD3/4A7Nnz27xe9/97ncZM2bMN9wiIYSMlIQQQqQN2egghBAibUhQEkIIkTYkKAkhhEgbEpSEEEKkDQlKQggh0sb/ByHrUIh7JWQKAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 435.725x360 with 1 Axes>"
       ]
@@ -733,27 +752,27 @@
     }
    ],
    "source": [
-    "sns.relplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=tips);"
+    "sns.relplot(x=\"day\", y=\"tip\", hue=\"smoker\", data=tips);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fe0c110bdd0>"
+       "<seaborn.axisgrid.FacetGrid at 0x14efb4c5088>"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 419.1x360 with 1 Axes>"
       ]
@@ -776,12 +795,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -824,22 +843,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0c1192a50>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14efba704c8>"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -854,22 +873,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0c11a5850>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x14efbb11348>"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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SG/tYee/1F5yLWNHp+HDn+b/EL+b6MdkkxnX/XNHR6eWEtZ2OTh8er49Orx9nh5ddtY3sO9aM0aDj5usGkJUa/F7BhY5/KZH82odC6ldXNNffk0tDMmdxjDpS14ZRr2WAJbHPvka8Uc/IC/RI+s51Azhc38avXt9F9acnuPGafPKy+q4OIcSlyRATMepIXRtDcpLRadV5C+RkmJg1eRDJJiMfflaHs8OjSh1CCAmCmOTx+jlhtTMs7/wpKa8kU7ye6Vfn4fcrfLJPZj0TQi0SBDHolK0dr0/p1RvFPZWSaGTiiExONrRzwtqudjlCxCQJghh0wtp1E2xQiPMP9LWxQzLISInj0/1WfDK/gRBXnARBDDrZ0E68URdSb50rQavVcN0oMy63jyMyeJ0QV5wEQQw60dDOAEsS2h4M+dFXcjJMpCfHse9oi9wrEOIKkyCIMX5F4VRDOwMtkTWGk0ajYeyQdFodnZxulHkrhLiSJAhiTGNrBx2dvogLAoAhuSkkxOnZd1SmvxTiSpIgiDEnv+yZE4lBoNNqGDM4jTPNTs62u4PvIIToFRIEMeZkgx0NMCAr8oIAYHh+KhrgaJ3cNBbiSpEgiDEnG9qxZJguOUS0mhLi9ORkmjhab5ebxkJcIRIEMeZkBN4o/qZheSm0uzzYzrrULkWImCBBEENcbi+NrR0RHwSDspPRaTUcqYvO0R+FiDYSBDHkZEPk3ig+l0GvZaAliWNn2vBF6RDkQkSTkIKgsrKS4uJiZs6cyfr168/b/t5771FaWsp3v/tdli5dSmtrKwAVFRVMnTqV0tJSSktLKS8v793qRVhO274MAnNkBwF0XR7q9Pipb5JnCoToa0HnI7BarZSXl7N582aMRiN33HEHkydPZsSIEQC0t7ezatUqNm3aRHZ2Nr/61a94/vnnefzxx9m7dy8rVqxg7ty5ff6NiODqGp3EGXVkpMSpXUpQuVkm9DoNJ63tDIiC4BIimgU9I9i2bRuFhYWkpaVhMpkoKiqiqqoqsN3j8bBy5Uqys7MBGDVqFPX19QDs2bOHiooKSkpKeOSRRwJnCkIddU0O8jIT0UTQ0BIXo9NqyTcncbKhXXoPCdHHggZBQ0MDZrM5sGyxWLBarYHl9PR0ZsyYAUBHRwcvvfQSt9xyCwBms5mlS5fyxhtvkJuby5o1a3q7fhGGukYHeVkmtcsI2UBLEh2dPhrPdqhdihD9WtBLQ36/v9snSEVRLviJ0m6384Mf/IDRo0dz2223AbBu3brA9kWLFgUCQ1x57S4PrY7OqJoScoA5EY2ma5A8IUTfCRoEOTk51NTUBJZtNhsWi6Vbm4aGBu6//34KCwt57LHHgK5g2LRpE/feey/QFSA6XfgPMYU7CXOkMZsjY8x/29EmAMYMywqppoZmJ8lJoQ9TbTLFYc4I/WxDCfH4+eYk6hodYR8fIue17ympX13RXn84ggbBlClTeP7552lubiYhIYHq6mqeeOKJwHafz8eSJUuYPXs2S5cuDaw3mUy88sorXHPNNUycOJHXXnutR2cETU3t+KO0C6HZnIzNFhl94ffV2gBINGhDq0mnw94e+iUZp9ONzecLvb3bG9Lx8zJNfLq/gaOnWtCEcfxIeu17QupXVzTXr9Vqwv4AHTQIsrOzKSsrY+HChXg8HubPn09BQQGLFy9m2bJlnDlzhn379uHz+XjnnXcAGD9+PE8++SRr165l1apVdHR0MGTIEJ555pmefWfistU1OjHqtWRGyGQ0oRpoSeLT/Q3sOdLEsFz1p9YUoj8KGgQAJSUllJSUdFv38ssvAzBhwgQOHDhwwf0mTZpERUXFZZYoekNdk4PczMSImowmFIkJBtKT4/j8aDOl3xqqdjlC9EvyZHGMiLYeQ+fKNydy5HQrzg6v2qUI0S9JEMQAl9tLi90dVT2GzjXAnIhfgc+PNatdihD9kgRBDKj7cpiGvMzoDIKstARM8Xp21zaqXYoQ/ZIEQQyo+3IO4Gg9I9BqNIwZnM6eI0345SljIXqdBEEMqG90otdpyUqLrh5D5xo3NIM2p4fjZ6KzS58QkUyCIAbUNTnIyTCh00bvf/eYIRlogF1yeUiIXhdS91ER3eoaHQzLS8HrB7cntJ43OkMfFxWmpAQDw/JT2HOkiVu/PUztcoToVyQI+jl3p4/G1g6mFuTi9njZsd8afCdg0rjcPq4sfAXDs6j4yxFaHZ2kJhrVLkeIfiN6rxWIkNQ3R3ePoXMVDMsEYO+RJpUrEaJ/kSDo56K9x9C5BmUnkZZkZNdhCQIhepMEQT9X3+REp9VgSU9Qu5TLptFoKBieyedHm/D6/GqXI0S/IUHQz9U1OsjOMKHX9Y//6gnDsnC5fRw+LbPdCdFb+sdvB3FRdY0O8jKjc4yhCxk7JB2dViOXh4ToRRIE/ZjH66PhrKtf3B/4SkKcnpED09gtQSBEr5Eg6MfONLtQlP5xo/hcE4dnUtfooPGsS+1ShOgXJAj6sUCPoX7QdfRcBSOyANgt3UiF6BUhBUFlZSXFxcXMnDmT9evXn7f9vffeo7S0lO9+97ssXbqU1tauG3l1dXXcddddzJo1i4ceegiHw9G71YtLqmt0oNFAdphz/Ua67PQELGkJcnlIiF4SNAisVivl5eVs2LCBLVu2sHHjRmprawPb29vbWbVqFS+99BJvvPEGo0aN4vnnnwdg9erVLFiwgKqqKsaPH88LL7zQd9+JOE9dkwNLWgIGff868fuqG+n+4y10ekKfx1gIcWFBf0Ns27aNwsJC0tLSMJlMFBUVUVVVFdju8XhYuXIl2dnZAIwaNYr6+no8Hg87duygqKgIgHnz5nXbT/S9rlnJrsxlIY1Wg8PtDfmP/zJHky4YkYnH6+fAiZbe+QaEiGFBxxpqaGjAbDYHli0WC7t37w4sp6enM2PGDAA6Ojp46aWXuPvuu2lpaSEpKQm9vutLmM1mrNbQxrkRl8/r89PQ4uLakebgjXuB2+Nj10FbyO0nXmZdowamYTRo2XW4iYLhWZd1LCFiXdAzAr/fj+acCc8VRem2/BW73c4DDzzA6NGjue222y7Y7kL7ib7R0OLC51f63Y3irxj0OsYOzmDP4SYUmaxGiMsS9IwgJyeHmpqawLLNZsNisXRr09DQwP33309hYSGPPfYYABkZGdjtdnw+Hzqd7oL7hSIzMynsfSKJ2Zysytc9WN81gcvYq8yBGpRmJ8lJoU9OE05bg0Hfp+1NpjjM37jpPeXqfF744y46/DAo5/zXWa3XvrdI/eqK9vrDETQIpkyZwvPPP09zczMJCQlUV1fzxBNPBLb7fD6WLFnC7NmzWbp0aWC9wWBg0qRJvP3225SUlLBlyxamTZsWdoFNTe34L/eCskrM5mRsNnVm1DpwpGsCl3gNgRqcbi/29o6QjxFOW48nvGOH297pdGPzdb8xPMzSdbbzYc1JZk0e1G2bmq99b5D61RXN9Wu1mrA/QAcNguzsbMrKyli4cCEej4f58+dTUFDA4sWLWbZsGWfOnGHfvn34fD7eeecdAMaPH8+TTz7JypUrWbFiBS+++CK5ubk899xzPfvORNjqm5xkpcYTZ9SpXUqfyUiJZ4A5id2HG88LAiFE6EKamKakpISSkpJu615++WUAJkyYwIEDBy64X35+Pr///e8vs0TRE1eyx5CaCoZn8s6nJ3B2eDHFyzxLQvRE/+pgLgDw+xXqm5zk9qPB5i6mYHgmPr/CvmPNapciRNSSIOiHGltdeH3+fttj6FzD81NIjNez67BMai9ET0kQ9EN1jU6g/w02dyE6rZZxQ7u6kfqlG6kQPSJB0A/VNXWN6ZQbA2cEABOHZ9Hm9HD8THT28hBCbRIE/VB9o4O0JGPM3DwdNywDDcggdEL0kARBP1TXFBs9hr6SYjIyLC+F3XKfQIgekSDoZxRFoa7JGTOXhb5SMDyTo/V2Wh2dapciRNSRIOhnWuxu3J2+mDojAAIDz+2VyWqECJsEQT/z9axk/f8ZgnMNyk4iNckok9oL0QOxcTcxhnwVBLkxdkag0WgoGJZJzRc2vD4/dmcnDrc35P3jDHr62fw9QoRMgqCfqWtykJRgIMVkVLuUK65geBZ/3V3PoZNnGT3cwI79oc9/cf2YbPRx8uMgYpN8Bupn6pqcMXd/4Cvjh2Zg1GupCWOCHCGEBEG/oigK9Y2OmLs/8JU4o44JwzP5+xc2ecpYiDBIEPQjbY5OHB3emLs/cK7rRplpdXRy+NRZtUsRImpIEPQjdU2xM8bQxUwcnoVep2XngQa1SxEiakgQ9CNfdx2N3SBIiNMzfmgGOw80yFzGQoQopCCorKykuLiYmTNnsn79+ou2e/TRR9m8eXNguaKigqlTp1JaWkppaSnl5eWXX7G4qLomBwlxOtKSYq/H0LmuG2Wmua2DptbQp8IUIpYF7S9ntVopLy9n8+bNGI1G7rjjDiZPnsyIESO6tVm5ciXbt2+nsLAwsH7v3r2sWLGCuXPn9k31opuuG8WJaDQatUtR1dVXZaHTajhubScrLUHtcoSIeEHPCLZt20ZhYSFpaWmYTCaKioqoqqrq1qayspLvfOc7zJ49u9v6PXv2UFFRQUlJCY888gitra29W73opj+PMaTRanC4vSH9QaNh1OB0jp+xy+UhIUIQ9IygoaEBs9kcWLZYLOzevbtbm0WLFgGwc+fObuvNZjP33Xcf1157Lc899xxr1qzhl7/8ZW/ULb7B7uykzdHZb28Uuz0+doXxfEDBiCz2HW2mxe4mIyW+DysTIvoFDQK/39/tUoOiKCFfeli3bl3g34sWLWLGjBlhF5iZmRT2PpHEbE6+Il+nvrbrl+S4EeaLfk2l2UlyUui/FMNpazDoI6p9vjmRje8dpL7ZxeC8tKDtTaY4zBmR9fzFlXrv9BWpP3oEDYKcnBxqamoCyzabDYvFEvTAdrudTZs2ce+99wJdAaLT6cIusKmpHb8/Ok/vzeZkbLYrM2vW54e6giDJqL3o13S6vdjbQ7+BGk5bjye8Y/d1+6TB6WSnmzh08ixjB6cF/fDidLqx+XwhH7+vXcn3Tl+Q+tWj1WrC/gAd9B7BlClT2L59O83NzbhcLqqrq5k2bVrQA5tMJl555RV27doFwGuvvdajMwIRmlM2B4nx+pjvMXSuwTlJtDk6aW2XOQqEuJSgQZCdnU1ZWRkLFy7k1ltvZe7cuRQUFLB48WL27Nlz0f10Oh1r165l1apVzJ49m88//5zly5f3avHia6dt7QwwJ8V8j6FzDcruOrU/JnMZC3FJIQ23WFJSQklJSbd1L7/88nntnnrqqW7LkyZNoqKi4jLKE6HwKwqnGh1MHZ+rdikRJSFOT06GiaP1bUwckSkhKcRFyJPF/UBTawfuTh/5lv7ZY+hyDM1Lxu700NQmD5cJcTESBFHM6weH28vhujYAslITLtm/PkrvuV+WwdnJaDUajtbJ5SEhLkZm4ohibo+XHfut7P5yesb6JgeNra6Ltp840nzRbf2V0aBjgCWRo/VtXDfKjFYrl4eE+CY5I+gHztrdJCUYMMhcixc0LC+Fjk4f9V+OziqE6E5+c/QDLe1u6TZ6CfnmRIx6LUfqZIgTIS5ELg1FOZ/fT5ujk4GW6H4Cuy/ptFqG5KZw+HQrbo+POEP4DzZ+k9ffdWkuVHEGPXLCJiKVBEGUa23vRFEgPSlO7VIi2lUDUzl48ixH69oYPTj9so/31f2ZUF0/Jht9nPy4icgkn1GiXHObG4CMFAmCS8lMiSczJY5Dp1plRFIhvkGCIMq12N3odRqSE+UeQTBXDUijxe6WCWuE+AYJgijX3NZBWlIcWnlqNqghecnodRoOnpKbxkKcS4IgivkVhWYZbz9kRr2OIbkpHK1ro6Mz9Bu9QvR3EgRRrKm1A4/XL/cHwjB2SDo+v8IXJ86qXYoQEUOCIIqdtrUDcqM4HGlJceSbE/nixFm8Pr/a5QgRESQIotiphnY0Guk6Gq5xQzPo6PRx+HSb2qUIEREkCKLYSZuD1EQjOp38N4YjOz2BzNR49h1rjtrZ74ToTfIbJIqdbmiXG8U9oNFoKBieid3p4eBJuVcgREhBUFlZSXFxMTNnzmT9+vUXbffoo4+yeaOMw8gAABjPSURBVPPmwHJdXR133XUXs2bN4qGHHsLhcFx+xQKgawpGRycZyXJZqCcGmBPJyTDxWW0jbk/kzFUshBqCBoHVaqW8vJwNGzawZcsWNm7cSG1t7XltlixZwjvvvNNt/erVq1mwYAFVVVWMHz+eF154oXerj2EnrF3j66fLjeIe0Wg0TBptptPjZ3dtk9rlCKGqoEGwbds2CgsLSUtLw2QyUVRURFVVVbc2lZWVfOc732H27NmBdR6Phx07dlBUVATAvHnzzttP9NzR+q4bnZlyaajHMlLiuWpAKgdOtASCVYhYFDQIGhoaMJu/ntDEYrFgtXYfbGvRokV873vf67aupaWFpKQk9PqugbbMZvN5+4meO1pvJzsjAWMvjKQZy64ZmUVCnJ7//839ODo8apcjhCqCDofo9/u7TfqtKEpIk4BfqF1PJg/PzIzu4ZXN5uReP6aiKByz2hk7JIPkpNDPCAwGfVjt+/LYfd0eQqs/GZh9wxC2fHiY31cf4if/8k8hzWKmNDvDqsdkisOcYQq5PfTNe+dKkvqjR9AgyMnJoaamJrBss9mwWCxBD5yRkYHdbsfn86HT6ULe75uamtqjtouf2ZyMzdb7lxwaW12ctbvJz0rE3h76AGoejzes9n157L5uD6HXnxin47Zpw/jjB4d56ref8i/Fo9EH6ZLrdIdXj9PpxuYL/aZ0X713rhSpXz1arSbsD9BBLw1NmTKF7du309zcjMvlorq6mmnTpgU9sMFgYNKkSbz99tsAbNmyJaT9RHBH67veoENyYucTS1+bdnUet317KNs/P8NzGz/D7uxUuyQhrpigQZCdnU1ZWRkLFy7k1ltvZe7cuRQUFLB48WL27NlzyX1XrlzJ66+/TnFxMTU1NfzoRz/qtcJj2dG6NvQ6DXnmRLVL6Tc0Gg0l3xrK4rljOXSqlRX/uZ23th/D3SldS0X/F9KUSSUlJZSUlHRb9/LLL5/X7qmnnuq2nJ+fz+9///vLKE9cyJH6NgZlJwe9fCHCd8P4HAblJLPpg8Ns+vAIb20/zj+NsTB1Qh7D81N6dJ9LiEgnc+dFGZ/fz7EzbXy7IE/tUvqt/KxEls0voPZUKx9+dpq/7bPyl131ZGeYmD4xj5uvzVe7RCF6lQRBlKlvdNLp8TMsN0XtUvq9EQNSGTEglQUzRrLzCxsf7a7j9T/X8m7NSeZOGQKE1oNOiEgnQRBljnz5INnQPAmCKyUhTs/UglymFuTyxYkWXv9zLb9/5wuG56dQOC4bnVYu0YnoJu/gKHPo1FmSEgxkpyeoXUpMGjUonZ8snMSsyYM4fLqN6k9P0SljFYkoJ0EQZQ6ePMtVA1LlkoSKtBoNc6YM4dsTc2lqdfHnf5zG55dJbkT0kiCIIs1tHdjOdjBqYJrapQhgaG4KUybkYG12sW3PGRQlOh98FELuEUSRg6e6xs4fOUiCIFIMy0vF4fLyj0ONpCfHMX5YptolCRE2OSOIIodOthJv1DHQEt3jL/U344dlMDg7ic8ONdLUGt4wGEJEAgmCKHLw5FlGDEiVXioRRqPRUDguhzijno921+P1yf0CEV3kN0qUsDs7Od3oYOQAuSwUieKMOr41IYdWRyf/ONiodjlChEWCIEocOtUKwEi5URyx8rISGTkwjQPHW+QSkYgqEgRR4uDJs+h1WobKE8UR7dqRWcQZdfztcyt+6UUkooQEQZT4/GgzVw1IxaCX/7JIZjTouH60haa2Dg6eOKt2OUKERH6rRIHmtg5ONzqYIF0To8KQ3GRyM03841Ajzg6v2uUIEZQEQRTYe7QZ6OqmKCKfRqNh8thsfH6FmgMNapcjRFASBFFg75Em0pPjyM+SiWiiRUqikQnDMjh2xs5pm0PtcoS4pJCCoLKykuLiYmbOnMn69evP275//37mzZtHUVERP/nJT/B6u06HKyoqmDp1KqWlpZSWllJeXt671ccAn9/P58daGD80Q8YXijLjh2WQYjLwyT4rnV4ZmE5ErqBBYLVaKS8vZ8OGDWzZsoWNGzdSW1vbrc3y5cv56U9/yjvvvIOiKLz++usA7N27lxUrVrB161a2bt1KWVlZ33wX/diRujZcbq/cH4hCOq2WwnE5tLs8VH96Uu1yhLiooEGwbds2CgsLSUtLw2QyUVRURFVVVWD76dOn6ejo4OqrrwZg3rx5ge179uyhoqKCkpISHnnkEVpbW/vo2+i/9hxpRqvRMHZIutqliB7IyTQxLC+F93acpK5RLhGJyBQ0CBoaGjCbzYFli8WC1Wq96Haz2RzYbjabWbp0KW+88Qa5ubmsWbOmN2uPCbtrGxmWn4Ip3qB2KaKHrhtlJs6g4/fvfCHPFoiIFHT0Ub/f3+3atKJ0n57vUtvXrVsXWL9o0SJmzJgRdoGZmdE9wJrZnNzjfets7ZxoaOf+746/4HGUZifJSfEhH89g0IfVvi+P3dftIbz6TaY4zBmmkNuH89onJ8H/uWUkv31rH59+0UjJt4eFtN/lvHcigdQfPYIGQU5ODjU1NYFlm82GxWLptt1mswWWGxsbsVgs2O12Nm3axL333gt0BYROpwu7wKamdvz+6PwUZTYnY7PZe7x/1bZjAIwZkHLB4zjdXuztoQ9l4PGE174vj93X7SG8+p1ONzZf6Dd0w33tJ4228Pfhmbz65ucMyjKRF6QH2OW+d9Qm9atHq9WE/QE66KWhKVOmsH37dpqbm3G5XFRXVzNt2rTA9vz8fOLi4ti5cycAW7duZdq0aZhMJl555RV27doFwGuvvdajM4JYtmO/lREDUslICe+TsIg8Go2Gf5k9mjiDjpcr98kIpSKiBA2C7OxsysrKWLhwIbfeeitz586loKCAxYsXs2fPHgCeffZZfvGLXzBr1iycTicLFy5Ep9Oxdu1aVq1axezZs/n8889Zvnx5n39D/UVdo4NTNgfXj7YEbyyiQmpSHPfOHs1xq50N7x5UuxwhAkKaoaykpISSkpJu615++eXAv0ePHs0f//jH8/abNGkSFRUVl1libNpxoAENMGmUBEF/cu1IM7MLB/Gnv51gYHYyN12Tr3ZJQsiTxZFIURQ+3W/lqoFppCfHqV2O6GW3TxvOhGGZbHj3IJ9/OXyIEGqSIIhAh061Ut/kZMr4HLVLEX1Aq9Xw4HfHkpuZyH9s2s2+YxIGQl0SBBHo/b+fwhSnZ/LYbLVLEX3EFG/gkTuvxpKewH/8cTe7DzepXZKIYSHdIxBXTmu7m51f2PjOdQOIM4Tf3Vb0jEarweEOfcjocHs0X+j4Op2WH8ybwLrNe/jVH3Yx91tDmHH9QDQaDfHOzrCO7/WD2xN6/XEGPTK1hfiKBEGE+XBXHT6/IjcRrzC3x8eug7bgDb80caQ5eKMQjz9tYh7b956h8uNj7PzCxuSxFuZ+ewThDDHo9njZsd8avOGXrh+TjT5OfvxFF3knRBCvz8+Hn9UxfmgG2WE85Sqim0Gv5dsTc7GkJ/D3gzbe+OgYnT6YcV0+KSaj2uWJGCBBEEH+urueFrube2aNVrsUcYVpNBpGD05nUHYSNQdsvPXxUao/Oc4N43K4YVw2Vw1IQ6uVYchF35AgiBBuj483PjrKVQNSmSAzkcUsU7yBaVfncf/gDKq3H+Nv+87wl111pCQaGTkwjZEDUrlqQBoDLUkSDKLXSBBEiPdqTtLq6OShW8fLBDSCvKwk/qV4DAtuGcmuw418dqiRg6fOBqa+TIjTMTQ3hWF5qQzLSyEnUy4lip6TIIgA7S4Pb//tBBOHZzJyYJra5YgIEmfU8U9jsvmnMV1diZtaOzh46iyHTrVy5HQrb20/xlcjW6ckGsnLNJFvTiI30yRnDCJkEgQqUxSF31UdoNPj4/Ybh6tdjohwmanx3JCaww3juh42dHf6OHamjQMnzrLjQAOHTrVy4MRZ4o06BuckMyw3hay0eDnLFJckQaCybXvPUPOFjfk3DmeAObrnXhBXXpxRx6hB6QzITiY1yYjP5+d0o4Oj9XZqT7XyxYmzJCUYuGpAKiMGpJIgXUbFBci7QkUNLU7Wv3uQkQPTmPVPg9QuR/QDOp2WQdnJDMpOptPr46S1ndrTrfzjUCO7ahsZmJ3MqIFpKDJTmjiHBIFKmts6ePZ/PkOn1bBo7hi5nit6nVGvY3h+KsPzU2ltd3PwZCuH61o5fsbOZ7WN3HxNPjeMzyFZnlWIeRIEKmhtd/P//c9ntLs8LL/zGrJSE9QuSfRzqUlxXD/GwjUjszh+xk5do5P/eb+WP3xwmDFD0pk0ysKEYZky2m2MCikIKisrefHFF/F6vdxzzz3cdddd3bbv37+fn/zkJzgcDiZNmsTq1avR6/XU1dWxfPlympqaGDp0KM8++yyJiZeeoq+/23+smZfe3IfL7eX//p+rGZqbonZJIobodVqG56dyxy0jaW7t4G/7zvDpvgZ+e+QAAPlZiQzJTWZITgqDc5IZaEmSMa9iQNAgsFqtlJeXs3nzZoxGI3fccQeTJ09mxIgRgTbLly/nZz/7GVdffTWPPfYYr7/+OgsWLGD16tUsWLCAOXPmsG7dOl544YWYnaWstd3NW9uP8787T5GdYaLsexMZlB07k2OL8Hh9fjr7cBA8gIGWJAZaRjB/+nBO2RzsOdLEFyfOsudwEx/vOQOABkhPiSMrNYH05DiyUuPJ/OpPSjzJiUa0F+iRFO6geaFSFAWP14/L48fZ4UGn06DXatFqNeh0mgvWAmDQ6/F4g7+eiqKgAHp7B35Fuejx+pugQbBt2zYKCwtJS+vq315UVERVVRU//OEPATh9+jQdHR1cffXVAMybN4//+I//4Hvf+x47duxg3bp1gfX//M//HFNB4PX52XesmZoDDXy89wxen59pV+dxx81XEWeUT1ni4tweHzVhDCIX7iB459JoNF+GQhLFhYNRFIUWu5tjZ+ycsNqxnXVhbXGx+3AjLrev275aTdfT0InxehITvv67cEIemYkGEhMMJBj1GA3aC3Zh9fr82J0e7M5O7C4PdkcnbY5OWi/wt9PtxeO99FzPGg1oNRq0Wk3g3xqNBqNeS6fXh6Lw5R+l62+Ubyx3P1ZivIGkBANJJgPJCQbSkuJIS44jPSmO9OSv/50Qp4vqLrpBg6ChoQGz+es3mcViYffu3RfdbjabsVqttLS0kJSUhF6v77a+v1EUhbpGB+0uD+0uD81tbmytLk5a2znR0I7L7cWo1zJ5TDZzpgwmO12eABWRTaPRkJEST0ZKPNd+GTAOd9fopl6fH4fLg93lweHy4HB5cXR4cHR4sTY7cbq9KAr87fPuP+tajSbwy9LnV/D5/F1/X+RURqfVkJpkJDXRSEZKPENykzHFGzDqtRj0WkDDSZsdxQ9+RcHvVwJ/+/xdv9T9ioKiKPgVyEiJp6WtA42m6/vTfPl9di2DBs3X2zQwND8Nr8dLu8vb9bPt7KThrIuDJ8/i6Dj/zCLOoPsyFIxdAfFlYKQmGokz6DDqtRgNOgx6LXFf/v3NDiJJCQb0OnXGBg8aBH6/v1vSKYrSbfli27/ZDuhRYkZ6b5p/HGrkv9871G2dQa8lNzORkm8PZZA5iZED0zD2weDvep0WU7yhT9rrdZo+O/aVaS/1B2sfzs/WucdPSYwj9yLt/H4Fd6eXIfnptLa5cHX6cHd66fD4cLt9XZdbtF2Xc3Q6DXq9hsR4I0nxehIT9CQmGEkxGYg3XvoTtqvTx67axpDrHzM0g/1hTAs6eUIeGp/vgts6vX7sDjetgTMWz9f/dnbSbHdzzGrH5wvvet24oRm9MuBkT35nBg2CnJwcampqAss2mw2LxdJtu8329TjrjY2NWCwWMjIysNvt+Hw+dDrdefuFKj09sm8u35KZxC2FQ1X7+gNyU8NqP2xAeshtB2aHdyM7nGNfifZSf+8K973W1/ryvR/MxYIwWgX9mDplyhS2b99Oc3MzLpeL6upqpk2bFtien59PXFwcO3fuBGDr1q1MmzYNg8HApEmTePvttwHYsmVLt/2EEEJEBo0SwiOGlZWV/Od//icej4f58+ezePFiFi9ezLJly5gwYQIHDhzg8ccfp729nXHjxvGLX/wCo9HI6dOnWbFiBU1NTeTm5vLcc8+RmhpZnyqEECLWhRQEQggh+i+ZvloIIWKcBIEQQsQ4CQIhhIhxEgRCCBHjJAiEECLGSRAIIUSMi+gg2LlzJ/Pnz6e0tJR77rmH06dPq11SSCorKykuLmbmzJmsX79e7XLC9utf/5o5c+YwZ84cnnnmGbXL6ZGnn36aFStWqF1G2N5//33mzZvH7Nmz+dnPfqZ2OWHbunVr4L3z9NNPq11OyNrb25k7dy6nTp0CugbbLCkpYebMmZSXl6tcXXDfrH/jxo3MnTuXkpISfvzjH9PZGWQ0WCWC3XTTTcr+/fsVRVGUP/zhD8qSJUtUrii4M2fOKDfddJPS0tKiOBwOpaSkRDl06JDaZYXs448/Vr7//e8rbrdb6ezsVBYuXKhUV1erXVZYtm3bpkyePFn5t3/7N7VLCcuJEyeUqVOnKvX19UpnZ6dy5513Kh988IHaZYXM6XQq119/vdLU1KR4PB5l/vz5yscff6x2WUF99tlnyty5c5Vx48YpJ0+eVFwulzJ9+nTlxIkTisfjUe67776I/n/4Zv1HjhxRZsyYodjtdsXv9yuPPvqo8uqrr17yGBF7RtDZ2cnDDz/M6NFdgzCNGjWK+vp6lasK7txhu00mU2DY7mhhNptZsWIFRqMRg8HA8OHDqaurU7uskJ09e5by8nKWLFmidilhe/fddykuLiYnJweDwUB5eTkTJ05Uu6yQ+Xw+/H4/LpcLr9eL1+slLi7yZzx7/fXXWblyZWAstN27dzN48GAGDhyIXq+npKQkon+Gv1m/0Whk5cqVJCUlodFoGDlyZNCf4YidqtJoNFJaWgp0jXD661//mltuuUXlqoILNmx3pLvqqqsC/z527Bh/+tOf+O///m8VKwrPT3/6U8rKyqLiQ8M3HT9+HIPBwJIlS6ivr+fGG2/kRz/6kdplhSwpKYmHH36Y2bNnk5CQwPXXX8+1116rdllBPfnkk92WL/QzHMlD6H+z/vz8fPLz8wFobm5m/fr1/OIXv7jkMSLijOBPf/oT06ZN6/bn3nvvBbrODB555BG8Xi8PPviguoWGINiw3dHi0KFD3HfffTz66KMMGTJE7XJC8oc//IHc3FxuuOEGtUvpEZ/Px/bt2/n5z3/Oxo0b2b17NxUVFWqXFbIDBw6wadMm/vznP/PXv/4VrVbLb37zG7XLClt/+Rm2Wq3cc8893H777UyePPmSbSPijGD27NnMnj37vPUOh4OHHnqItLQ0XnzxRQyG0MdbV0uwYbujwc6dO1m2bBmPPfYYc+bMUbuckL399tvYbDZKS0tpbW3F6XTy85//nMcee0zt0kKSlZXFDTfcQEZGBgC33HILu3fvZt68eSpXFpqPPvqIG264gczMTKBrVsINGzawaNEilSsLzzeH1o/Gn+HDhw+zaNEi7r77bu67776g7SPijOBili9fzuDBg1m7di1Go1HtckISbNjuSFdfX88PfvADnn322agKAYBXX32VN998k61bt7Js2TJuvvnmqAkBgJtuuomPPvqItrY2fD4ff/3rXxk3bpzaZYVs9OjRbNu2DafTiaIovP/++0yYMEHtssI2ceJEjh49yvHjx/H5fLz55ptR9TPc3t7O/fffz8MPPxxSCECEnBFcyL59+/jf//1fRowYwW233QZ0Xat7+eWXVa7s0rKzsykrK2PhwoWBYbsLCgrULitkv/nNb3C73Tz11FOBdXfccQd33nmnilXFhokTJ7Jo0SIWLFiAx+PhW9/6FrfffrvaZYVs6tSp7Nu3j3nz5mEwGJgwYQIPPPCA2mWFLS4ujqeeeop//dd/xe12M336dGbNmqV2WSH74x//SGNjI6+++iqvvvoqADfffDMPP/zwRfeRYaiFECLGRfSlISGEEH1PgkAIIWKcBIEQQsQ4CQIhhIhxEgRCCBHjJAiEECLGSRAIEcR9991Hc3Mzixcvpra2Vu1yhOh18hyBEEGMGjWK7du3B4Z+EKK/idgni4WIBD/+8Y8BuOeee6itreX111/H6XTy7LPPkpeXx5EjR4iPj+epp55i+PDhKlcrRM/IpSEhLuGr4Xv/67/+i9zc3MD6vXv3cvfdd1NZWcm8efNYvny5WiUKcdkkCITogdGjRzNp0iQAbr/9dvbv309LS4vKVQnRMxIEQvSATqcLaZ0Q0UCCQIggdDodXq+327oDBw5w4MABoGui8GuuuYaUlBQ1yhPissnNYiGCmDVrFnfffTcOhyOwLisri7Vr13L69GkyMjJ45plnVKxQiMsj3UeFCNMnn3zCE088wZtvvql2KUL0Crk0JIQQMU7OCIQQIsbJGYEQQsQ4CQIhhIhxEgRCCBHjJAiEECLGSRAIIUSMkyAQQogY9/8Ahif5Yh0N/EUAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -891,12 +910,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -911,7 +930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -920,7 +939,7 @@
        "matplotlib.axes._subplots.AxesSubplot"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -931,7 +950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1402,12 +1421,181 @@
     "dir(fig)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.matrix.ClusterGrid at 0x14efbc58d48>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x936 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "sns.set()\n",
+    "\n",
+    "# Load the brain networks example dataset\n",
+    "df = sns.load_dataset(\"brain_networks\", header=[0, 1, 2], index_col=0)\n",
+    "\n",
+    "# Select a subset of the networks\n",
+    "used_networks = [1, 5, 6, 7, 8, 12, 13, 17]\n",
+    "used_columns = (df.columns.get_level_values(\"network\")\n",
+    "                          .astype(int)\n",
+    "                          .isin(used_networks))\n",
+    "df = df.loc[:, used_columns]\n",
+    "\n",
+    "# Create a categorical palette to identify the networks\n",
+    "network_pal = sns.husl_palette(8, s=.45)\n",
+    "network_lut = dict(zip(map(str, used_networks), network_pal))\n",
+    "\n",
+    "# Convert the palette to vectors that will be drawn on the side of the matrix\n",
+    "networks = df.columns.get_level_values(\"network\")\n",
+    "network_colors = pd.Series(networks, index=df.columns).map(network_lut)\n",
+    "\n",
+    "# Draw the full plot\n",
+    "sns.clustermap(df.corr(), center=0, cmap=\"vlag\",\n",
+    "               row_colors=network_colors, col_colors=network_colors,\n",
+    "               linewidths=.75, figsize=(13, 13))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import msca"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = msca.base.raf_sum(1,2)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "from msca.base import raf_sum\n",
+    "print(raf_sum(1,2))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(raf_sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " 'potato',\n",
+       " 'raf_sum']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(msca.base)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No cool\n"
+     ]
+    }
+   ],
+   "source": [
+    "msca.base.potato(a)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "git clone xxx\n",
+    "cd xxx\n",
+    "pip install ."
+   ]
   }
  ],
  "metadata": {
diff --git a/lectures/Lecture 7 - Logistic Regression, Feature Extraction and Transformation.ipynb b/lectures/Lecture 7 - Logistic Regression, Feature Extraction and Transformation.ipynb
index 7ce8cb5..ea34382 100644
--- a/lectures/Lecture 7 - Logistic Regression, Feature Extraction and Transformation.ipynb	
+++ b/lectures/Lecture 7 - Logistic Regression, Feature Extraction and Transformation.ipynb	
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv(\"data/iris.csv\")"
+    "df = pd.read_csv(\"../data/iris.csv\")"
    ]
   },
   {
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -182,49 +182,49 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>125</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1.8</td>\n",
+       "      <td>128</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>2.1</td>\n",
        "      <td>Virginica</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>105</td>\n",
-       "      <td>7.6</td>\n",
+       "      <td>77</td>\n",
+       "      <td>6.7</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>Virginica</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>Versicolor</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>102</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>Virginica</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>66</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.5</td>\n",
-       "      <td>1.5</td>\n",
+       "      <td>58</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>1.3</td>\n",
        "      <td>Versicolor</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>28</td>\n",
-       "      <td>5.2</td>\n",
+       "      <td>136</td>\n",
+       "      <td>6.3</td>\n",
        "      <td>3.4</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>Virginica</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -232,21 +232,21 @@
       ],
       "text/plain": [
        "     sepal.length  sepal.width  petal.length  petal.width     variety  \\\n",
-       "125           7.2          3.2           6.0          1.8   Virginica   \n",
-       "105           7.6          3.0           6.6          2.1   Virginica   \n",
-       "102           7.1          3.0           5.9          2.1   Virginica   \n",
-       "66            5.6          3.0           4.5          1.5  Versicolor   \n",
-       "28            5.2          3.4           1.4          0.2      Setosa   \n",
+       "128           6.4          2.8           5.6          2.1   Virginica   \n",
+       "77            6.7          3.0           5.0          1.7  Versicolor   \n",
+       "2             4.7          3.2           1.3          0.2      Setosa   \n",
+       "58            6.6          2.9           4.6          1.3  Versicolor   \n",
+       "136           6.3          3.4           5.6          2.4   Virginica   \n",
        "\n",
        "     classification  \n",
-       "125               0  \n",
-       "105               0  \n",
-       "102               0  \n",
-       "66                0  \n",
-       "28                1  "
+       "128               0  \n",
+       "77                0  \n",
+       "2                 1  \n",
+       "58                0  \n",
+       "136               0  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,13 +273,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "y = df[\"classification\"]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     sepal.length  sepal.width  petal.length  petal.width\n",
+      "0             5.1          3.5           1.4          0.2\n",
+      "1             4.9          3.0           1.4          0.2\n",
+      "2             4.7          3.2           1.3          0.2\n",
+      "3             4.6          3.1           1.5          0.2\n",
+      "4             5.0          3.6           1.4          0.2\n",
+      "..            ...          ...           ...          ...\n",
+      "145           6.7          3.0           5.2          2.3\n",
+      "146           6.3          2.5           5.0          1.9\n",
+      "147           6.5          3.0           5.2          2.0\n",
+      "148           6.2          3.4           5.4          2.3\n",
+      "149           5.9          3.0           5.1          1.8\n",
+      "\n",
+      "[150 rows x 4 columns]\n",
+      "0      1\n",
+      "1      1\n",
+      "2      1\n",
+      "3      1\n",
+      "4      1\n",
+      "      ..\n",
+      "145    0\n",
+      "146    0\n",
+      "147    0\n",
+      "148    0\n",
+      "149    0\n",
+      "Name: classification, Length: 150, dtype: int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x)\n",
+    "print(y)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -289,11 +332,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
+      "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
+      "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
+      "                   warm_start=False)\n",
+      "<function sum at 0x000002A41897D828>\n"
+     ]
+    }
+   ],
    "source": [
-    "reg = LogisticRegression()"
+    "reg = LogisticRegression()\n",
+    "rafsum = np.sum\n",
+    "\n",
+    "print(reg)\n",
+    "print(rafsum)"
    ]
   },
   {
@@ -305,14 +365,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/conagrabrands/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
       "  FutureWarning)\n"
      ]
     },
@@ -326,7 +386,7 @@
        "                   warm_start=False)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -345,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -369,7 +429,7 @@
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -380,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -395,7 +455,7 @@
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -413,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -422,7 +482,7 @@
        "1.0"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -440,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +509,7 @@
        "array([[ 0.41021713,  1.46416217, -2.26003266, -1.02103509]])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -496,16 +556,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.2478905])"
+       "array([0.26421853])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -524,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -569,23 +629,98 @@
        "      <td>1.4</td>\n",
        "      <td>0.2</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>145</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>146</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>147</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>148</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>149</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>150 rows × 4 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width\n",
-       "0           5.1          3.5           1.4          0.2\n",
-       "1           4.9          3.0           1.4          0.2"
+       "     sepal.length  sepal.width  petal.length  petal.width\n",
+       "0             5.1          3.5           1.4          0.2\n",
+       "1             4.9          3.0           1.4          0.2\n",
+       "2             4.7          3.2           1.3          0.2\n",
+       "3             4.6          3.1           1.5          0.2\n",
+       "4             5.0          3.6           1.4          0.2\n",
+       "..            ...          ...           ...          ...\n",
+       "145           6.7          3.0           5.2          2.3\n",
+       "146           6.3          2.5           5.0          1.9\n",
+       "147           6.5          3.0           5.2          2.0\n",
+       "148           6.2          3.4           5.4          2.3\n",
+       "149           5.9          3.0           5.1          1.8\n",
+       "\n",
+       "[150 rows x 4 columns]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x.head(2)"
+    "x.head(150)"
    ]
   },
   {
@@ -597,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -607,28 +742,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4.079999999999999"
+       "-6.315999999999999"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pred = custom_predict(5.1, 3.5, 1.4, .2)\n",
+    "pred = custom_predict(5.9, 3, 5.1, 1.8)\n",
     "pred"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -637,7 +772,7 @@
        "3.9979999999999998"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -649,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -657,10 +792,10 @@
       "text/plain": [
        "0    1\n",
        "1    1\n",
-       "Name: classification, dtype: int64"
+       "Name: classification, dtype: int32"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -684,7 +819,7 @@
     {
      "data": {
       "text/plain": [
-       "4.112640702773946"
+       "-433.2630461266474"
       ]
      },
      "execution_count": 44,
@@ -693,12 +828,12 @@
     }
    ],
    "source": [
-    "reg.intercept_[0] + np.sum(np.multiply(reg.coef_[0], np.array(x.head(1))))"
+    "reg.intercept_[0] + np.sum(np.multiply(reg.coef_[0], np.array(x.iloc[2:])))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -707,13 +842,14 @@
        "array([[5.1, 3.5, 1.4, 0.2]])"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.array(x.head(1))"
+    "np.array(x.head(1))\n",
+    "here = os.getcwd()"
    ]
   },
   {
@@ -728,22 +864,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4.112640702773946"
+       "-6.287043672009523"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "p = pred(np.array(x.head(1)), reg.coef_[0], reg.intercept_[0])\n",
+    "p = pred(np.array(x.iloc[149,:]), reg.coef_[0], reg.intercept_[0])\n",
     "p"
    ]
   },
@@ -788,7 +924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -867,7 +1003,7 @@
        "4      2.051086     5.270984     -3.164046    -0.204207"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -880,7 +1016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -894,7 +1030,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -905,7 +1041,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -925,7 +1070,7 @@
        "Length: 150, dtype: float64"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -936,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -946,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -955,7 +1100,7 @@
        "0.008149369173106819"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -967,7 +1112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -976,7 +1121,7 @@
        "0.9918506308268932"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1148,7 +1293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -1197,128 +1342,39 @@
        "0           5.1          3.5           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = pd.read_csv(\"data/iris.csv\")\n",
+    "df = pd.read_csv(\"../data/iris.csv\")\n",
     "df.head(1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>count</td>\n",
-       "      <td>150.000000</td>\n",
-       "      <td>150.000000</td>\n",
-       "      <td>150.000000</td>\n",
-       "      <td>150.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>mean</td>\n",
-       "      <td>5.843333</td>\n",
-       "      <td>3.057333</td>\n",
-       "      <td>3.758000</td>\n",
-       "      <td>1.199333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>std</td>\n",
-       "      <td>0.828066</td>\n",
-       "      <td>0.435866</td>\n",
-       "      <td>1.765298</td>\n",
-       "      <td>0.762238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>min</td>\n",
-       "      <td>4.300000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.100000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>25%</td>\n",
-       "      <td>5.100000</td>\n",
-       "      <td>2.800000</td>\n",
-       "      <td>1.600000</td>\n",
-       "      <td>0.300000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>50%</td>\n",
-       "      <td>5.800000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.350000</td>\n",
-       "      <td>1.300000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>75%</td>\n",
-       "      <td>6.400000</td>\n",
-       "      <td>3.300000</td>\n",
-       "      <td>5.100000</td>\n",
-       "      <td>1.800000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>max</td>\n",
-       "      <td>7.900000</td>\n",
-       "      <td>4.400000</td>\n",
-       "      <td>6.900000</td>\n",
-       "      <td>2.500000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "       sepal.length  sepal.width  petal.length  petal.width\n",
-       "count    150.000000   150.000000    150.000000   150.000000\n",
-       "mean       5.843333     3.057333      3.758000     1.199333\n",
-       "std        0.828066     0.435866      1.765298     0.762238\n",
-       "min        4.300000     2.000000      1.000000     0.100000\n",
-       "25%        5.100000     2.800000      1.600000     0.300000\n",
-       "50%        5.800000     3.000000      4.350000     1.300000\n",
-       "75%        6.400000     3.300000      5.100000     1.800000\n",
-       "max        7.900000     4.400000      6.900000     2.500000"
+       "sepal.length    6.3\n",
+       "sepal.width     3.3\n",
+       "petal.length    4.7\n",
+       "petal.width     1.4\n",
+       "dtype: float64"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.describe()"
+    "a = df.groupby('variety').first()\n",
+    "a.median()"
    ]
   },
   {
@@ -1331,7 +1387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1428,7 +1484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1531,7 +1587,7 @@
        "max    2.492019e+00  3.090775e+00  1.785832e+00  1.712096e+00"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1540,12 +1596,13 @@
     "# note the transform returns a numpy array\n",
     "x_scaler = scaler.transform(x)\n",
     "x_scale_df = pd.DataFrame(x_scaler, columns = x.columns)\n",
-    "x_scale_df.describe()"
+    "x_scale_df.describe()\n",
+    "path = '.'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1675,7 +1732,7 @@
        "[150 rows x 4 columns]"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1694,7 +1751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1703,7 +1760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -1806,7 +1863,7 @@
        "max        1.000000     1.000000      1.000000     1.000000"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1837,7 +1894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1846,7 +1903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1859,7 +1916,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1880,7 +1937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1936,6 +1993,27 @@
    "cell_type": "code",
    "execution_count": 101,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<4x9 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "\twith 21 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1943,10 +2021,10 @@
        "array([[0, 1, 1, 1, 0, 0, 1, 0, 1],\n",
        "       [0, 2, 0, 1, 0, 1, 1, 0, 1],\n",
        "       [1, 0, 0, 1, 1, 0, 1, 1, 1],\n",
-       "       [0, 1, 1, 1, 0, 0, 1, 0, 1]])"
+       "       [0, 1, 1, 1, 0, 0, 1, 0, 1]], dtype=int64)"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1957,7 +2035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1966,7 +2044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
@@ -2062,7 +2140,7 @@
        "3    0         1      1   1    0       0    1      0     1"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2073,7 +2151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2082,7 +2160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -2184,7 +2262,7 @@
        "3  0.000000  0.384085  "
       ]
      },
-     "execution_count": 105,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2206,7 +2284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2215,7 +2293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
@@ -2278,7 +2356,7 @@
        "2  0  1  1  3"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2291,7 +2369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -2394,7 +2472,7 @@
        "max    0.0  2.000000  4.000000  3.0"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2405,7 +2483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -2413,10 +2491,10 @@
       "text/plain": [
        "array([[2, 0],\n",
        "       [1, 4],\n",
-       "       [1, 1]])"
+       "       [1, 1]], dtype=int64)"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2429,7 +2507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -2438,7 +2516,7 @@
        "array([0.        , 0.22222222, 2.88888889, 0.        ])"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2449,7 +2527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -2458,7 +2536,7 @@
        "Int64Index([1, 2], dtype='int64')"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/lectures/Lecture 8 - Decision Tree, Regression, Model Evaluation.ipynb b/lectures/Lecture 8 - Decision Tree, Regression, Model Evaluation.ipynb
index 827351f..f94db49 100644
--- a/lectures/Lecture 8 - Decision Tree, Regression, Model Evaluation.ipynb	
+++ b/lectures/Lecture 8 - Decision Tree, Regression, Model Evaluation.ipynb	
@@ -15,46 +15,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.preprocessing import StandardScaler"
+    "import copy\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = df.drop(\"variety\", 1)"
+    "from sklearn.datasets import load_iris\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "iris = load_iris()\n",
+    "data = iris[\"data\"]\n",
+    "labels = iris[\"target_names\"]\n",
+    "feature_columns = iris[\"feature_names\"]\n",
+    "\n",
+    "df = pd.DataFrame(data, columns = feature_columns)\n",
+    "df[\"label\"] = np.array([labels[x] for x in iris[\"target\"]])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "StandardScaler(copy=True, with_mean=True, with_std=True)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scaler = StandardScaler()\n",
-    "scaler.fit(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [
     {
@@ -78,577 +67,3275 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>count</td>\n",
-       "      <td>1.500000e+02</td>\n",
-       "      <td>1.500000e+02</td>\n",
-       "      <td>1.500000e+02</td>\n",
-       "      <td>1.500000e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>mean</td>\n",
-       "      <td>-2.775558e-16</td>\n",
-       "      <td>-9.695948e-16</td>\n",
-       "      <td>-8.652338e-16</td>\n",
-       "      <td>-4.662937e-16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>std</td>\n",
-       "      <td>1.003350e+00</td>\n",
-       "      <td>1.003350e+00</td>\n",
-       "      <td>1.003350e+00</td>\n",
-       "      <td>1.003350e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>min</td>\n",
-       "      <td>-1.870024e+00</td>\n",
-       "      <td>-2.433947e+00</td>\n",
-       "      <td>-1.567576e+00</td>\n",
-       "      <td>-1.447076e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>25%</td>\n",
-       "      <td>-9.006812e-01</td>\n",
-       "      <td>-5.923730e-01</td>\n",
-       "      <td>-1.226552e+00</td>\n",
-       "      <td>-1.183812e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>50%</td>\n",
-       "      <td>-5.250608e-02</td>\n",
-       "      <td>-1.319795e-01</td>\n",
-       "      <td>3.364776e-01</td>\n",
-       "      <td>1.325097e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>75%</td>\n",
-       "      <td>6.745011e-01</td>\n",
-       "      <td>5.586108e-01</td>\n",
-       "      <td>7.627583e-01</td>\n",
-       "      <td>7.906707e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>max</td>\n",
-       "      <td>2.492019e+00</td>\n",
-       "      <td>3.090775e+00</td>\n",
-       "      <td>1.785832e+00</td>\n",
-       "      <td>1.712096e+00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       sepal.length   sepal.width  petal.length   petal.width\n",
-       "count  1.500000e+02  1.500000e+02  1.500000e+02  1.500000e+02\n",
-       "mean  -2.775558e-16 -9.695948e-16 -8.652338e-16 -4.662937e-16\n",
-       "std    1.003350e+00  1.003350e+00  1.003350e+00  1.003350e+00\n",
-       "min   -1.870024e+00 -2.433947e+00 -1.567576e+00 -1.447076e+00\n",
-       "25%   -9.006812e-01 -5.923730e-01 -1.226552e+00 -1.183812e+00\n",
-       "50%   -5.250608e-02 -1.319795e-01  3.364776e-01  1.325097e-01\n",
-       "75%    6.745011e-01  5.586108e-01  7.627583e-01  7.906707e-01\n",
-       "max    2.492019e+00  3.090775e+00  1.785832e+00  1.712096e+00"
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "\n",
+       "    label  \n",
+       "0  setosa  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x_scaler = scaler.transform(x)\n",
-    "x_scale_df = pd.DataFrame(x_scaler, columns = x.columns)\n",
-    "x_scale_df.describe()"
+    "df.head(1)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 158,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "#### Simple Decision Tree\n",
-    "https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html"
+    "x = df.drop(\"label\", 1)\n",
+    "y = df[\"label\"]\n",
+    "\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "import pandas as pd"
+    "### Clustering\n",
+    "* https://scikit-learn.org/stable/modules/clustering.html\n",
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 121,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1          3.5           1.4          0.2  Setosa"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df = pd.read_csv(\"data/iris.csv\")\n",
-    "df.head(1)"
+    "from sklearn.cluster import KMeans"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
-    "y = df[\"variety\"]\n",
-    "x = df.drop(\"variety\", axis = 1)"
+    "kmeans = KMeans(3).fit(x)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width\n",
-       "0           5.1          3.5           1.4          0.2"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2\n",
+      " 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 2 2 2 2 0 2 2 2 0 2 2 2 0 2\n",
+      " 2 0]\n",
+      "0         setosa\n",
+      "1         setosa\n",
+      "2         setosa\n",
+      "3         setosa\n",
+      "4         setosa\n",
+      "         ...    \n",
+      "145    virginica\n",
+      "146    virginica\n",
+      "147    virginica\n",
+      "148    virginica\n",
+      "149    virginica\n",
+      "Name: label, Length: 150, dtype: object\n"
+     ]
     }
    ],
    "source": [
-    "x.head(1)"
+    "result = kmeans.labels_\n",
+    "print(result)\n",
+    "print(y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0    Setosa\n",
-       "Name: variety, dtype: object"
+       "array([[5.9016129 , 2.7483871 , 4.39354839, 1.43387097],\n",
+       "       [5.006     , 3.428     , 1.462     , 0.246     ],\n",
+       "       [6.85      , 3.07368421, 5.74210526, 2.07105263]])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "y.head(1)"
+    "kmeans.cluster_centers_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 125,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
-       "                       max_features=None, max_leaf_nodes=None,\n",
-       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
-       "                       min_samples_leaf=1, min_samples_split=2,\n",
-       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
-       "                       random_state=None, splitter='best')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "clf = DecisionTreeClassifier()\n",
-    "clf.fit(x,y)"
+    "from sklearn.metrics import silhouette_score, f1_score, precision_score, accuracy_score"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array(['Setosa'], dtype=object)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: 'setosa'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-126-494b6b06e49c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msilhouette_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkmeans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\cluster\\unsupervised.py\u001b[0m in \u001b[0;36msilhouette_score\u001b[1;34m(X, labels, metric, sample_size, random_state, **kwds)\u001b[0m\n\u001b[0;32m    115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 117\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msilhouette_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\cluster\\unsupervised.py\u001b[0m in \u001b[0;36msilhouette_samples\u001b[1;34m(X, labels, metric, **kwds)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m     \"\"\"\n\u001b[1;32m--> 212\u001b[1;33m     \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'csr'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m     \u001b[0mle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLabelEncoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    717\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 719\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    720\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    494\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m                 \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    497\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m     \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m    946\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    947\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"M8[ns]\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 948\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    949\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    950\u001b[0m     \u001b[1;31m# ----------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m     \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\numpy_.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[0m_HANDLED_TYPES\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m     \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'setosa'"
+     ]
     }
    ],
    "source": [
-    "clf.predict([[5.1,3.5,1.4,.2]])"
+    "score = silhouette_score(y,kmeans.labels_)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 54,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array(['Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa',\n",
-       "       'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa',\n",
-       "       'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa',\n",
-       "       'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa',\n",
-       "       'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa',\n",
-       "       'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa',\n",
-       "       'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa',\n",
-       "       'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Setosa',\n",
-       "       'Setosa', 'Setosa', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Versicolor',\n",
-       "       'Versicolor', 'Versicolor', 'Versicolor', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Virginica'], dtype=object)"
+       "0.5528190123564102"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "clf.predict(x)"
+    "score"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# What is some stuff we could start doing to make this model better"
+    "ks = [2,3,4,5,6,7,8,9,10]\n",
+    "scores = []\n",
+    "for k in ks:\n",
+    "    kmeans = KMeans(k).fit(x)\n",
+    "    scores.append(silhouette_score(x,kmeans.labels_))"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 56,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 0.6810461692117465)\n",
+      "(3, 0.5528190123564102)\n",
+      "(4, 0.49805050499728803)\n",
+      "(5, 0.48874888709310654)\n",
+      "(6, 0.36598336415829236)\n",
+      "(7, 0.34521786463700094)\n",
+      "(8, 0.350060295968675)\n",
+      "(9, 0.32233069884845983)\n",
+      "(10, 0.30973766533913044)\n"
+     ]
+    }
+   ],
    "source": [
-    "### Train and Test Split"
+    "for i in zip(ks, scores):\n",
+    "    print(i)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.model_selection import train_test_split"
+    "kmeans = KMeans(3).fit(x)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+    "assign_dct = dict(x for x in zip(x.index, kmeans.labels_))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 59,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
-       "                       max_features=None, max_leaf_nodes=None,\n",
-       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
-       "                       min_samples_leaf=1, min_samples_split=2,\n",
-       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
-       "                       random_state=None, splitter='best')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "clf = DecisionTreeClassifier()\n",
-    "clf.fit(x_train,y_train)"
+    "assign_df = pd.DataFrame({\n",
+    "    \"observation\": x.index,\n",
+    "    \"label\": kmeans.labels_\n",
+    "})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "array(['Versicolor', 'Setosa', 'Virginica', 'Versicolor', 'Versicolor',\n",
-       "       'Setosa', 'Versicolor', 'Virginica', 'Versicolor', 'Versicolor',\n",
-       "       'Virginica', 'Setosa', 'Setosa', 'Setosa', 'Setosa', 'Versicolor',\n",
-       "       'Virginica', 'Versicolor', 'Versicolor', 'Virginica', 'Setosa',\n",
-       "       'Virginica', 'Setosa', 'Virginica', 'Virginica', 'Virginica',\n",
-       "       'Virginica', 'Virginica', 'Setosa', 'Setosa'], dtype=object)"
+       "   observation  label\n",
+       "0            0      0\n",
+       "1            1      0\n",
+       "2            2      0\n",
+       "3            3      0\n",
+       "4            4      0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "clf.predict(x_test)"
+    "assign_df.head(5)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Simple Validation"
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, classification_report"
+    "from sklearn.cluster import AgglomerativeClustering"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
-    "yhat_test = clf.predict(x_test)"
+    "agglom = AgglomerativeClustering(n_clusters = 3).fit(x)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
+       "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
+       "       2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int64)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "accuracy_score(yhat_test, y_test)"
+    "agglom.labels_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 127,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "precision_score(yhat_test, y_test, average = \"weighted\")"
+    "assign_df[\"agglom_labels\"] = agglom.labels_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assign_df[\"diff\"] = [True if i[0] == i[1] else False for i in \n",
+    "                     zip(assign_df[\"label\"].tolist(), assign_df[\"agglom_labels\"].tolist())]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assign_df[\"diff1\"] = np.where(assign_df[\"label\"] == assign_df[\"agglom_labels\"], True, False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "      <th>label</th>\n",
+       "      <th>agglom_labels</th>\n",
+       "      <th>diff</th>\n",
+       "      <th>diff1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>145</td>\n",
+       "      <td>145</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>146</td>\n",
+       "      <td>146</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>147</td>\n",
+       "      <td>147</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>148</td>\n",
+       "      <td>148</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>149</td>\n",
+       "      <td>149</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "1.0"
+       "     observation  label  agglom_labels   diff  diff1\n",
+       "0              0      0              1  False  False\n",
+       "1              1      0              1  False  False\n",
+       "2              2      0              1  False  False\n",
+       "3              3      0              1  False  False\n",
+       "4              4      0              1  False  False\n",
+       "..           ...    ...            ...    ...    ...\n",
+       "145          145      2              2   True   True\n",
+       "146          146      1              0  False  False\n",
+       "147          147      2              2   True   True\n",
+       "148          148      2              2   True   True\n",
+       "149          149      1              0  False  False\n",
+       "\n",
+       "[150 rows x 5 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "recall_score(yhat_test, y_test, average = \"weighted\")"
+    "assign_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus = [\n",
+    "    'This is the first document.',\n",
+    "    'This document is the second document.',\n",
+    "    'And this is the third one.',\n",
+    "    'Is this the first document?',\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorizer = CountVectorizer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = vectorizer.fit_transform(corpus)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(x.toarray(), columns = vectorizer.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>and</th>\n",
+       "      <th>document</th>\n",
+       "      <th>first</th>\n",
+       "      <th>is</th>\n",
+       "      <th>one</th>\n",
+       "      <th>second</th>\n",
+       "      <th>the</th>\n",
+       "      <th>third</th>\n",
+       "      <th>this</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "array([[10,  0,  0],\n",
-       "       [ 0,  9,  0],\n",
-       "       [ 0,  0, 11]])"
+       "   and  document  first  is  one  second  the  third  this\n",
+       "0    0         1      1   1    0       0    1      0     1\n",
+       "1    0         2      0   1    0       1    1      0     1\n",
+       "2    1         0      0   1    1       0    1      1     1\n",
+       "3    0         1      1   1    0       0    1      0     1"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "con_matr = confusion_matrix(yhat_test, y_test, labels=[\"Setosa\", \"Versicolor\", \"Virginica\"])\n",
-    "con_matr"
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(2).fit(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      Setosa       1.00      1.00      1.00        10\n",
-      "  Versicolor       1.00      1.00      1.00         9\n",
-      "   Virginica       1.00      1.00      1.00        11\n",
-      "\n",
-      "    accuracy                           1.00        30\n",
-      "   macro avg       1.00      1.00      1.00        30\n",
-      "weighted avg       1.00      1.00      1.00        30\n",
-      "\n"
+      "('This is the first document.', 1)\n",
+      "('This document is the second document.', 1)\n",
+      "('And this is the third one.', 0)\n",
+      "('Is this the first document?', 1)\n"
      ]
     }
    ],
    "source": [
-    "print(classification_report(yhat_test, y_test))"
+    "for i in zip(corpus, kmeans.labels_):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* consider grouping articles\n",
+    "* corpus is a bunch of documents of articles\n",
+    "* we could cluster them based on the words in them\n",
+    "* and classify new articles as they come in"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN\n",
+    "* density based clustering\n",
+    "* dbscan labels noise (items that don't fit into clusters well) as -1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import DBSCAN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "iris = load_iris()\n",
+    "data = iris[\"data\"]\n",
+    "labels = iris[\"target_names\"]\n",
+    "feature_columns = iris[\"feature_names\"]\n",
+    "\n",
+    "df = pd.DataFrame(data, columns = feature_columns)\n",
+    "\n",
+    "scan = DBSCAN().fit(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "        0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  1,\n",
+       "        1,  1,  1,  1,  1,  1, -1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,\n",
+       "       -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
+       "        1,  1, -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,\n",
+       "        1,  1,  1, -1, -1,  1, -1, -1,  1,  1,  1,  1,  1,  1,  1, -1, -1,\n",
+       "        1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1, -1, -1,\n",
+       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1],\n",
+       "      dtype=int64)"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scan.labels_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* in this case, with iris, we have actual labels, ground truth\n",
+    "* say had a sample of data with labels\n",
+    "* we could cluster the items we have labels for and feed that into a classificaiton model\n",
+    "* if that classification model performs well, we could feel more comfortable applying that to unlabeled data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(3).fit(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = kmeans.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Number of labels=150 does not match number of samples=4",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-148-77853a8289f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    814\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    815\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 816\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m    817\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    248\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m             raise ValueError(\"Number of labels=%d does not match \"\n\u001b[1;32m--> 250\u001b[1;33m                              \"number of samples=%d\" % (len(y), n_samples))\n\u001b[0m\u001b[0;32m    251\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin_weight_fraction_leaf\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"min_weight_fraction_leaf must in [0, 0.5]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Number of labels=150 does not match number of samples=4"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'dt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-149-0db4274f6938>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0myhat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'dt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "yhat = dt.predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if our decision tree is good we could save it here and apply it to new, unlabeled data\n",
+    "# many times you will run into cases where you have portions of data that's been labeled\n",
+    "# but you'd like to label the entire sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "1                4.9               3.0                1.4               0.2   \n",
+       "2                4.7               3.2                1.3               0.2   \n",
+       "3                4.6               3.1                1.5               0.2   \n",
+       "4                5.0               3.6                1.4               0.2   \n",
+       "\n",
+       "    label  \n",
+       "0  setosa  \n",
+       "1  setosa  \n",
+       "2  setosa  \n",
+       "3  setosa  \n",
+       "4  setosa  "
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_sample = copy.copy(x)\n",
+    "x_sample[\"label\"] = \"?\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "concat_df = pd.concat([df, x_sample])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "1                4.9               3.0                1.4               0.2   \n",
+       "2                4.7               3.2                1.3               0.2   \n",
+       "\n",
+       "    label  \n",
+       "0  setosa  \n",
+       "1  setosa  \n",
+       "2  setosa  "
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "concat_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>?</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "147                6.5               3.0                5.2               2.0   \n",
+       "148                6.2               3.4                5.4               2.3   \n",
+       "149                5.9               3.0                5.1               1.8   \n",
+       "\n",
+       "    label  \n",
+       "147     ?  \n",
+       "148     ?  \n",
+       "149     ?  "
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "concat_df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = DecisionTreeClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
+       "                       max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yhat = dt.predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict wants a list of lists\n",
+    "# or a 2d numpy array\n",
+    "# so you have to wrap single observations\n",
+    "#dt.predict([1,2,3,4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt.predict([[1,2,3,4]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* https://scikit-learn.org/stable/modules/model_persistence.html\n",
+    "* in the specific case of scikit-learn, it may be better to use joblib’s replacement of pickle (dump & load), which is more efficient on objects that carry large numpy arrays internally as is often the case for fitted scikit-learn estimators\n",
+    "* pickle is standard library in C while joblib is pure python, so it we aren't saving items with large numpy arrays behind them, pickle can be much faster (dicts, str objects, lists)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "from joblib import dump, load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('dt.pkl', 'wb') as f:\n",
+    "    pickle.dump(dt, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['dt.joblib']"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dump(dt,'dt.joblib') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
+       "                       max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'dt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-109-36673dcb6ee0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'dt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt = load('dt.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
+       "                       max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt.predict([[1,2,3,4]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('dt.pkl', 'rb') as f:\n",
+    "    dt = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
+       "                       max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt.predict([[1,2,3,4]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'det' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-117-232750731761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mdet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'det' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "del det"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score(vect):\n",
+    "    dt = load('dt.joblib')\n",
+    "    return dt.predict(vect)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
+       "                       max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del dt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'numpy.float64' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-154-5c6f42131fa8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: 'numpy.float64' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "pred = score([[1,2,3,4]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'pred' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-155-c6fc79dbd350>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'pred' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* persisting models is very important in practice\n",
+    "* the whole idea of training models is using them to make predictions later on\n",
+    "* or in the case of something like neural networks, load them and incrementally continue training\n",
+    "* we are training with toy datasets, but in industry models may take a very long time to train\n",
+    "* and have complicated data transformations and feature engineering prior to actual training\n",
+    "* be sure to think about how you will organize code going forward so it's reusable and easy to run again if neeeded, or train on new data easily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(x,y,path):\n",
+    "    dt = DecisionTreeClassifier().fit(x,y)\n",
+    "    dump(dt,path)\n",
+    "    \n",
+    "def score(vect,path):\n",
+    "    mod = load(path)\n",
+    "    return mod.predict(vect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### what if we wanted to add in PCA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* we can save our transformations as well, reload them and reapply them\n",
+    "* remember whatever we do to our training data we must do to new data to score of predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def decompose(x, path = \"pca.joblib\"):\n",
+    "    pca = PCA(n_components=2).fit(x)\n",
+    "    dump(pca, path) \n",
+    "    return pca.transform(x)\n",
+    "\n",
+    "def train(x,y,path = \"model.joblib\"):\n",
+    "    dt = DecisionTreeClassifier().fit(x,y)\n",
+    "    dump(dt,path)\n",
+    "    return dt\n",
+    "    \n",
+    "def score(vect, pca_path = \"pca.joblib\", mod_path = \"model.joblib\"):\n",
+    "    pca = load(pca_path)\n",
+    "    mod = load(mod_path)\n",
+    "    return mod.predict(pca.transform(vect))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca_x = decompose(x)\n",
+    "model = train(pca_x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = score([[1,2,3,4]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['versicolor'], dtype=object)"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What if we have 100M datapoints to score\n",
+    "* we definatly could do them all at once by feeding in one large matrix\n",
+    "* but maybe that won't fit in memory\n",
+    "* we could make a generator that feeds in one row at a time and runs the score function\n",
+    "* maybe multiprocessing would be faster? depends on the problem\n",
+    "* if multiprocessing, remember the score function loads 2 models, that is a lot of IO.  You'd likly be better off packing the data and models into tuples to save IO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#def score(item):\n",
+    "#    pca = item[1]\n",
+    "#    mod = item[2]\n",
+    "#    return mod.predict(pca.transform(item[0]))\n",
+    "\n",
+    "#data = [\n",
+    "#    [1,2,3,4], mod_object_1, mod_object2],\n",
+    "#    [1,2,3,4], mod_object_1, mod_object2],\n",
+    "#    [1,2,3,4], mod_object_1, mod_object2]\n",
+    "#]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "    [1,2,3,4],\n",
+    "    [1,2,3,4],\n",
+    "    [1,2,3,4],\n",
+    "    [1,2,3,4],\n",
+    "    [1,2,3,4],\n",
+    "    [1,2,3,4],\n",
+    "    [1,2,3,4]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we could mape the score function above to the data\n",
+    "# pool.map(score, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* the best approach will depend on the problem, but there is a lot of options to efficiently score and predict new data?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* once a model is saved what can we do with it?\n",
+    "* embed in a dashboard\n",
+    "* deploy as an API that can be pinged to get results\n",
+    "* share it with co workers...if you're nice of course :)\n",
+    "* use it to batch score overnight (lots of data points, think song recommendations for every spotify user)\n",
+    "* flow could be something like\n",
+    "    * parse your data across multiple executors/servers with your score function and model objects\n",
+    "    * make predictions and send them to a database or storeage somewhere\n",
+    "    * backend engineers make some API that pings with data, loads your model, spits back predictions\n",
+    "* something abstract is happening like this with most seaech querys performed\n",
+    "* the strings get sent to an API\n",
+    "* the api takes the string and runs some model/grabs results and sends the results back to be displayed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### KNN Classifier\n",
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\n",
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5).fit(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yhat = knn.predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.classes_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9666666666666667\n",
+      "0.9667867146858743\n",
+      "0.9666666666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "acc = accuracy_score(y,yhat)\n",
+    "prec = precision_score(y, yhat, labels = knn.classes_, average=\"weighted\")\n",
+    "rec = recall_score(y, yhat, labels = knn.classes_, average=\"weighted\")\n",
+    "print(acc)\n",
+    "print(prec)\n",
+    "print(rec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K:1\n",
+      "Accuracy:1.0\n",
+      "Precision:1.0\n",
+      "Recall:1.0\n",
+      "\n",
+      "\n",
+      "K:2\n",
+      "Accuracy:0.98\n",
+      "Precision:0.9811320754716981\n",
+      "Recall:0.98\n",
+      "\n",
+      "\n",
+      "K:3\n",
+      "Accuracy:0.96\n",
+      "Precision:0.96\n",
+      "Recall:0.96\n",
+      "\n",
+      "\n",
+      "K:4\n",
+      "Accuracy:0.96\n",
+      "Precision:0.96\n",
+      "Recall:0.96\n",
+      "\n",
+      "\n",
+      "K:5\n",
+      "Accuracy:0.9666666666666667\n",
+      "Precision:0.9667867146858743\n",
+      "Recall:0.9666666666666667\n",
+      "\n",
+      "\n",
+      "K:6\n",
+      "Accuracy:0.9733333333333334\n",
+      "Precision:0.9733333333333334\n",
+      "Recall:0.9733333333333334\n",
+      "\n",
+      "\n",
+      "K:7\n",
+      "Accuracy:0.9733333333333334\n",
+      "Precision:0.9738247863247864\n",
+      "Recall:0.9733333333333334\n",
+      "\n",
+      "\n",
+      "K:8\n",
+      "Accuracy:0.98\n",
+      "Precision:0.980125383486728\n",
+      "Recall:0.98\n",
+      "\n",
+      "\n",
+      "K:9\n",
+      "Accuracy:0.98\n",
+      "Precision:0.980125383486728\n",
+      "Recall:0.98\n",
+      "\n",
+      "\n",
+      "K:10\n",
+      "Accuracy:0.98\n",
+      "Precision:0.980125383486728\n",
+      "Recall:0.98\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "precs = []\n",
+    "recs = []\n",
+    "accs = []\n",
+    "for k in [1,2,3,4,5,6,7,8,9,10]:\n",
+    "    knn = KNeighborsClassifier(n_neighbors=k).fit(x,y)\n",
+    "    yhat = knn.predict(x)\n",
+    "    acc = accuracy_score(y,yhat)\n",
+    "    prec = precision_score(y, yhat, labels = knn.classes_, average=\"weighted\")\n",
+    "    rec = recall_score(y, yhat, labels = knn.classes_, average=\"weighted\")\n",
+    "    precs.append(prec)\n",
+    "    recs.append(rec)\n",
+    "    accs.append(acc)\n",
+    "    print(\"K:{}\".format(k))\n",
+    "    print(\"Accuracy:{}\".format(acc))\n",
+    "    print(\"Precision:{}\".format(prec))\n",
+    "    print(\"Recall:{}\".format(rec))\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### SVM Classifier\n",
+    "* https://scikit-learn.org/stable/modules/svm.html/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernels = [\"linear\", \"poly\", \"rbf\", \"sigmoid\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kernel:linear\n",
+      "Accuracy:0.9933333333333333\n",
+      "Precision:0.9934640522875816\n",
+      "Recall:0.9933333333333333\n",
+      "\n",
+      "\n",
+      "Kernel:poly\n",
+      "Accuracy:0.98\n",
+      "Precision:0.980125383486728\n",
+      "Recall:0.98\n",
+      "\n",
+      "\n",
+      "Kernel:rbf\n",
+      "Accuracy:0.9866666666666667\n",
+      "Precision:0.9871794871794873\n",
+      "Recall:0.9866666666666667\n",
+      "\n",
+      "\n",
+      "Kernel:sigmoid\n",
+      "Accuracy:0.04\n",
+      "Precision:0.023604465709728868\n",
+      "Recall:0.04\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k in kernels:\n",
+    "    svm = SVC(kernel = k).fit(x,y)\n",
+    "    yhat = svm.predict(x)\n",
+    "    acc = accuracy_score(y,yhat)\n",
+    "    prec = precision_score(y, yhat, labels = knn.classes_, average=\"weighted\")\n",
+    "    rec = recall_score(y, yhat, labels = knn.classes_, average=\"weighted\")\n",
+    "    precs.append(prec)\n",
+    "    recs.append(rec)\n",
+    "    accs.append(acc)\n",
+    "    print(\"Kernel:{}\".format(k))\n",
+    "    print(\"Accuracy:{}\".format(acc))\n",
+    "    print(\"Precision:{}\".format(prec))\n",
+    "    print(\"Recall:{}\".format(rec))\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1 = x.drop(\"sepal length (cm)\", 1)\n",
+    "y1 = x[\"sepal length (cm)\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr = LinearRegression()\n",
+    "dt = DecisionTreeRegressor()\n",
+    "knn = KNeighborsRegressor()\n",
+    "svr = SVR()\n",
+    "\n",
+    "models = [lr, dt, knn, svr]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model:LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
+      "MSE:0.09630269942460723\n",
+      "\n",
+      "\n",
+      "Model:DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
+      "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
+      "                      min_impurity_split=None, min_samples_leaf=1,\n",
+      "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+      "                      presort=False, random_state=None, splitter='best')\n",
+      "MSE:0.0009333333333333338\n",
+      "\n",
+      "\n",
+      "Model:KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+      "                    metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
+      "                    weights='uniform')\n",
+      "MSE:0.08234400000000003\n",
+      "\n",
+      "\n",
+      "Model:SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
+      "    gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
+      "    tol=0.001, verbose=False)\n",
+      "MSE:0.0893454354409234\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for mod in models:\n",
+    "    mod.fit(x1,y1)\n",
+    "    yhat = mod.predict(x1)\n",
+    "    mse = mean_squared_error(y1,yhat)\n",
+    "    print(\"Model:{}\".format(mod))\n",
+    "    print(\"MSE:{}\".format(mse))\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What is some stuff we could start doing to make this model better"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train and Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
+       "                       max_features=None, max_leaf_nodes=None,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = DecisionTreeClassifier()\n",
+    "clf.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['versicolor', 'setosa', 'virginica', 'versicolor', 'versicolor',\n",
+       "       'setosa', 'versicolor', 'virginica', 'versicolor', 'versicolor',\n",
+       "       'virginica', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor',\n",
+       "       'virginica', 'versicolor', 'versicolor', 'virginica', 'setosa',\n",
+       "       'virginica', 'setosa', 'virginica', 'virginica', 'virginica',\n",
+       "       'virginica', 'virginica', 'setosa', 'setosa'], dtype=object)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yhat_test = clf.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(yhat_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(yhat_test, y_test, average = \"weighted\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(yhat_test, y_test, average = \"weighted\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10,  0,  0],\n",
+       "       [ 0,  9,  0],\n",
+       "       [ 0,  0, 11]])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "con_matr = confusion_matrix(yhat_test, y_test)\n",
+    "con_matr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>setosa</th>\n",
+       "      <th>versicolor</th>\n",
+       "      <th>virginica</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>setosa</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>versicolor</th>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>virginica</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            setosa  versicolor  virginica\n",
+       "setosa          10           0          0\n",
+       "versicolor       0           9          0\n",
+       "virginica        0           0         11"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(con_matr, index = clf.classes_, columns = clf.classes_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        10\n",
+      "  versicolor       1.00      1.00      1.00         9\n",
+      "   virginica       1.00      1.00      1.00        11\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(yhat_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Collaborative Filtering\n",
+    "* java library for rec systems, super comprehensive\n",
+    "* https://www.librec.net/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### User Based"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# user item rating will be common\n",
+    "data = [\n",
+    "    [\"A\", \"Avengers\", 5],\n",
+    "    [\"A\", \"Star Wars\", 5],\n",
+    "    [\"A\", \"Mean Girls\", 1],\n",
+    "    [\"A\", \"Mean Girls 2\", 1],\n",
+    "    [\"B\", \"Avengers\", 2],\n",
+    "    [\"B\", \"Star Wars\", 5],\n",
+    "    [\"B\", \"Mean Girls\", 1],\n",
+    "    [\"B\", \"Mean Girls 2\", 1],\n",
+    "    [\"C\", \"Avengers\", 5],\n",
+    "    [\"C\", \"Star Wars\", 3],\n",
+    "    [\"C\", \"Mean Girls\", 1],\n",
+    "    [\"C\", \"Mean Girls 2\", 1]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get a ratings matrix\n",
+    "ui_df = pd.DataFrame(data, columns = [\"user\", \"movie\", \"rating\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratings_matrix = ui_df.pivot(index = \"user\", columns = \"movie\", values = \"rating\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>movie</th>\n",
+       "      <th>Avengers</th>\n",
+       "      <th>Mean Girls</th>\n",
+       "      <th>Mean Girls 2</th>\n",
+       "      <th>Star Wars</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>A</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>C</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "movie  Avengers  Mean Girls  Mean Girls 2  Star Wars\n",
+       "user                                                \n",
+       "A             5           1             1          5\n",
+       "B             2           1             1          5\n",
+       "C             5           1             1          3"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# rows are users columns are items (movies)\n",
+    "# could just as easily be products from Amazon\n",
+    "# or any website\n",
+    "# cells don't have to be ratings, could be purchase frequency\n",
+    "ratings_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.spatial.distance import cdist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.        , 3.        , 2.        ],\n",
+       "       [3.        , 0.        , 3.60555128],\n",
+       "       [2.        , 3.60555128, 0.        ]])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdist(ratings_matrix, ratings_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user_user_sim = pd.DataFrame(cdist(ratings_matrix, ratings_matrix), index = ratings_matrix.index, \n",
+    "                            columns = ratings_matrix.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>user</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>A</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>B</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.605551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>C</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.605551</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user    A         B         C\n",
+       "user                         \n",
+       "A     0.0  3.000000  2.000000\n",
+       "B     3.0  0.000000  3.605551\n",
+       "C     2.0  3.605551  0.000000"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_user_sim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### what are we building essentially?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>user</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>B</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>C</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user  A  B  C\n",
+       "user         \n",
+       "A     0  2  1\n",
+       "B     1  0  2\n",
+       "C     2  0  1"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argsort(user_user_sim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>user</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>B</td>\n",
+       "      <td>B</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user  A  B  C\n",
+       "user         \n",
+       "A     A  C  B\n",
+       "B     B  A  C\n",
+       "C     C  A  B"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argsort(user_user_sim).applymap(lambda x: user_user_sim.columns[x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>user</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user  B\n",
+       "user   \n",
+       "A     C\n",
+       "B     A\n",
+       "C     A"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop last row because we can't be most similar to ourselves\n",
+    "np.argsort(user_user_sim).applymap(lambda x: user_user_sim.columns[x]).iloc[:,1:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop last row because we can't be most similar to ourselves\n",
+    "nearest_neighbor_df = np.argsort(user_user_sim).applymap(lambda x: user_user_sim.columns[x]).iloc[:,1:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_dict.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nearest_neighbor_df.columns = [\"nearest_neighbor\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'A': {'nearest_neighbor': 'C'},\n",
+       " 'B': {'nearest_neighbor': 'A'},\n",
+       " 'C': {'nearest_neighbor': 'A'}}"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nearest_neighbor_df.to_dict(\"index\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'A': {'Avengers': 5, 'Mean Girls': 1, 'Mean Girls 2': 1, 'Star Wars': 5},\n",
+       " 'B': {'Avengers': 2, 'Mean Girls': 1, 'Mean Girls 2': 1, 'Star Wars': 5},\n",
+       " 'C': {'Avengers': 5, 'Mean Girls': 1, 'Mean Girls 2': 1, 'Star Wars': 3}}"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ratings_matrix.to_dict(\"index\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# once we have this data, we could go through and ad some sort of script that says\n",
+    "# recommend movies nearest neighbors like, but user hasn't seen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### this was a high level example of a user-user collaborative filtering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>user</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>A</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>B</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.605551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>C</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.605551</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user    A         B         C\n",
+       "user                         \n",
+       "A     0.0  3.000000  2.000000\n",
+       "B     3.0  0.000000  3.605551\n",
+       "C     2.0  3.605551  0.000000"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_user_sim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What if we did item-item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.        , 5.74456265, 5.74456265, 3.60555128],\n",
+       "       [5.74456265, 0.        , 0.        , 6.        ],\n",
+       "       [5.74456265, 0.        , 0.        , 6.        ],\n",
+       "       [3.60555128, 6.        , 6.        , 0.        ]])"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdist(ratings_matrix.T, ratings_matrix.T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "item_item_sim = pd.DataFrame(cdist(ratings_matrix.T, ratings_matrix.T), index = ratings_matrix.T.index, \n",
+    "                            columns = ratings_matrix.T.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>movie</th>\n",
+       "      <th>Avengers</th>\n",
+       "      <th>Mean Girls</th>\n",
+       "      <th>Mean Girls 2</th>\n",
+       "      <th>Star Wars</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>movie</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>Avengers</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.744563</td>\n",
+       "      <td>5.744563</td>\n",
+       "      <td>3.605551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Mean Girls</td>\n",
+       "      <td>5.744563</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Mean Girls 2</td>\n",
+       "      <td>5.744563</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Star Wars</td>\n",
+       "      <td>3.605551</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "movie         Avengers  Mean Girls  Mean Girls 2  Star Wars\n",
+       "movie                                                      \n",
+       "Avengers      0.000000    5.744563      5.744563   3.605551\n",
+       "Mean Girls    5.744563    0.000000      0.000000   6.000000\n",
+       "Mean Girls 2  5.744563    0.000000      0.000000   6.000000\n",
+       "Star Wars     3.605551    6.000000      6.000000   0.000000"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "item_item_sim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* would be quite similar, if user saw Avengers and rated it high, we could recommend movies similar to the avengers\n",
+    "* this example has it backwards, but in reality we would have more users then items (how many users does Netflix have vs. movies?)  thus a user-user matrix is pretty huge.\n",
+    "* remember, simple rec systems are just some linear algebra, the concepts are pretty simple\n",
+    "    * get some sort of vector representation for your observations\n",
+    "    * find a way to quantify the similarity"
    ]
   },
   {
diff --git a/lectures/Sample Lecture - Objects.ipynb b/lectures/Sample Lecture - Objects.ipynb
new file mode 100644
index 0000000..562c71b
--- /dev/null
+++ b/lectures/Sample Lecture - Objects.ipynb	
@@ -0,0 +1,1063 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### OOO\n",
+    "* simple OOO example\n",
+    "* classes - template of objects\n",
+    "* objects - member or instance of class\n",
+    "* classes can have\n",
+    "    * characteristics/attribuets\n",
+    "    * methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple framework for a dog\n",
+    "class dog:\n",
+    "    \n",
+    "    # these are params here, things we set when\n",
+    "    # we initialize our instance of our class\n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class '__main__.dog'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dog = dog(color = \"brown\", breed = \"Corgi\", name = \"Mr. Bacon\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'brown'"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Corgi'"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.breed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'breed',\n",
+       " 'color',\n",
+       " 'name']"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(my_dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.dog"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(my_dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple framework for a dog\n",
+    "class dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        \n",
+    "    def speak(self,xxx=None):\n",
+    "        print('My name is ' + self.name + ' and I am a '+ self.color +' ' + self.breed)\n",
+    "        if xxx==None: \n",
+    "            xxx = \"You dont like me!\"\n",
+    "        print(xxx)\n",
+    "        \n",
+    "    def bark(self):\n",
+    "        print(\"Woof!Woof!\")    \n",
+    "        \n",
+    "    def paintmydog(self, newcolor):\n",
+    "        self.color = newcolor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dog = dog(color = \"brown\", breed = \"Corgi\", name = \"Mr. Bacon\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brown Mr. Bacon\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(my_dog.color, my_dog.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method dog.speak of <__main__.dog object at 0x00000251EEA3C388>>"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.speak"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My name is Mr. Bacon and I am a brown Corgi\n",
+      "I love you!\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_dog.speak(xxx='I love you!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'bark',\n",
+       " 'breed',\n",
+       " 'color',\n",
+       " 'name',\n",
+       " 'paintmydog',\n",
+       " 'speak']"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(my_dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'red'"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.paintmydog(\"red\")\n",
+    "my_dog.color\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My name is Mr. Bacon and I am a red Corgi\n",
+      "You dont like me!\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_dog.speak()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple framework for a dog\n",
+    "class dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name, distance=0):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.distance += distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog = dog(color = \"brown\", breed = \"Corgi\", name = \"Mr. Bacon\")\n",
+    "my_dog.distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.walk(5)\n",
+    "my_dog.distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.walk(13)\n",
+    "my_dog.distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.walk(18)\n",
+    "my_dog.distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inheritence\n",
+    "* Inherit properties and functions from other objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.distance += distance\n",
+    "        \n",
+    "class Retreiver(Dog):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dog = Retreiver(color = \"brown\", breed = \"Retriever\", name = \"Mr. Bacon\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'brown'"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog.color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'walk']"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.distance += distance\n",
+    "        \n",
+    "class Retreiver(Dog):\n",
+    "\n",
+    "    def speak(self):\n",
+    "        print(\"My name is {}\".format(self.name))\n",
+    "        \n",
+    "    def blink182(self):\n",
+    "        print(\"What is my age again?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'blink182',\n",
+       " 'breed',\n",
+       " 'color',\n",
+       " 'distance',\n",
+       " 'name',\n",
+       " 'speak',\n",
+       " 'walk']"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dog = Retreiver(color = \"brown\", breed = \"Retriever\", name = \"Mr. PinK\")\n",
+    "dir(my_dog)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        return 'My name is ' + self.name + ' and I am a '+ self.color +' ' + self.breed "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My name is Mr. Bacon and I am a brown Corgi\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_dog = Dog(color = \"brown\", breed = \"Corgi\", name = \"Mr. Bacon\")\n",
+    "print(my_dog) #What will this return?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'breed',\n",
+       " 'color',\n",
+       " 'name']"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(my_dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.__total_distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.__total_distance += distance\n",
+    "    def get_dist(self): \n",
+    "        print(self.__total_distance)\n",
+    "        return self.__total_distance\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "10\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "newdog = Dog('red','corgi','mr raf')\n",
+    "newdog.get_dist()\n",
+    "newdog.walk(10)\n",
+    "newdog.get_dist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Class Methods\n",
+    "* takes cls as the first input\n",
+    "* bound to the class, not the instance of the object\n",
+    "* can access class variables though not instance variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    class_atr = 10\n",
+    "    \n",
+    "    @classmethod\n",
+    "    def speak(cls, speak):\n",
+    "        print(speak)\n",
+    "        \n",
+    "    @classmethod\n",
+    "    def add(cls,x,y):\n",
+    "        return x+y\n",
+    "    \n",
+    "    @classmethod\n",
+    "    def printer(cls):\n",
+    "        print(cls.class_atr)\n",
+    "        \n",
+    "    @classmethod\n",
+    "    def class_add(cls):\n",
+    "        cls.class_atr = cls.class_atr + 10\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        self.distance = Dog.add(self.distance, distance)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rex =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
+    "thor =  Dog(color = \"blue\", breed = \"retreiver\", name = \"thor\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "rex.printer()\n",
+    "thor.class_add()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n"
+     ]
+    }
+   ],
+   "source": [
+    "rex.class_add()\n",
+    "rex.printer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "say something\n"
+     ]
+    }
+   ],
+   "source": [
+    "rex.speak(\"say something\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "say something\n"
+     ]
+    }
+   ],
+   "source": [
+    "thor.speak(\"say something\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r.walk(5)\n",
+    "r.walk(10)\n",
+    "r.distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Static Method\n",
+    "* neither bound to the instance or class\n",
+    "* no access to the instance or class attribuets\n",
+    "* could be a standalone function but we want to keep it in the class\n",
+    "* for instance, distance measures inside a KNN class.  then we can reference the functions when we have a fit method\n",
+    "* aren't super useful due to limited access of information\n",
+    "* utility type funciton"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Distance:\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def add(a,b):\n",
+    "        print(a+b)\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def sub(a,b):\n",
+    "        print(a-b)\n",
+    "    \n",
+    "    def __init__(self,x,y):\n",
+    "        self.x = x\n",
+    "        self.y = y\n",
+    "        \n",
+    "    def operate(self, how):\n",
+    "        if how == \"add\":\n",
+    "            Distance.add(self.x, self.y)\n",
+    "        else:\n",
+    "            Distance.sub(self.x, self.y)\n",
+    "        \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = Distance(10,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n"
+     ]
+    }
+   ],
+   "source": [
+    "d.operate(\"add\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "d.operate(\"sub\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Characteristics/Properties/Attributes\n",
+    "* accessed using dot notation\n",
+    "* methods are used using dot notation and parentheses ()\n",
+    "* we can now read the sklearn documentation that much better\n",
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
+    "* since sklearn documentation is consistent, we can easily look at any if the page and disect how the data structure works\n",
+    "    * what methods we can use\n",
+    "    * what characteristics we can access\n",
+    "    * what params we can pass\n",
+    "* https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/msca/base.py b/msca/base.py
index a150939..e69de29 100644
--- a/msca/base.py
+++ b/msca/base.py
@@ -1,15 +0,0 @@
-
-
-
-def raf_sum(a,b):
-  return a+b
-
-
- def Lucia(x):
-     x**2
-
-def potato(state):
-  if state=='hot':
-    print('hot potato')
-  else:
-    print('No cool')
diff --git a/msca/face_det.py b/msca/face_det.py
index 75fb42d..1a43ec9 100644
--- a/msca/face_det.py
+++ b/msca/face_det.py
@@ -1,16 +1,14 @@
-
+import cv2
 
 def face_detect(imgPath, cascPath=None):
-	try :
-		import cv2
-
+	
 	if cascPath==None:
 		print('No cascadades.. No cool')
 		return
 
 	faceCascade = cv2.CascadeClassifier(cascPath)
 
-	image = cv2.imread(imgPath)
+	image = cv2.imread(imgPath,0)
 	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
 	faces = faceCascade.detectMultiScale(
diff --git a/presentation/MSCA_37014_v1.pptx b/presentation/MSCA_37014_v1.pptx
index 800939fe35eead18cda70c8d0363dfb9909ed080..46062519947c3cbe24136bbea6f08b49f746c8d9 100644
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From eb99d7d6fe10e41f3e3a061aef2b9bd3708a1504 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Mon, 2 Mar 2020 13:15:58 -0600
Subject: [PATCH 28/36] rebase

---
 .gitignore                                    |    3 +-
 build/lib/data/__init__.py                    |    0
 build/lib/msca/__init__.py                    |    4 -
 build/lib/msca/base.py                        |    0
 build/lib/msca/face_det.py                    |   29 -
 build/lib/msca/face_recon.py                  |    0
 build/lib/msca/imagenet.py                    |    0
 build/lib/msca/nqueens.py                     |   27 -
 .../Week 5-checkpoint.ipynb                   | 2160 -----------------
 .../week 1-checkpoint.ipynb                   | 1040 --------
 .../week 2-checkpoint.ipynb                   |    6 -
 follow_up_questions/Week 5.ipynb              | 2160 -----------------
 follow_up_questions/week 1.ipynb              | 1040 --------
 follow_up_questions/week 2.ipynb              |   32 -
 .../Assignment 1-checkpoint.ipynb             |  491 ----
 .../Assignment 2-checkpoint.ipynb             |  257 --
 homework/Assignment 1 Answers.ipynb           | 1312 ----------
 homework/Assignment 1.ipynb                   |  633 -----
 homework/Assignment 2.ipynb                   |  257 --
 other_topics/multiprocessing_run.py           |   39 -
 20 files changed, 2 insertions(+), 9488 deletions(-)
 delete mode 100644 build/lib/data/__init__.py
 delete mode 100644 build/lib/msca/__init__.py
 delete mode 100644 build/lib/msca/base.py
 delete mode 100644 build/lib/msca/face_det.py
 delete mode 100644 build/lib/msca/face_recon.py
 delete mode 100644 build/lib/msca/imagenet.py
 delete mode 100644 build/lib/msca/nqueens.py
 delete mode 100644 follow_up_questions/.ipynb_checkpoints/Week 5-checkpoint.ipynb
 delete mode 100644 follow_up_questions/.ipynb_checkpoints/week 1-checkpoint.ipynb
 delete mode 100644 follow_up_questions/.ipynb_checkpoints/week 2-checkpoint.ipynb
 delete mode 100644 follow_up_questions/Week 5.ipynb
 delete mode 100644 follow_up_questions/week 1.ipynb
 delete mode 100644 follow_up_questions/week 2.ipynb
 delete mode 100644 homework/.ipynb_checkpoints/Assignment 1-checkpoint.ipynb
 delete mode 100644 homework/.ipynb_checkpoints/Assignment 2-checkpoint.ipynb
 delete mode 100644 homework/Assignment 1 Answers.ipynb
 delete mode 100644 homework/Assignment 1.ipynb
 delete mode 100644 homework/Assignment 2.ipynb
 delete mode 100644 other_topics/multiprocessing_run.py

diff --git a/.gitignore b/.gitignore
index 977fab8..1f5a32f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,4 +4,5 @@
 *.pyc
 /dist/
 /*.egg-info
-__pycache__
\ No newline at end of file
+__pycache__
+/build*/*
diff --git a/build/lib/data/__init__.py b/build/lib/data/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/build/lib/msca/__init__.py b/build/lib/msca/__init__.py
deleted file mode 100644
index cbdcccb..0000000
--- a/build/lib/msca/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from . import base
-from . import face_det
-from . import nqueens
-from . import imagenet
diff --git a/build/lib/msca/base.py b/build/lib/msca/base.py
deleted file mode 100644
index e69de29..0000000
diff --git a/build/lib/msca/face_det.py b/build/lib/msca/face_det.py
deleted file mode 100644
index 75fb42d..0000000
--- a/build/lib/msca/face_det.py
+++ /dev/null
@@ -1,29 +0,0 @@
-
-
-def face_detect(imgPath, cascPath=None):
-	try :
-		import cv2
-
-	if cascPath==None:
-		print('No cascadades.. No cool')
-		return
-
-	faceCascade = cv2.CascadeClassifier(cascPath)
-
-	image = cv2.imread(imgPath)
-	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-	faces = faceCascade.detectMultiScale(
-	    gray,
-	    scaleFactor=1.1,
-	    minNeighbors=5,
-	    minSize=(30, 30)
-	)
-
-
-	return image, faces
-
-def draw_squares(image, faces):
-	for (x, y, w, h) in faces:
-	    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
-	return image
diff --git a/build/lib/msca/face_recon.py b/build/lib/msca/face_recon.py
deleted file mode 100644
index e69de29..0000000
diff --git a/build/lib/msca/imagenet.py b/build/lib/msca/imagenet.py
deleted file mode 100644
index e69de29..0000000
diff --git a/build/lib/msca/nqueens.py b/build/lib/msca/nqueens.py
deleted file mode 100644
index 6ef119b..0000000
--- a/build/lib/msca/nqueens.py
+++ /dev/null
@@ -1,27 +0,0 @@
-class raf_nq:
-    def __init__(self, size, show=False):
-        self.size = size
-        self.solutions = 0
-        self.solve()
-
-    def solve(self):
-        positions = [-1] * self.size
-        self.put_queen(positions, 0)
-        return self.solutions
-
-    def put_queen(self, positions, target_row):
-        if target_row == self.size:
-            self.solutions += 1
-        else:
-            for column in range(self.size):
-                if self.check_place(positions, target_row, column):
-                    positions[target_row] = column
-                    self.put_queen(positions, target_row + 1)
-
-    def check_place(self, positions, ocuppied_rows, column):
-        for i in range(ocuppied_rows):
-            if positions[i] == column or \
-                positions[i] - i == column - ocuppied_rows or \
-                positions[i] + i == column + ocuppied_rows:
-                return False
-        return True
diff --git a/follow_up_questions/.ipynb_checkpoints/Week 5-checkpoint.ipynb b/follow_up_questions/.ipynb_checkpoints/Week 5-checkpoint.ipynb
deleted file mode 100644
index 1cff6ec..0000000
--- a/follow_up_questions/.ipynb_checkpoints/Week 5-checkpoint.ipynb	
+++ /dev/null
@@ -1,2160 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Broadcasting\n",
-    "* duplicate a smaller array so we can add and subtract arrays that aren't the same size\n",
-    "* \"    The term broadcasting describes how numpy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes.\" - scipy\n",
-    "* NumPy does not actually duplicate the smaller array; instead, it makes memory and computationally efficient use of existing structures in memory that in effect achieve the same result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 3, 3, 3])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# one dimensional\n",
-    "array = np.array([2,2,2,2])\n",
-    "array + 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2],\n",
-       "       [2, 2]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([[1,1],[1,1]])\n",
-    "array + 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* The dimensions are considered in reverse order, starting with the trailing dimension; for example, looking at columns before rows in a two-dimensional case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1,3],\n",
-    "    [2,2,3]\n",
-    "])\n",
-    "\n",
-    "# we have 2 rows and 3 columns\n",
-    "print(array.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* consider a case where we have a 2 by 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2)\n",
-      "(1, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1],\n",
-    "    [2,2]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([1,5])\n",
-    "array1 = np.reshape(array1, (1,2))\n",
-    "print(array.shape)\n",
-    "print(array1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 6],\n",
-       "       [3, 7]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# each vector is added to each row\n",
-    "# since our second dimension aligns, it adds\n",
-    "# along the columns, because that is our second dimension\n",
-    "# think of it as \"hoping\" down the, like long, like a column\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 1],\n",
-       "       [2, 2]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 4])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# remember when we sum using axis = 1, we sum columns\n",
-    "# so when our axis 1 aligns above, we are hoping down the columns\n",
-    "# and broadcasting that vecotr\n",
-    "# in the below case, we are hoping down each column and adding them together\n",
-    "np.sum(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2)\n",
-      "(2, 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1],\n",
-    "    [2,2]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([1,5])\n",
-    "array1 = np.reshape(array1, (2,1))\n",
-    "print(array.shape)\n",
-    "print(array1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2],\n",
-       "       [7, 7]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# if we  flip the dimensions of array 1, the behavior changes\n",
-    "# now the first dimension (Rows aligns)\n",
-    "# we \"hop\" across and broadcast\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 1],\n",
-       "       [2, 2]])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 3])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# when we make use of our first axis here, to sum\n",
-    "# we sort of hop along each column and add them together\n",
-    "# similir to how we are broadings, we are hoping along\n",
-    "# each column and adding that vector\n",
-    "np.sum(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* can only be performed when the shape of each dimension in the arrays are equal or one has the dimension size of 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "operands could not be broadcast together with shapes (2,) (3,) ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-49-a46bbd09ccf2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0marray1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0marray\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,) (3,) "
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([1,2])\n",
-    "array1 = np.array([1,2,3])\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "operands could not be broadcast together with shapes (2,4) (2,2) ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-fa125fcfcab3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m ])\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0marray\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,4) (2,2) "
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1,1,1],\n",
-    "    [1,1,1,1]\n",
-    "])\n",
-    "\n",
-    "# can't just  expand this out or double it\n",
-    "array1 = np.array([\n",
-    "    [1,1],\n",
-    "    [1,1]\n",
-    "])\n",
-    "\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2, 2, 2],\n",
-       "       [2, 2, 2, 2]])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1,1,1],\n",
-    "    [1,1,1,1]\n",
-    "])\n",
-    "\n",
-    "# can't just  expand this out or double it\n",
-    "array1 = np.array([\n",
-    "    [1,1],\n",
-    "    [1,1]\n",
-    "])\n",
-    "\n",
-    "# could reshape it though\n",
-    "array1 = np.reshape(array1, (1,4))\n",
-    "\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2, 2, 2],\n",
-       "       [2, 2, 2, 2]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1,1,1],\n",
-    "    [1,1,1,1]\n",
-    "])\n",
-    "\n",
-    "# can't just  expand this out or double it\n",
-    "array1 = np.array([\n",
-    "    [1],\n",
-    "    [1]\n",
-    "])\n",
-    "\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* play around with it"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Apply Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c\n",
-       "0  1  2  3\n",
-       "1  4  5  6"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = [\n",
-    "    [1,2,3],\n",
-    "    [4,5,6]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\", \"c\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    <class 'pandas.core.series.Series'>\n",
-       "b    <class 'pandas.core.series.Series'>\n",
-       "c    <class 'pandas.core.series.Series'>\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# when we do apply returns a series\n",
-    "l = lambda x: type(x)\n",
-    "df.apply(l, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1\n",
-       "b    2\n",
-       "c    3\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# remember this is a series where each series is a column of data\n",
-    "# so we can access elements of that series using bracket notation\n",
-    "# here we access the first element of each column\n",
-    "# think of this as slice 1\n",
-    "l = lambda x: x[0]\n",
-    "df.apply(l, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    4\n",
-       "b    5\n",
-       "c    6\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# and slice two\n",
-    "l = lambda x: x[1]\n",
-    "df.apply(l, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    <class 'pandas.core.series.Series'>\n",
-       "1    <class 'pandas.core.series.Series'>\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#  now with rows\n",
-    "l = lambda x: type(x)\n",
-    "df.apply(l, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    1\n",
-       "1    4\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l = lambda x: x[\"a\"]\n",
-    "df.apply(l, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    2\n",
-       "1    5\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l = lambda x: x[\"b\"]\n",
-    "df.apply(l, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>A</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>B</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c\n",
-       "0  1  2  A\n",
-       "1  4  5  B\n",
-       "2  1  2  C"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# let's see it in action\n",
-    "data = [\n",
-    "    [1,2,\"A\"],\n",
-    "    [4,5,\"B\"],\n",
-    "    [1,2,\"C\"]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\", \"c\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def f(x):\n",
-    "    if x == \"A\":\n",
-    "        return \"this is a\"\n",
-    "    elif x == \"B\":\n",
-    "        return \"this is b\"\n",
-    "    else:\n",
-    "        return \"this is c\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>new_col</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>A</td>\n",
-       "      <td>this is a</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>B</td>\n",
-       "      <td>this is b</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>this is c</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c    new_col\n",
-       "0  1  2  A  this is a\n",
-       "1  4  5  B  this is b\n",
-       "2  1  2  C  this is c"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# being applied to specific column\n",
-    "df[\"new_col\"] = df[\"c\"].apply(f)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    A x\n",
-       "1    B x\n",
-       "2    C x\n",
-       "Name: c, dtype: object"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l = lambda x: x + \" x\"\n",
-    "df[\"c\"].apply(l)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Piping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "0  1  2\n",
-       "1  4  5\n",
-       "2  1  2"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = [\n",
-    "    [1,2],\n",
-    "    [4,5],\n",
-    "    [1,2]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "0  1  2\n",
-       "1  4  5\n",
-       "2  1  2"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.pipe(lambda x: x + 2).pipe(lambda x: x - 5)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a   b\n",
-       "0  5  10\n",
-       "1  5  10\n",
-       "2  5  10"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# the first argument of pipe takes the \"data\" argument, which is just passing in the dataframe\n",
-    "# then we can think of the function as generally manipulating the dataframe\n",
-    "data = [\n",
-    "    [1,2],\n",
-    "    [4,5],\n",
-    "    [1,2]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\"]\n",
-    "\n",
-    "def fa(x):\n",
-    "    x[\"a\"] = 5\n",
-    "    return x\n",
-    "\n",
-    "def fb(x):\n",
-    "    x[\"b\"] = 10\n",
-    "    return x\n",
-    "    \n",
-    "    \n",
-    "df.pipe(fa).pipe(fb)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a   b\n",
-       "0  5  10\n",
-       "1  5  10\n",
-       "2  5  10"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = [\n",
-    "    [1,2],\n",
-    "    [4,5],\n",
-    "    [1,2]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\"]\n",
-    "\n",
-    "df = fa(df)\n",
-    "df = fb(df)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pipe on single columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def f(x,i):\n",
-    "    return x-i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>-6</td>\n",
-       "      <td>-5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "0 -9 -8\n",
-       "1 -6 -5\n",
-       "2 -9 -8"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.pipe(f,i = 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rolling Functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val</th>\n",
-       "      <th>id</th>\n",
-       "      <th>period</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   val id period\n",
-       "0    1  A      1\n",
-       "1    4  A      2\n",
-       "2    1  A      3\n",
-       "3    1  A      4\n",
-       "4    4  A      5\n",
-       "5    1  A      6"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# let's see it in action\n",
-    "data = [\n",
-    "    [1,\"A\",\"1\"],\n",
-    "    [4,\"A\",\"2\"],\n",
-    "    [1,\"A\",\"3\"],\n",
-    "    [1,\"A\",\"4\"],\n",
-    "    [4,\"A\",\"5\"],\n",
-    "    [1,\"A\",\"6\"]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"val\", \"id\", \"period\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    NaN\n",
-       "1    5.0\n",
-       "2    5.0\n",
-       "3    2.0\n",
-       "4    5.0\n",
-       "5    5.0\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(2).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    NaN\n",
-       "1    NaN\n",
-       "2    6.0\n",
-       "3    6.0\n",
-       "4    6.0\n",
-       "5    6.0\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(3).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    NaN\n",
-       "1    2.5\n",
-       "2    2.5\n",
-       "3    1.0\n",
-       "4    2.5\n",
-       "5    2.5\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(2).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     NaN\n",
-       "1     NaN\n",
-       "2     NaN\n",
-       "3    1.75\n",
-       "4    2.50\n",
-       "5    1.75\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(4).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    NaN\n",
-       "1    2.5\n",
-       "2    2.5\n",
-       "3    1.0\n",
-       "4    2.5\n",
-       "5    2.5\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(2).median()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"rolling_mean\"] = df[\"val\"].rolling(2).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val</th>\n",
-       "      <th>id</th>\n",
-       "      <th>period</th>\n",
-       "      <th>rolling_mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   val id period  rolling_mean\n",
-       "0    1  A      1           NaN\n",
-       "1    4  A      2           2.5\n",
-       "2    1  A      3           2.5\n",
-       "3    1  A      4           1.0\n",
-       "4    4  A      5           2.5\n",
-       "5    1  A      6           2.5"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "periods = [1,2,3,4]\n",
-    "for p in periods:\n",
-    "    name = \"rolling_mean_p_{}\".format(str(p))\n",
-    "    df[name] = df[\"val\"].rolling(p).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val</th>\n",
-       "      <th>id</th>\n",
-       "      <th>period</th>\n",
-       "      <th>rolling_mean</th>\n",
-       "      <th>rolling_mean_p_1</th>\n",
-       "      <th>rolling_mean_p_2</th>\n",
-       "      <th>rolling_mean_p_3</th>\n",
-       "      <th>rolling_mean_p_4</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.75</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   val id period  rolling_mean  rolling_mean_p_1  rolling_mean_p_2  \\\n",
-       "0    1  A      1           NaN               1.0               NaN   \n",
-       "1    4  A      2           2.5               4.0               2.5   \n",
-       "2    1  A      3           2.5               1.0               2.5   \n",
-       "3    1  A      4           1.0               1.0               1.0   \n",
-       "4    4  A      5           2.5               4.0               2.5   \n",
-       "5    1  A      6           2.5               1.0               2.5   \n",
-       "\n",
-       "   rolling_mean_p_3  rolling_mean_p_4  \n",
-       "0               NaN               NaN  \n",
-       "1               NaN               NaN  \n",
-       "2               2.0               NaN  \n",
-       "3               2.0              1.75  \n",
-       "4               2.0              2.50  \n",
-       "5               2.0              1.75  "
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Window Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val</th>\n",
-       "      <th>id</th>\n",
-       "      <th>period</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>11</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>15</td>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>7</td>\n",
-       "      <td>B</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>11</td>\n",
-       "      <td>4</td>\n",
-       "      <td>B</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    val id period\n",
-       "0     1  A      1\n",
-       "1     4  A      2\n",
-       "2     1  A      3\n",
-       "3     1  A      4\n",
-       "4     4  A      5\n",
-       "5     1  A      6\n",
-       "6    11  B      1\n",
-       "7     4  B      2\n",
-       "8    15  B      3\n",
-       "9     8  B      4\n",
-       "10    7  B      5\n",
-       "11    4  B      6"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# let's see it in action\n",
-    "data = [\n",
-    "    [1,\"A\",\"1\"],\n",
-    "    [4,\"A\",\"2\"],\n",
-    "    [1,\"A\",\"3\"],\n",
-    "    [1,\"A\",\"4\"],\n",
-    "    [4,\"A\",\"5\"],\n",
-    "    [1,\"A\",\"6\"],\n",
-    "    [11,\"B\",\"1\"],\n",
-    "    [4,\"B\",\"2\"],\n",
-    "    [15,\"B\",\"3\"],\n",
-    "    [8,\"B\",\"4\"],\n",
-    "    [7,\"B\",\"5\"],\n",
-    "    [4,\"B\",\"6\"]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"val\", \"id\", \"period\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>level_1</th>\n",
-       "      <th>val</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>B</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>B</td>\n",
-       "      <td>7</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>B</td>\n",
-       "      <td>8</td>\n",
-       "      <td>19.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>B</td>\n",
-       "      <td>9</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>B</td>\n",
-       "      <td>10</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>11</td>\n",
-       "      <td>B</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id  level_1   val\n",
-       "0   A        0   NaN\n",
-       "1   A        1   5.0\n",
-       "2   A        2   5.0\n",
-       "3   A        3   2.0\n",
-       "4   A        4   5.0\n",
-       "5   A        5   5.0\n",
-       "6   B        6   NaN\n",
-       "7   B        7  15.0\n",
-       "8   B        8  19.0\n",
-       "9   B        9  23.0\n",
-       "10  B       10  15.0\n",
-       "11  B       11  11.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('id')['val'].rolling(2).sum().reset_index()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Time Series in Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# to add"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Raw Strings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello there \n",
-      "this will do a return\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"hello there \\nthis will do a return\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello there \\nthis will not do a return\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(r\"hello there \\nthis will not do a return\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\t this will tab\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\t this will tab\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\t this will not tab\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(r\"\\t this will not tab\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/follow_up_questions/.ipynb_checkpoints/week 1-checkpoint.ipynb b/follow_up_questions/.ipynb_checkpoints/week 1-checkpoint.ipynb
deleted file mode 100644
index c239cdb..0000000
--- a/follow_up_questions/.ipynb_checkpoints/week 1-checkpoint.ipynb	
+++ /dev/null
@@ -1,1040 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Underscores"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In truth we won't worry much about underscores, though I wanted to provide a bit of info on their use Python, specifically in relation to the double underscored attribuets and methods we saw running the dir() function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Double Underscore\n",
-    "* these refer to \"magic methods\", meaning python uses them behind the scenes and in most cases we don't generally want to alter or change how they function it.  think of them as python setting up some nuts and bolts to make your programs run\n",
-    "* for instance lists and integers (among other datatypes) can be added together using the + symbol or \\_\\_add\\_\\_.  Though using the symbol is far easier.  In this case, when we do list + list, the + sign tells python to call the \\_\\_add\\_\\_ method.\n",
-    "* \\_\\_init\\_\\_ is the initialization method for an object, which will will use, but not alter the functionality of\n",
-    "* \\_\\_iter\\_\\_ is used to return an iteration, so this is a method that iterators make use of\n",
-    "* \\_\\_new\\_\\_ is called to make a new object and used by the \\_\\_init\\_\\_ method\n",
-    "* for brevity I will leave it there, but these aren't something we'll need to be altering the functionality"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2]\n",
-    "b = [3,4]\n",
-    "a + b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.__add__(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "to the second point, there isn't a great way to tell the difference between methods and attributes in dir(), though we can make use of the getmembers function from inspect.  we can see that methods like extend, append etc. are listed as functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from inspect import getmembers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('__add__', <method-wrapper '__add__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__class__', list),\n",
-       " ('__contains__',\n",
-       "  <method-wrapper '__contains__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__delattr__',\n",
-       "  <method-wrapper '__delattr__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__delitem__',\n",
-       "  <method-wrapper '__delitem__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__dir__', <function list.__dir__()>),\n",
-       " ('__doc__',\n",
-       "  'Built-in mutable sequence.\\n\\nIf no argument is given, the constructor creates a new empty list.\\nThe argument must be an iterable if specified.'),\n",
-       " ('__eq__', <method-wrapper '__eq__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__format__', <function list.__format__(format_spec, /)>),\n",
-       " ('__ge__', <method-wrapper '__ge__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__getattribute__',\n",
-       "  <method-wrapper '__getattribute__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__getitem__', <function list.__getitem__>),\n",
-       " ('__gt__', <method-wrapper '__gt__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__hash__', None),\n",
-       " ('__iadd__', <method-wrapper '__iadd__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__imul__', <method-wrapper '__imul__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__init__', <method-wrapper '__init__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__init_subclass__', <function list.__init_subclass__>),\n",
-       " ('__iter__', <method-wrapper '__iter__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__le__', <method-wrapper '__le__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__len__', <method-wrapper '__len__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__lt__', <method-wrapper '__lt__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__mul__', <method-wrapper '__mul__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__ne__', <method-wrapper '__ne__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__new__', <function list.__new__(*args, **kwargs)>),\n",
-       " ('__reduce__', <function list.__reduce__()>),\n",
-       " ('__reduce_ex__', <function list.__reduce_ex__(protocol, /)>),\n",
-       " ('__repr__', <method-wrapper '__repr__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__reversed__', <function list.__reversed__()>),\n",
-       " ('__rmul__', <method-wrapper '__rmul__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__setattr__',\n",
-       "  <method-wrapper '__setattr__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__setitem__',\n",
-       "  <method-wrapper '__setitem__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__sizeof__', <function list.__sizeof__()>),\n",
-       " ('__str__', <method-wrapper '__str__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__subclasshook__', <function list.__subclasshook__>),\n",
-       " ('append', <function list.append(object, /)>),\n",
-       " ('clear', <function list.clear()>),\n",
-       " ('copy', <function list.copy()>),\n",
-       " ('count', <function list.count(value, /)>),\n",
-       " ('extend', <function list.extend(iterable, /)>),\n",
-       " ('index', <function list.index(value, start=0, stop=9223372036854775807, /)>),\n",
-       " ('insert', <function list.insert(index, object, /)>),\n",
-       " ('pop', <function list.pop(index=-1, /)>),\n",
-       " ('remove', <function list.remove(value, /)>),\n",
-       " ('reverse', <function list.reverse()>),\n",
-       " ('sort', <function list.sort(*, key=None, reverse=False)>)]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "getmembers(lst)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "if we make a quick dummy class (we'll cover this in lecture 3), and give it an attribuet of name, we can see the tuple of attribuet name and value using the getmembers function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('__class__', __main__.Dog),\n",
-       " ('__delattr__',\n",
-       "  <method-wrapper '__delattr__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__dict__', {'name': 'Brian'}),\n",
-       " ('__dir__', <function Dog.__dir__()>),\n",
-       " ('__doc__', None),\n",
-       " ('__eq__', <method-wrapper '__eq__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__format__', <function Dog.__format__(format_spec, /)>),\n",
-       " ('__ge__', <method-wrapper '__ge__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__getattribute__',\n",
-       "  <method-wrapper '__getattribute__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__gt__', <method-wrapper '__gt__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__hash__', <method-wrapper '__hash__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__init__',\n",
-       "  <bound method Dog.__init__ of <__main__.Dog object at 0x7fe08823d610>>),\n",
-       " ('__init_subclass__', <function Dog.__init_subclass__>),\n",
-       " ('__le__', <method-wrapper '__le__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__lt__', <method-wrapper '__lt__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__module__', '__main__'),\n",
-       " ('__ne__', <method-wrapper '__ne__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__new__', <function object.__new__(*args, **kwargs)>),\n",
-       " ('__reduce__', <function Dog.__reduce__()>),\n",
-       " ('__reduce_ex__', <function Dog.__reduce_ex__(protocol, /)>),\n",
-       " ('__repr__', <method-wrapper '__repr__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__setattr__',\n",
-       "  <method-wrapper '__setattr__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__sizeof__', <function Dog.__sizeof__()>),\n",
-       " ('__str__', <method-wrapper '__str__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__subclasshook__', <function Dog.__subclasshook__>),\n",
-       " ('__weakref__', None),\n",
-       " ('name', 'Brian')]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class Dog:\n",
-    "    def __init__(self, name):\n",
-    "        self.name = name\n",
-    "        \n",
-    "d = Dog(name = \"Brian\")\n",
-    "getmembers(d)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The underscore is also used in python from a formatting perspective, in some cases to mean \"throwaway.\"  Below we are saying we don't care about 2, or the middle value.  the variable is technically declared but it's pythonic to see this as, it's not going to be relevant info to our program"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "x, _, y = (1, 2, 3)\n",
-    "print(x)\n",
-    "print(_)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "while the _ is being used as the iteration variable,  it's pythonic to mean we don't actually care about the values in the range(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n",
-      "6\n",
-      "7\n",
-      "8\n",
-      "9\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _ in range(10):\n",
-    "    print(_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for _ in range(10):\n",
-    "    # do something 10 times, but we don't  need the 1,2,3,4, etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "the same holds here, we as we are saying, we don't really care about what the function is returning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func():\n",
-    "    return 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_  = my_func()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "if you want to get rather in depth, you can look up name mangling in python, which is another use of underscores when making classes, but something outside the scope of this class"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## pass vs. continue"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "remember continue will throw us to the next iteration task of the current interation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---------------------------------------------------------------------------\n",
-      "First Loop:1\n",
-      "Outer loop iteration count:1\n",
-      "---------------------------------------------------------------------------\n",
-      "First Loop:[3, 4]\n",
-      "Second Loop:4\n",
-      "Outer loop iteration count:2\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,[3,4]]\n",
-    "c_outer = 0\n",
-    "\n",
-    "for i in a:\n",
-    "    \n",
-    "    # this just prints the first element of our list\n",
-    "    # initially the value is 1\n",
-    "    print(\"-\"*75) # divide up our outer loop printing\n",
-    "    print(\"First Loop:{}\".format(i))\n",
-    "    \n",
-    "    # since the first value of the list a is a number\n",
-    "    # this will be false and we will go to the else which is a pass\n",
-    "    # so we increment the counter and print the last statement \n",
-    "    # but when i = [3,4] we drop into the second loop\n",
-    "    if isinstance(i, list):\n",
-    "        \n",
-    "        # consider we are on element 2 where\n",
-    "        # i = [3,4]\n",
-    "        for i2 in i:\n",
-    "            # initially i2 is 3\n",
-    "            # so we continue\n",
-    "            # this will go back up to the line\n",
-    "            # for i2 in i (the current loop we are in)\n",
-    "            # and finish the iteration of [3,4]\n",
-    "            # notice that the continue here\n",
-    "            # doesen't put us back to for i in a\n",
-    "            # it puts us back to the current iteration loop\n",
-    "            # we can see this because c_outer is incremented and printed\n",
-    "            if i2 == 3:\n",
-    "                continue\n",
-    "            else:\n",
-    "                print(\"Second Loop:{}\".format(i2))\n",
-    "            \n",
-    "    # in this case it's a continue\n",
-    "    # so notice the next piece of the code is not executed\n",
-    "    else:\n",
-    "        pass\n",
-    "        \n",
-    "    c_outer+=1\n",
-    "    print(\"Outer loop iteration count:{}\".format(c_outer))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "if we change the bottom else to continue, we see for the first iteration loop, we skip the c_outer incrementing and skip the printing of that value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---------------------------------------------------------------------------\n",
-      "First Loop:1\n",
-      "---------------------------------------------------------------------------\n",
-      "First Loop:[3, 4]\n",
-      "Second Loop:4\n",
-      "Outer loop iteration count:1\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,[3,4]]\n",
-    "c_outer = 0\n",
-    "\n",
-    "for i in a:\n",
-    "    \n",
-    "    # this just prints the first element of our list\n",
-    "    # initially the value is 1\n",
-    "    print(\"-\"*75)\n",
-    "    print(\"First Loop:{}\".format(i))\n",
-    "    \n",
-    "    # since the first value of the list a is a number\n",
-    "    # this will be false and we will go to the else which is a pass\n",
-    "    # so we increment the counter and print the last statement \n",
-    "    # but when i = [3,4] we drop into the second loop\n",
-    "    if isinstance(i, list):\n",
-    "        \n",
-    "        # consider we are on element 2 where\n",
-    "        # i = [3,4]\n",
-    "        for i2 in i:\n",
-    "            # initially i2 is 3\n",
-    "            # so we continue\n",
-    "            # this will go back up to the line\n",
-    "            # for i2 in i\n",
-    "            # and finish the iteration of [3,4]\n",
-    "            # notice that the continue here\n",
-    "            # doesen't put us back to for i in a\n",
-    "            # it puts us back to the current iteration loop\n",
-    "            # most nested or lowest level\n",
-    "            if i2 == 3:\n",
-    "                continue\n",
-    "            else:\n",
-    "                print(\"Second Loop:{}\".format(i2))\n",
-    "            \n",
-    "    # in this case it's a continue\n",
-    "    # so notice the next piece of the code is not executed\n",
-    "    \n",
-    "    # if we change this to continue, when i initially is 1\n",
-    "    # the loop will shoot back up to for i in a and continue\n",
-    "    # missine the bottom  print and counter increment\n",
-    "    # so in this sence, we only partially complete the outer loop\n",
-    "    else:\n",
-    "        continue\n",
-    "        \n",
-    "    c_outer+=1\n",
-    "    print(\"Outer loop iteration count:{}\".format(c_outer))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "control flow of a loop can be tricky.  the best way to learn it will be to write some loops and print output so you can visually trace what is happening"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## quick overview of comprehension\n",
-    "these are just concise and speed efficient ways to right for loops"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst = []\n",
-    "for i in range(10):\n",
-    "    lst.append(i)\n",
-    "lst"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "notice to wording is basically the same, just ordered a bit differently and put on one line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# as a comprehension\n",
-    "lst = [i for i in range(10)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "we are compacking the for loop syntax into brackets (meaning we are using comprehension to make a list).  we could also use comprehensions to make a tuple or dictionary.  simply put, comprehensions let us compact our loop code and run more efficiently.  these will show up often as you continue to write and read python code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\n",
-      "{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10}\n",
-      "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# we can use other comprehensions with othere iterablees\n",
-    "# like tuples or lists\n",
-    "tup = tuple(i for i in range(10))\n",
-    "print(tup)\n",
-    "\n",
-    "dct = dict((idx,i+1) for idx,i in enumerate(range(10)))\n",
-    "print(dct)\n",
-    "\n",
-    "s = set(i for i in range(10))\n",
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "we can also apply functions with comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst = [-1,-2,-3,-4]\n",
-    "a = [abs(x) for x in lst]\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "these techniques above, will make up the majority of coding within assignment 1.  we will cover them far more in our second lecture, but for those that want to get started ahead of time, the above should help"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## map, comprehensions, for loops and time\n",
-    "* as we discussed runtime and concise code matters\n",
-    "* most likely, there is going to be runtime gains when using comprehensions and map vs. loops\n",
-    "* not to mention  you get the concise code with comprehension\n",
-    "* Once you learn comprehensions, you will not bother thinking about using map much, as comprehensions are rather simple and efficient to run\n",
-    "* plus, they are more pythonic\n",
-    "* but the concept of mapping will come up later on using the multiprocessing library so it is important to understand the idea of \"mapping\" a function to a collection of data\n",
-    "* the below prints out the seconds needed to run each chunk of code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.24928903579711914\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "start = time.time()\n",
-    "a = list(map(abs,lst))\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.2070269584655762\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "b = []\n",
-    "start = time.time()\n",
-    "for i in lst:\n",
-    "    b.append(abs(i))\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.4653120040893555\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "start = time.time()\n",
-    "for idx,i in enumerate(lst):\n",
-    "    lst[idx]= abs(i)\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7413120269775391\n"
-     ]
-    }
-   ],
-   "source": [
-    "# comprehension\n",
-    "start = time.time()\n",
-    "b = [abs(x) for x in list(range(10000000))]\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "with a udf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x):\n",
-    "    return abs(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8321022987365723\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "start = time.time()\n",
-    "a = list(map(my_func,lst))\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.619018793106079\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "b = []\n",
-    "start = time.time()\n",
-    "for i in lst:\n",
-    "    b.append(my_func(i))\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.9668538570404053\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "start = time.time()\n",
-    "for idx,i in enumerate(lst):\n",
-    "    lst[idx]= my_func(i)\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.2628250122070312\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "b = [my_func(x) for x in list(range(10000000))]\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "once you start working with larger datasets and writing more in depth programs, timing steps and keeping track of runtimes will be common.  as will be trying to find the most efficient way to write your code from a runtime and concise standpoint.  the above is a pretty simple example, but with a more complicated function or more complicated dataset, these functions may scale differently"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## dictionary searches\n",
-    "* dictionaries aren't generally used to do nested searching\n",
-    "* there is some nice functionality in pandas to do json_normalization, which will blow out the  json into  a dataframe, but we will ignore that for illustrative purposes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "doritos\n",
-      "lays\n",
-      "lays\n"
-     ]
-    }
-   ],
-   "source": [
-    "# if we know our keys, we might be making use of a dictionary like this, in a lookup fashion\n",
-    "lookup = {\n",
-    "    \"item_a\":\"doritos\",\n",
-    "    \"item_b\":\"lays\"\n",
-    "}\n",
-    "\n",
-    "for i in [\"item_a\", \"item_b\", \"item_b\"]:\n",
-    "    print(lookup[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "to do some custom searching we can use a for loop or we can use list comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dict = {\n",
-    "    \n",
-    "    1:{\"first_name\":\"Brian\"},\n",
-    "    2:{\"first_name\":\"Jane\", \"last_name\":\"Doe\"},\n",
-    "    3:{\"first_name\":\"John\", \"last_name\":\"Doe2\"}\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 {'first_name': 'Jane', 'last_name': 'Doe'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# this does let us search that nested dictionary\n",
-    "\n",
-    "for k,v in my_dict.items():\n",
-    "    # this is  saying if \"last_name\" is a key in our dictionary\n",
-    "    # which in this case, v is one of those nested dictionaries\n",
-    "    # and the last_name value is Doe, then print the k,v\n",
-    "    if \"last_name\" in v and v[\"last_name\"] == \"Doe\":     \n",
-    "        print(k,v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "in this case comprehensions aren't as concise, but we can solve this writting a custom UDF and then throwing it in a comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3]"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[k for k,v in my_dict.items()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'first_name': 'Brian'},\n",
-       " {'first_name': 'Jane', 'last_name': 'Doe'},\n",
-       " {'first_name': 'John', 'last_name': 'Doe2'}]"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[v for k,v in my_dict.items()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(k,v,search_term):\n",
-    "    if \"last_name\" in v and v[\"last_name\"] == search_term:\n",
-    "        return (k,v)\n",
-    "    else:\n",
-    "        return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[False, (2, {'first_name': 'Jane', 'last_name': 'Doe'}), False]"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search = \"Doe\"\n",
-    "[my_func(k,v,search) for k,v in my_dict.items()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(2, {'first_name': 'Jane', 'last_name': 'Doe'})]"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# the filter(None, iterable) filters False, None, empty items\n",
-    "search = \"Doe\"\n",
-    "list(filter(None, [my_func(k,v,search) for k,v in my_dict.items()]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/follow_up_questions/.ipynb_checkpoints/week 2-checkpoint.ipynb b/follow_up_questions/.ipynb_checkpoints/week 2-checkpoint.ipynb
deleted file mode 100644
index 2fd6442..0000000
--- a/follow_up_questions/.ipynb_checkpoints/week 2-checkpoint.ipynb	
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/follow_up_questions/Week 5.ipynb b/follow_up_questions/Week 5.ipynb
deleted file mode 100644
index 1cff6ec..0000000
--- a/follow_up_questions/Week 5.ipynb	
+++ /dev/null
@@ -1,2160 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Broadcasting\n",
-    "* duplicate a smaller array so we can add and subtract arrays that aren't the same size\n",
-    "* \"    The term broadcasting describes how numpy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes.\" - scipy\n",
-    "* NumPy does not actually duplicate the smaller array; instead, it makes memory and computationally efficient use of existing structures in memory that in effect achieve the same result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 3, 3, 3])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# one dimensional\n",
-    "array = np.array([2,2,2,2])\n",
-    "array + 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2],\n",
-       "       [2, 2]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([[1,1],[1,1]])\n",
-    "array + 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* The dimensions are considered in reverse order, starting with the trailing dimension; for example, looking at columns before rows in a two-dimensional case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1,3],\n",
-    "    [2,2,3]\n",
-    "])\n",
-    "\n",
-    "# we have 2 rows and 3 columns\n",
-    "print(array.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* consider a case where we have a 2 by 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2)\n",
-      "(1, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1],\n",
-    "    [2,2]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([1,5])\n",
-    "array1 = np.reshape(array1, (1,2))\n",
-    "print(array.shape)\n",
-    "print(array1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 6],\n",
-       "       [3, 7]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# each vector is added to each row\n",
-    "# since our second dimension aligns, it adds\n",
-    "# along the columns, because that is our second dimension\n",
-    "# think of it as \"hoping\" down the, like long, like a column\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 1],\n",
-       "       [2, 2]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 4])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# remember when we sum using axis = 1, we sum columns\n",
-    "# so when our axis 1 aligns above, we are hoping down the columns\n",
-    "# and broadcasting that vecotr\n",
-    "# in the below case, we are hoping down each column and adding them together\n",
-    "np.sum(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2)\n",
-      "(2, 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1],\n",
-    "    [2,2]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([1,5])\n",
-    "array1 = np.reshape(array1, (2,1))\n",
-    "print(array.shape)\n",
-    "print(array1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2],\n",
-       "       [7, 7]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# if we  flip the dimensions of array 1, the behavior changes\n",
-    "# now the first dimension (Rows aligns)\n",
-    "# we \"hop\" across and broadcast\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 1],\n",
-       "       [2, 2]])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 3])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# when we make use of our first axis here, to sum\n",
-    "# we sort of hop along each column and add them together\n",
-    "# similir to how we are broadings, we are hoping along\n",
-    "# each column and adding that vector\n",
-    "np.sum(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* can only be performed when the shape of each dimension in the arrays are equal or one has the dimension size of 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "operands could not be broadcast together with shapes (2,) (3,) ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-49-a46bbd09ccf2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0marray1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0marray\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,) (3,) "
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([1,2])\n",
-    "array1 = np.array([1,2,3])\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "operands could not be broadcast together with shapes (2,4) (2,2) ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-fa125fcfcab3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m ])\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0marray\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,4) (2,2) "
-     ]
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1,1,1],\n",
-    "    [1,1,1,1]\n",
-    "])\n",
-    "\n",
-    "# can't just  expand this out or double it\n",
-    "array1 = np.array([\n",
-    "    [1,1],\n",
-    "    [1,1]\n",
-    "])\n",
-    "\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2, 2, 2],\n",
-       "       [2, 2, 2, 2]])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1,1,1],\n",
-    "    [1,1,1,1]\n",
-    "])\n",
-    "\n",
-    "# can't just  expand this out or double it\n",
-    "array1 = np.array([\n",
-    "    [1,1],\n",
-    "    [1,1]\n",
-    "])\n",
-    "\n",
-    "# could reshape it though\n",
-    "array1 = np.reshape(array1, (1,4))\n",
-    "\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2, 2, 2],\n",
-       "       [2, 2, 2, 2]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,1,1,1],\n",
-    "    [1,1,1,1]\n",
-    "])\n",
-    "\n",
-    "# can't just  expand this out or double it\n",
-    "array1 = np.array([\n",
-    "    [1],\n",
-    "    [1]\n",
-    "])\n",
-    "\n",
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* play around with it"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Apply Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c\n",
-       "0  1  2  3\n",
-       "1  4  5  6"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = [\n",
-    "    [1,2,3],\n",
-    "    [4,5,6]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\", \"c\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    <class 'pandas.core.series.Series'>\n",
-       "b    <class 'pandas.core.series.Series'>\n",
-       "c    <class 'pandas.core.series.Series'>\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# when we do apply returns a series\n",
-    "l = lambda x: type(x)\n",
-    "df.apply(l, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1\n",
-       "b    2\n",
-       "c    3\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# remember this is a series where each series is a column of data\n",
-    "# so we can access elements of that series using bracket notation\n",
-    "# here we access the first element of each column\n",
-    "# think of this as slice 1\n",
-    "l = lambda x: x[0]\n",
-    "df.apply(l, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    4\n",
-       "b    5\n",
-       "c    6\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# and slice two\n",
-    "l = lambda x: x[1]\n",
-    "df.apply(l, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    <class 'pandas.core.series.Series'>\n",
-       "1    <class 'pandas.core.series.Series'>\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#  now with rows\n",
-    "l = lambda x: type(x)\n",
-    "df.apply(l, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    1\n",
-       "1    4\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l = lambda x: x[\"a\"]\n",
-    "df.apply(l, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    2\n",
-       "1    5\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l = lambda x: x[\"b\"]\n",
-    "df.apply(l, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>A</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>B</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c\n",
-       "0  1  2  A\n",
-       "1  4  5  B\n",
-       "2  1  2  C"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# let's see it in action\n",
-    "data = [\n",
-    "    [1,2,\"A\"],\n",
-    "    [4,5,\"B\"],\n",
-    "    [1,2,\"C\"]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\", \"c\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def f(x):\n",
-    "    if x == \"A\":\n",
-    "        return \"this is a\"\n",
-    "    elif x == \"B\":\n",
-    "        return \"this is b\"\n",
-    "    else:\n",
-    "        return \"this is c\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>new_col</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>A</td>\n",
-       "      <td>this is a</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>B</td>\n",
-       "      <td>this is b</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>this is c</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c    new_col\n",
-       "0  1  2  A  this is a\n",
-       "1  4  5  B  this is b\n",
-       "2  1  2  C  this is c"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# being applied to specific column\n",
-    "df[\"new_col\"] = df[\"c\"].apply(f)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    A x\n",
-       "1    B x\n",
-       "2    C x\n",
-       "Name: c, dtype: object"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l = lambda x: x + \" x\"\n",
-    "df[\"c\"].apply(l)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Piping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "0  1  2\n",
-       "1  4  5\n",
-       "2  1  2"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = [\n",
-    "    [1,2],\n",
-    "    [4,5],\n",
-    "    [1,2]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "0  1  2\n",
-       "1  4  5\n",
-       "2  1  2"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.pipe(lambda x: x + 2).pipe(lambda x: x - 5)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a   b\n",
-       "0  5  10\n",
-       "1  5  10\n",
-       "2  5  10"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# the first argument of pipe takes the \"data\" argument, which is just passing in the dataframe\n",
-    "# then we can think of the function as generally manipulating the dataframe\n",
-    "data = [\n",
-    "    [1,2],\n",
-    "    [4,5],\n",
-    "    [1,2]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\"]\n",
-    "\n",
-    "def fa(x):\n",
-    "    x[\"a\"] = 5\n",
-    "    return x\n",
-    "\n",
-    "def fb(x):\n",
-    "    x[\"b\"] = 10\n",
-    "    return x\n",
-    "    \n",
-    "    \n",
-    "df.pipe(fa).pipe(fb)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a   b\n",
-       "0  5  10\n",
-       "1  5  10\n",
-       "2  5  10"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = [\n",
-    "    [1,2],\n",
-    "    [4,5],\n",
-    "    [1,2]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"a\", \"b\"]\n",
-    "\n",
-    "df = fa(df)\n",
-    "df = fb(df)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pipe on single columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def f(x,i):\n",
-    "    return x-i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>-6</td>\n",
-       "      <td>-5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "0 -9 -8\n",
-       "1 -6 -5\n",
-       "2 -9 -8"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.pipe(f,i = 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Rolling Functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val</th>\n",
-       "      <th>id</th>\n",
-       "      <th>period</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   val id period\n",
-       "0    1  A      1\n",
-       "1    4  A      2\n",
-       "2    1  A      3\n",
-       "3    1  A      4\n",
-       "4    4  A      5\n",
-       "5    1  A      6"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# let's see it in action\n",
-    "data = [\n",
-    "    [1,\"A\",\"1\"],\n",
-    "    [4,\"A\",\"2\"],\n",
-    "    [1,\"A\",\"3\"],\n",
-    "    [1,\"A\",\"4\"],\n",
-    "    [4,\"A\",\"5\"],\n",
-    "    [1,\"A\",\"6\"]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"val\", \"id\", \"period\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    NaN\n",
-       "1    5.0\n",
-       "2    5.0\n",
-       "3    2.0\n",
-       "4    5.0\n",
-       "5    5.0\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(2).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    NaN\n",
-       "1    NaN\n",
-       "2    6.0\n",
-       "3    6.0\n",
-       "4    6.0\n",
-       "5    6.0\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(3).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    NaN\n",
-       "1    2.5\n",
-       "2    2.5\n",
-       "3    1.0\n",
-       "4    2.5\n",
-       "5    2.5\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(2).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     NaN\n",
-       "1     NaN\n",
-       "2     NaN\n",
-       "3    1.75\n",
-       "4    2.50\n",
-       "5    1.75\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(4).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    NaN\n",
-       "1    2.5\n",
-       "2    2.5\n",
-       "3    1.0\n",
-       "4    2.5\n",
-       "5    2.5\n",
-       "Name: val, dtype: float64"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"val\"].rolling(2).median()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"rolling_mean\"] = df[\"val\"].rolling(2).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val</th>\n",
-       "      <th>id</th>\n",
-       "      <th>period</th>\n",
-       "      <th>rolling_mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   val id period  rolling_mean\n",
-       "0    1  A      1           NaN\n",
-       "1    4  A      2           2.5\n",
-       "2    1  A      3           2.5\n",
-       "3    1  A      4           1.0\n",
-       "4    4  A      5           2.5\n",
-       "5    1  A      6           2.5"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "periods = [1,2,3,4]\n",
-    "for p in periods:\n",
-    "    name = \"rolling_mean_p_{}\".format(str(p))\n",
-    "    df[name] = df[\"val\"].rolling(p).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val</th>\n",
-       "      <th>id</th>\n",
-       "      <th>period</th>\n",
-       "      <th>rolling_mean</th>\n",
-       "      <th>rolling_mean_p_1</th>\n",
-       "      <th>rolling_mean_p_2</th>\n",
-       "      <th>rolling_mean_p_3</th>\n",
-       "      <th>rolling_mean_p_4</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.75</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   val id period  rolling_mean  rolling_mean_p_1  rolling_mean_p_2  \\\n",
-       "0    1  A      1           NaN               1.0               NaN   \n",
-       "1    4  A      2           2.5               4.0               2.5   \n",
-       "2    1  A      3           2.5               1.0               2.5   \n",
-       "3    1  A      4           1.0               1.0               1.0   \n",
-       "4    4  A      5           2.5               4.0               2.5   \n",
-       "5    1  A      6           2.5               1.0               2.5   \n",
-       "\n",
-       "   rolling_mean_p_3  rolling_mean_p_4  \n",
-       "0               NaN               NaN  \n",
-       "1               NaN               NaN  \n",
-       "2               2.0               NaN  \n",
-       "3               2.0              1.75  \n",
-       "4               2.0              2.50  \n",
-       "5               2.0              1.75  "
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Window Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val</th>\n",
-       "      <th>id</th>\n",
-       "      <th>period</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>11</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>15</td>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>8</td>\n",
-       "      <td>B</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>7</td>\n",
-       "      <td>B</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>11</td>\n",
-       "      <td>4</td>\n",
-       "      <td>B</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    val id period\n",
-       "0     1  A      1\n",
-       "1     4  A      2\n",
-       "2     1  A      3\n",
-       "3     1  A      4\n",
-       "4     4  A      5\n",
-       "5     1  A      6\n",
-       "6    11  B      1\n",
-       "7     4  B      2\n",
-       "8    15  B      3\n",
-       "9     8  B      4\n",
-       "10    7  B      5\n",
-       "11    4  B      6"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# let's see it in action\n",
-    "data = [\n",
-    "    [1,\"A\",\"1\"],\n",
-    "    [4,\"A\",\"2\"],\n",
-    "    [1,\"A\",\"3\"],\n",
-    "    [1,\"A\",\"4\"],\n",
-    "    [4,\"A\",\"5\"],\n",
-    "    [1,\"A\",\"6\"],\n",
-    "    [11,\"B\",\"1\"],\n",
-    "    [4,\"B\",\"2\"],\n",
-    "    [15,\"B\",\"3\"],\n",
-    "    [8,\"B\",\"4\"],\n",
-    "    [7,\"B\",\"5\"],\n",
-    "    [4,\"B\",\"6\"]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "df.columns = [\"val\", \"id\", \"period\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>level_1</th>\n",
-       "      <th>val</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>A</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>A</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>A</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>B</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>B</td>\n",
-       "      <td>7</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>B</td>\n",
-       "      <td>8</td>\n",
-       "      <td>19.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>B</td>\n",
-       "      <td>9</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>B</td>\n",
-       "      <td>10</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>11</td>\n",
-       "      <td>B</td>\n",
-       "      <td>11</td>\n",
-       "      <td>11.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id  level_1   val\n",
-       "0   A        0   NaN\n",
-       "1   A        1   5.0\n",
-       "2   A        2   5.0\n",
-       "3   A        3   2.0\n",
-       "4   A        4   5.0\n",
-       "5   A        5   5.0\n",
-       "6   B        6   NaN\n",
-       "7   B        7  15.0\n",
-       "8   B        8  19.0\n",
-       "9   B        9  23.0\n",
-       "10  B       10  15.0\n",
-       "11  B       11  11.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('id')['val'].rolling(2).sum().reset_index()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Time Series in Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# to add"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Raw Strings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello there \n",
-      "this will do a return\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"hello there \\nthis will do a return\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello there \\nthis will not do a return\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(r\"hello there \\nthis will not do a return\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\t this will tab\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\t this will tab\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\t this will not tab\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(r\"\\t this will not tab\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/follow_up_questions/week 1.ipynb b/follow_up_questions/week 1.ipynb
deleted file mode 100644
index c239cdb..0000000
--- a/follow_up_questions/week 1.ipynb	
+++ /dev/null
@@ -1,1040 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Underscores"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In truth we won't worry much about underscores, though I wanted to provide a bit of info on their use Python, specifically in relation to the double underscored attribuets and methods we saw running the dir() function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Double Underscore\n",
-    "* these refer to \"magic methods\", meaning python uses them behind the scenes and in most cases we don't generally want to alter or change how they function it.  think of them as python setting up some nuts and bolts to make your programs run\n",
-    "* for instance lists and integers (among other datatypes) can be added together using the + symbol or \\_\\_add\\_\\_.  Though using the symbol is far easier.  In this case, when we do list + list, the + sign tells python to call the \\_\\_add\\_\\_ method.\n",
-    "* \\_\\_init\\_\\_ is the initialization method for an object, which will will use, but not alter the functionality of\n",
-    "* \\_\\_iter\\_\\_ is used to return an iteration, so this is a method that iterators make use of\n",
-    "* \\_\\_new\\_\\_ is called to make a new object and used by the \\_\\_init\\_\\_ method\n",
-    "* for brevity I will leave it there, but these aren't something we'll need to be altering the functionality"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2]\n",
-    "b = [3,4]\n",
-    "a + b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.__add__(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "to the second point, there isn't a great way to tell the difference between methods and attributes in dir(), though we can make use of the getmembers function from inspect.  we can see that methods like extend, append etc. are listed as functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from inspect import getmembers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('__add__', <method-wrapper '__add__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__class__', list),\n",
-       " ('__contains__',\n",
-       "  <method-wrapper '__contains__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__delattr__',\n",
-       "  <method-wrapper '__delattr__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__delitem__',\n",
-       "  <method-wrapper '__delitem__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__dir__', <function list.__dir__()>),\n",
-       " ('__doc__',\n",
-       "  'Built-in mutable sequence.\\n\\nIf no argument is given, the constructor creates a new empty list.\\nThe argument must be an iterable if specified.'),\n",
-       " ('__eq__', <method-wrapper '__eq__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__format__', <function list.__format__(format_spec, /)>),\n",
-       " ('__ge__', <method-wrapper '__ge__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__getattribute__',\n",
-       "  <method-wrapper '__getattribute__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__getitem__', <function list.__getitem__>),\n",
-       " ('__gt__', <method-wrapper '__gt__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__hash__', None),\n",
-       " ('__iadd__', <method-wrapper '__iadd__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__imul__', <method-wrapper '__imul__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__init__', <method-wrapper '__init__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__init_subclass__', <function list.__init_subclass__>),\n",
-       " ('__iter__', <method-wrapper '__iter__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__le__', <method-wrapper '__le__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__len__', <method-wrapper '__len__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__lt__', <method-wrapper '__lt__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__mul__', <method-wrapper '__mul__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__ne__', <method-wrapper '__ne__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__new__', <function list.__new__(*args, **kwargs)>),\n",
-       " ('__reduce__', <function list.__reduce__()>),\n",
-       " ('__reduce_ex__', <function list.__reduce_ex__(protocol, /)>),\n",
-       " ('__repr__', <method-wrapper '__repr__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__reversed__', <function list.__reversed__()>),\n",
-       " ('__rmul__', <method-wrapper '__rmul__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__setattr__',\n",
-       "  <method-wrapper '__setattr__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__setitem__',\n",
-       "  <method-wrapper '__setitem__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__sizeof__', <function list.__sizeof__()>),\n",
-       " ('__str__', <method-wrapper '__str__' of list object at 0x7fe0b8c1f6e0>),\n",
-       " ('__subclasshook__', <function list.__subclasshook__>),\n",
-       " ('append', <function list.append(object, /)>),\n",
-       " ('clear', <function list.clear()>),\n",
-       " ('copy', <function list.copy()>),\n",
-       " ('count', <function list.count(value, /)>),\n",
-       " ('extend', <function list.extend(iterable, /)>),\n",
-       " ('index', <function list.index(value, start=0, stop=9223372036854775807, /)>),\n",
-       " ('insert', <function list.insert(index, object, /)>),\n",
-       " ('pop', <function list.pop(index=-1, /)>),\n",
-       " ('remove', <function list.remove(value, /)>),\n",
-       " ('reverse', <function list.reverse()>),\n",
-       " ('sort', <function list.sort(*, key=None, reverse=False)>)]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "getmembers(lst)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "if we make a quick dummy class (we'll cover this in lecture 3), and give it an attribuet of name, we can see the tuple of attribuet name and value using the getmembers function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('__class__', __main__.Dog),\n",
-       " ('__delattr__',\n",
-       "  <method-wrapper '__delattr__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__dict__', {'name': 'Brian'}),\n",
-       " ('__dir__', <function Dog.__dir__()>),\n",
-       " ('__doc__', None),\n",
-       " ('__eq__', <method-wrapper '__eq__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__format__', <function Dog.__format__(format_spec, /)>),\n",
-       " ('__ge__', <method-wrapper '__ge__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__getattribute__',\n",
-       "  <method-wrapper '__getattribute__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__gt__', <method-wrapper '__gt__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__hash__', <method-wrapper '__hash__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__init__',\n",
-       "  <bound method Dog.__init__ of <__main__.Dog object at 0x7fe08823d610>>),\n",
-       " ('__init_subclass__', <function Dog.__init_subclass__>),\n",
-       " ('__le__', <method-wrapper '__le__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__lt__', <method-wrapper '__lt__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__module__', '__main__'),\n",
-       " ('__ne__', <method-wrapper '__ne__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__new__', <function object.__new__(*args, **kwargs)>),\n",
-       " ('__reduce__', <function Dog.__reduce__()>),\n",
-       " ('__reduce_ex__', <function Dog.__reduce_ex__(protocol, /)>),\n",
-       " ('__repr__', <method-wrapper '__repr__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__setattr__',\n",
-       "  <method-wrapper '__setattr__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__sizeof__', <function Dog.__sizeof__()>),\n",
-       " ('__str__', <method-wrapper '__str__' of Dog object at 0x7fe08823d610>),\n",
-       " ('__subclasshook__', <function Dog.__subclasshook__>),\n",
-       " ('__weakref__', None),\n",
-       " ('name', 'Brian')]"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class Dog:\n",
-    "    def __init__(self, name):\n",
-    "        self.name = name\n",
-    "        \n",
-    "d = Dog(name = \"Brian\")\n",
-    "getmembers(d)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The underscore is also used in python from a formatting perspective, in some cases to mean \"throwaway.\"  Below we are saying we don't care about 2, or the middle value.  the variable is technically declared but it's pythonic to see this as, it's not going to be relevant info to our program"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "x, _, y = (1, 2, 3)\n",
-    "print(x)\n",
-    "print(_)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "while the _ is being used as the iteration variable,  it's pythonic to mean we don't actually care about the values in the range(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n",
-      "6\n",
-      "7\n",
-      "8\n",
-      "9\n"
-     ]
-    }
-   ],
-   "source": [
-    "for _ in range(10):\n",
-    "    print(_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for _ in range(10):\n",
-    "    # do something 10 times, but we don't  need the 1,2,3,4, etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "the same holds here, we as we are saying, we don't really care about what the function is returning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func():\n",
-    "    return 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_  = my_func()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "if you want to get rather in depth, you can look up name mangling in python, which is another use of underscores when making classes, but something outside the scope of this class"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## pass vs. continue"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "remember continue will throw us to the next iteration task of the current interation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---------------------------------------------------------------------------\n",
-      "First Loop:1\n",
-      "Outer loop iteration count:1\n",
-      "---------------------------------------------------------------------------\n",
-      "First Loop:[3, 4]\n",
-      "Second Loop:4\n",
-      "Outer loop iteration count:2\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,[3,4]]\n",
-    "c_outer = 0\n",
-    "\n",
-    "for i in a:\n",
-    "    \n",
-    "    # this just prints the first element of our list\n",
-    "    # initially the value is 1\n",
-    "    print(\"-\"*75) # divide up our outer loop printing\n",
-    "    print(\"First Loop:{}\".format(i))\n",
-    "    \n",
-    "    # since the first value of the list a is a number\n",
-    "    # this will be false and we will go to the else which is a pass\n",
-    "    # so we increment the counter and print the last statement \n",
-    "    # but when i = [3,4] we drop into the second loop\n",
-    "    if isinstance(i, list):\n",
-    "        \n",
-    "        # consider we are on element 2 where\n",
-    "        # i = [3,4]\n",
-    "        for i2 in i:\n",
-    "            # initially i2 is 3\n",
-    "            # so we continue\n",
-    "            # this will go back up to the line\n",
-    "            # for i2 in i (the current loop we are in)\n",
-    "            # and finish the iteration of [3,4]\n",
-    "            # notice that the continue here\n",
-    "            # doesen't put us back to for i in a\n",
-    "            # it puts us back to the current iteration loop\n",
-    "            # we can see this because c_outer is incremented and printed\n",
-    "            if i2 == 3:\n",
-    "                continue\n",
-    "            else:\n",
-    "                print(\"Second Loop:{}\".format(i2))\n",
-    "            \n",
-    "    # in this case it's a continue\n",
-    "    # so notice the next piece of the code is not executed\n",
-    "    else:\n",
-    "        pass\n",
-    "        \n",
-    "    c_outer+=1\n",
-    "    print(\"Outer loop iteration count:{}\".format(c_outer))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "if we change the bottom else to continue, we see for the first iteration loop, we skip the c_outer incrementing and skip the printing of that value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "---------------------------------------------------------------------------\n",
-      "First Loop:1\n",
-      "---------------------------------------------------------------------------\n",
-      "First Loop:[3, 4]\n",
-      "Second Loop:4\n",
-      "Outer loop iteration count:1\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,[3,4]]\n",
-    "c_outer = 0\n",
-    "\n",
-    "for i in a:\n",
-    "    \n",
-    "    # this just prints the first element of our list\n",
-    "    # initially the value is 1\n",
-    "    print(\"-\"*75)\n",
-    "    print(\"First Loop:{}\".format(i))\n",
-    "    \n",
-    "    # since the first value of the list a is a number\n",
-    "    # this will be false and we will go to the else which is a pass\n",
-    "    # so we increment the counter and print the last statement \n",
-    "    # but when i = [3,4] we drop into the second loop\n",
-    "    if isinstance(i, list):\n",
-    "        \n",
-    "        # consider we are on element 2 where\n",
-    "        # i = [3,4]\n",
-    "        for i2 in i:\n",
-    "            # initially i2 is 3\n",
-    "            # so we continue\n",
-    "            # this will go back up to the line\n",
-    "            # for i2 in i\n",
-    "            # and finish the iteration of [3,4]\n",
-    "            # notice that the continue here\n",
-    "            # doesen't put us back to for i in a\n",
-    "            # it puts us back to the current iteration loop\n",
-    "            # most nested or lowest level\n",
-    "            if i2 == 3:\n",
-    "                continue\n",
-    "            else:\n",
-    "                print(\"Second Loop:{}\".format(i2))\n",
-    "            \n",
-    "    # in this case it's a continue\n",
-    "    # so notice the next piece of the code is not executed\n",
-    "    \n",
-    "    # if we change this to continue, when i initially is 1\n",
-    "    # the loop will shoot back up to for i in a and continue\n",
-    "    # missine the bottom  print and counter increment\n",
-    "    # so in this sence, we only partially complete the outer loop\n",
-    "    else:\n",
-    "        continue\n",
-    "        \n",
-    "    c_outer+=1\n",
-    "    print(\"Outer loop iteration count:{}\".format(c_outer))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "control flow of a loop can be tricky.  the best way to learn it will be to write some loops and print output so you can visually trace what is happening"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## quick overview of comprehension\n",
-    "these are just concise and speed efficient ways to right for loops"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst = []\n",
-    "for i in range(10):\n",
-    "    lst.append(i)\n",
-    "lst"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "notice to wording is basically the same, just ordered a bit differently and put on one line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# as a comprehension\n",
-    "lst = [i for i in range(10)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "we are compacking the for loop syntax into brackets (meaning we are using comprehension to make a list).  we could also use comprehensions to make a tuple or dictionary.  simply put, comprehensions let us compact our loop code and run more efficiently.  these will show up often as you continue to write and read python code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\n",
-      "{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10}\n",
-      "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# we can use other comprehensions with othere iterablees\n",
-    "# like tuples or lists\n",
-    "tup = tuple(i for i in range(10))\n",
-    "print(tup)\n",
-    "\n",
-    "dct = dict((idx,i+1) for idx,i in enumerate(range(10)))\n",
-    "print(dct)\n",
-    "\n",
-    "s = set(i for i in range(10))\n",
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "we can also apply functions with comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst = [-1,-2,-3,-4]\n",
-    "a = [abs(x) for x in lst]\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "these techniques above, will make up the majority of coding within assignment 1.  we will cover them far more in our second lecture, but for those that want to get started ahead of time, the above should help"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## map, comprehensions, for loops and time\n",
-    "* as we discussed runtime and concise code matters\n",
-    "* most likely, there is going to be runtime gains when using comprehensions and map vs. loops\n",
-    "* not to mention  you get the concise code with comprehension\n",
-    "* Once you learn comprehensions, you will not bother thinking about using map much, as comprehensions are rather simple and efficient to run\n",
-    "* plus, they are more pythonic\n",
-    "* but the concept of mapping will come up later on using the multiprocessing library so it is important to understand the idea of \"mapping\" a function to a collection of data\n",
-    "* the below prints out the seconds needed to run each chunk of code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.24928903579711914\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "start = time.time()\n",
-    "a = list(map(abs,lst))\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.2070269584655762\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "b = []\n",
-    "start = time.time()\n",
-    "for i in lst:\n",
-    "    b.append(abs(i))\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.4653120040893555\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "start = time.time()\n",
-    "for idx,i in enumerate(lst):\n",
-    "    lst[idx]= abs(i)\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7413120269775391\n"
-     ]
-    }
-   ],
-   "source": [
-    "# comprehension\n",
-    "start = time.time()\n",
-    "b = [abs(x) for x in list(range(10000000))]\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "with a udf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x):\n",
-    "    return abs(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8321022987365723\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "start = time.time()\n",
-    "a = list(map(my_func,lst))\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.619018793106079\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "b = []\n",
-    "start = time.time()\n",
-    "for i in lst:\n",
-    "    b.append(my_func(i))\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.9668538570404053\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10000000))\n",
-    "start = time.time()\n",
-    "for idx,i in enumerate(lst):\n",
-    "    lst[idx]= my_func(i)\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.2628250122070312\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "b = [my_func(x) for x in list(range(10000000))]\n",
-    "end = time.time()\n",
-    "print(end - start)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "once you start working with larger datasets and writing more in depth programs, timing steps and keeping track of runtimes will be common.  as will be trying to find the most efficient way to write your code from a runtime and concise standpoint.  the above is a pretty simple example, but with a more complicated function or more complicated dataset, these functions may scale differently"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## dictionary searches\n",
-    "* dictionaries aren't generally used to do nested searching\n",
-    "* there is some nice functionality in pandas to do json_normalization, which will blow out the  json into  a dataframe, but we will ignore that for illustrative purposes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "doritos\n",
-      "lays\n",
-      "lays\n"
-     ]
-    }
-   ],
-   "source": [
-    "# if we know our keys, we might be making use of a dictionary like this, in a lookup fashion\n",
-    "lookup = {\n",
-    "    \"item_a\":\"doritos\",\n",
-    "    \"item_b\":\"lays\"\n",
-    "}\n",
-    "\n",
-    "for i in [\"item_a\", \"item_b\", \"item_b\"]:\n",
-    "    print(lookup[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "to do some custom searching we can use a for loop or we can use list comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dict = {\n",
-    "    \n",
-    "    1:{\"first_name\":\"Brian\"},\n",
-    "    2:{\"first_name\":\"Jane\", \"last_name\":\"Doe\"},\n",
-    "    3:{\"first_name\":\"John\", \"last_name\":\"Doe2\"}\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 {'first_name': 'Jane', 'last_name': 'Doe'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# this does let us search that nested dictionary\n",
-    "\n",
-    "for k,v in my_dict.items():\n",
-    "    # this is  saying if \"last_name\" is a key in our dictionary\n",
-    "    # which in this case, v is one of those nested dictionaries\n",
-    "    # and the last_name value is Doe, then print the k,v\n",
-    "    if \"last_name\" in v and v[\"last_name\"] == \"Doe\":     \n",
-    "        print(k,v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "in this case comprehensions aren't as concise, but we can solve this writting a custom UDF and then throwing it in a comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3]"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[k for k,v in my_dict.items()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'first_name': 'Brian'},\n",
-       " {'first_name': 'Jane', 'last_name': 'Doe'},\n",
-       " {'first_name': 'John', 'last_name': 'Doe2'}]"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[v for k,v in my_dict.items()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(k,v,search_term):\n",
-    "    if \"last_name\" in v and v[\"last_name\"] == search_term:\n",
-    "        return (k,v)\n",
-    "    else:\n",
-    "        return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[False, (2, {'first_name': 'Jane', 'last_name': 'Doe'}), False]"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search = \"Doe\"\n",
-    "[my_func(k,v,search) for k,v in my_dict.items()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(2, {'first_name': 'Jane', 'last_name': 'Doe'})]"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# the filter(None, iterable) filters False, None, empty items\n",
-    "search = \"Doe\"\n",
-    "list(filter(None, [my_func(k,v,search) for k,v in my_dict.items()]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/follow_up_questions/week 2.ipynb b/follow_up_questions/week 2.ipynb
deleted file mode 100644
index 4b6d2eb..0000000
--- a/follow_up_questions/week 2.ipynb	
+++ /dev/null
@@ -1,32 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/homework/.ipynb_checkpoints/Assignment 1-checkpoint.ipynb b/homework/.ipynb_checkpoints/Assignment 1-checkpoint.ipynb
deleted file mode 100644
index 86f6c2f..0000000
--- a/homework/.ipynb_checkpoints/Assignment 1-checkpoint.ipynb	
+++ /dev/null
@@ -1,491 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assignment 1\n",
-    "* Due 1/28 by 5pm\n",
-    "* Topics: containers, udfs, comprehensions, error handling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
-    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
-    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sample Question\n",
-    "* Create a UDF that adds 2 to a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add_func(x):\n",
-    "    return(x+2)\n",
-    "\n",
-    "number = add_func(a)\n",
-    "print(number)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 1\n",
-    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
-    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
-    "    * make use of zip and comprehension to make the dictionary\n",
-    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [5,7]\n",
-    "# b = [\"x\", \"z\"]\n",
-    "# return {5:\"x\", 7:\"z\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8]\n",
-    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 2\n",
-    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
-    "* The function should return 4 things"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 3\n",
-    "* Map a function to the below list that\n",
-    "    * returns the remainder of each number divided by 2 (51/2 = 25 with a remainder of 1, we want the 1 to be returned)\n",
-    "    * use lambdas for the mapping\n",
-    "    * hint, look at modulus (%) in python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst = list(range(25))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 4\n",
-    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
-    "* example below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lst = [1,2]\n",
-    "# dict = {1:\"a\", 2:\"b\"}\n",
-    "# new_lst = [\"a\",\"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = list(range(5))\n",
-    "dct = {\n",
-    "    0:\"a\",\n",
-    "    1:\"b\",\n",
-    "    2:\"c\",\n",
-    "    3:\"d\",\n",
-    "    4:\"e\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 5\n",
-    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
-    "* use comprehension to perform the squaring of each item\n",
-    "* hint, this should be doable in one line using args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_func(1,2,3)\n",
-    "    # do some stuff\n",
-    "    \n",
-    "# return_lst = [1,4,9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for this pass in the numberse 1,2,3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 6\n",
-    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [2,3]\n",
-    "# return_lst = [2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7]\n",
-    "b = [2,3,4,6,8,10,11,21]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 7\n",
-    "* write a function that takes a list of numbers and filters for odds only\n",
-    "* use the filter method and a lambda as the function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,2]\n",
-    "# new_lst = [1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 8\n",
-    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
-    "* Use the function to map it to a list comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,-2,1]\n",
-    "# using a comprehension return [3,4,3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,-2,1,8,-10,15,-12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 9\n",
-    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
-    "* note you may not use any third party modules.  use  comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [3,1]\n",
-    "# distance = ~2.23"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,1]\n",
-    "b = [3,1,2,4,3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 10\n",
-    "* write a function that takes a list, sorts it and finds the middle value.  \n",
-    "* use error handling to check the list is odd in length\n",
-    "* if it's even, return an error message\n",
-    "* feel free to use a try/except or assert\n",
-    "* show the output run on both lists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [4,6,1]\n",
-    "# sort = [1,4,6]\n",
-    "# middle val = 4\n",
-    "# if even number in size, return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
-    "b = [4,6,1,3,12,41,4,1,24,12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 11\n",
-    "* Make a generator that parses the list of transaction data\n",
-    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
-    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
-    "* make sure to skip the header somehow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# transactions = [A, item_a], [A, item_b]\n",
-    "# dict = {A:[item_a, item_b]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transactions = [\n",
-    "    [\"user_id|item_id\"],\n",
-    "    [\"A|item_a\"],\n",
-    "    [\"B|item_a\"],\n",
-    "    [\"C|item_a\"],\n",
-    "    [\"C|item_b\"],\n",
-    "    [\"C|item_c\"],\n",
-    "    [\"B|item_c\"],\n",
-    "    [\"D|item_b\"],\n",
-    "    [\"D|item_b\"]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 12\n",
-    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
-    "* Use error handling of some kind if the lists are not of equal length."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# should return [True, True, True, False, False]\n",
-    "a = [1,2,3,4,5]\n",
-    "b = [1,2,3,5,6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 13\n",
-    "Briefly explain the use of the following words in python:\n",
-    "* assert\n",
-    "* raise\n",
-    "* yield\n",
-    "* def\n",
-    "* lambda\n",
-    "* return\n",
-    "* map\n",
-    "* filter\n",
-    "* try\n",
-    "* except\n",
-    "* continue\n",
-    "* pass\n",
-    "* break\n",
-    "* while\n",
-    "* do (in the context of a while loop)\n",
-    "* for"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 14\n",
-    "Briefly describe the functionality of each:\n",
-    "* list\n",
-    "* tuple\n",
-    "* dictionary\n",
-    "* set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 15\n",
-    "* What two datatypes can be declared with { }?\n",
-    "* What two datatypes can be declared with ( )?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 16\n",
-    "Describe what a generator is and how it differs from a list or tuple."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/homework/.ipynb_checkpoints/Assignment 2-checkpoint.ipynb b/homework/.ipynb_checkpoints/Assignment 2-checkpoint.ipynb
deleted file mode 100644
index 46da27c..0000000
--- a/homework/.ipynb_checkpoints/Assignment 2-checkpoint.ipynb	
+++ /dev/null
@@ -1,257 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assignment 2\n",
-    "* Due: Tuesday 2/18/2020 by  5 PM \n",
-    "* Topics: pandas and numpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 1\n",
-    "* sklearn has the iris dataset built in\n",
-    "* this is what we will be working with\n",
-    "* put the iris data into a dataframe where we have 5 columns (the 4 features and the target)\n",
-    "* but the target shouldn't be numerical, it should be the actual label (so put setosa not 0 in the target column) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import load_iris\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "iris = load_iris()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 2\n",
-    "* using the data array from the iris variable (should be a numpy array of size 150x4), for each row and column, find the sum, min and max\n",
-    "* print out the results, but for the rows, show only the first 5 elements of the array and display the length (otherwise it will be large arrays since each row has a value)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 3\n",
-    "* write a function to return the multiplication of two matrices\n",
-    "* use some sort of error handling to make sure the dimensions are compatible\n",
-    "* use teh function to multiply the iris data (don't use the label column) by it's transpose\n",
-    "* what are the dimensions of the result\n",
-    "* put the sum of each row of the resulting matrix into a vector, printing out the length and first 5 elements.\n",
-    "* put the sum of each column of the resulting matrix in a vector and print it out."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 4\n",
-    "* create a subset of the iris dataframe that doesn't have the label column\n",
-    "* for each row, find the index of the min and max feature value\n",
-    "    * if we have a row with values [10,2,3,4] we want to return 1 for min and 0 for max, as these are the indicies that align to the min and max values\n",
-    "* the result should be two arrays of length 150 (one for each row) with the indices of the min and max value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 5\n",
-    "* Use the above two arrays to make a dataframe with 2 columns, the first being the feature of minimum value and the second the feature name of the maximum value\n",
-    "* note, don't have the cell values be the index value of the min/max value, have it be the feature name\n",
-    "    * a row should be [min_val = sepal width (cm), max_val = petal length (cm)]\n",
-    "* show the distributions for max and min features (how many times is each feature the max and min value for a row)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 6\n",
-    "* Describe a situation the functionality from question 4 and 5 could be of use?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 7\n",
-    "* sort the feature values for each row and replace the indices with the feature names\n",
-    "    * so each row will have 4 columns, the first column being the feature name that is the highest value, the second column being the feature name that is the second highest value, etc.\n",
-    "* note, watch out how argsort in numpy works.  you will need to reverse the order somehow.  the sorted(reverse = True) functionality might be of help.\n",
-    "* make sure to replace the index values with the feature name, as we did above\n",
-    "* put the resulting 2-d array into a pandas dataframe and print the first 5 rows.\n",
-    "* hint, look at the apply_along_axis() method for numpy arrays"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 8\n",
-    "* apply z-score normalization to each column in the iris data (note you do not need the target/label column)\n",
-    "* column wise meaning, treat each column as an array, and find the standard deviations and means of that column\n",
-    "* note, you can use zscore from scipy.stats\n",
-    "* results should be a pandas dataframe, printing out 5 rows and running the describe() method on the dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 9\n",
-    "* make a function that takes in a 2d numpy array X, a 1d numpy array Y and the size the training data, test data and valiation datasets.  \n",
-    "* return 6 items\n",
-    "    * train_x, train_y, test_x, test_y, val_x, val_y\n",
-    "* do not use any modules.  this can be solved using bracket notation to subset.  Note // will take care of decimals in doing division. you could also use int() to convert the float to a whole number\n",
-    "* make the params for the training, test and val size be decimals, that repsent percentages of the data.  For instance .8, .1, .1 means an 80% training, 10% test and 10% validation split\n",
-    "* use an assert to make sure the numbers add to 1\n",
-    "* print out the dimensions of all 6 elements, using iris as a test case with an 80-10-10 split"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 10\n",
-    "* using pandas, find the sum of each feature by species type (label)\n",
-    "* do the same, but find the min, max and median as well"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 11\n",
-    "* mean center each column of the iris dataframe (excluding the label column of course)\n",
-    "* this means, for each column, the mean should be zero.  to accomplish this we can subtract the column mean from each element of the column\n",
-    "* note, broadcasting, which if we have say a 150 row and 4 column dataframe, can  take a row vector of size 4 and apply it to each element\n",
-    "*  thus, we can answer this questions doing something like df - df_col_means\n",
-    "* run the describe() method at the end to show the data has been mean center"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quesiton 12\n",
-    "* Explain what the axis mean in regards to a pandas dataframe and numpy array\n",
-    "* How would you groupby two columns in pandas?\n",
-    "* What functions are used to read in csvs and excel files in pandas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 13\n",
-    "* using Pandas, subset the dataset for only species setosa and petal length > 1.3\n",
-    "* sum the feature columns and display the results for each (excluding the label column)\n",
-    "* do the same, but change the and to an or, and repeat the same calculation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 14\n",
-    "* write a lambda that subtracts 2 from all elements of a pandas dataframe\n",
-    "* display the top 5 rows\n",
-    "* use the same lambda,  but applying it to the sepal length  column only\n",
-    "* so the output should be the top 5 rows of a dataframe where all cells have had 2 subtracted and then another dataframe where only sepal length has 2 subtracted from it"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 15\n",
-    "* normalize each column of pandas dataframe to 0-1 scale.\n",
-    "* hint\n",
-    "    * 0-1 is done by using (X - xmin)/(xmax - xmin)\n",
-    "    * hint, the approach should be similar to when we mean centered the dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 16\n",
-    "* assume the below 2 matrices, the first is observations and the second is cluster centers\n",
-    "* using cdist from scipy, create a matrix where the rows represent our 3 observations and the columns represent our 2 clusters and the cells values are the euclidean distances between a given observation and cluster\n",
-    "* when would this be of use?  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "observations = np.array([\n",
-    "    [1,2,3],\n",
-    "    [4,3,1],\n",
-    "    [2,3,4]\n",
-    "])\n",
-    "\n",
-    "cluster_centers = np.array([\n",
-    "    [2,3,1],\n",
-    "    [2,1,3]\n",
-    "])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/homework/Assignment 1 Answers.ipynb b/homework/Assignment 1 Answers.ipynb
deleted file mode 100644
index f1ebaaf..0000000
--- a/homework/Assignment 1 Answers.ipynb	
+++ /dev/null
@@ -1,1312 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* Question 1-12: 7 points each (84 points)\n",
-    "* Question 13-16: 4 points each (16 points)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pointer\n",
-    "# use copy module"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* https://docs.python.org/2/library/copy.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import copy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 2\n",
-    "y = x\n",
-    "print(x)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 3\n",
-    "print(x)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "y = 3\n",
-    "print(x)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "don't use if else for error handling, use try except with raise or assert"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "error\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 15\n",
-    "if x > 10:\n",
-    "    print(\"error\")\n",
-    "else:\n",
-    "    print(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "RuntimeError",
-     "evalue": "No active exception to reraise",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-4c0012669ff1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: No active exception to reraise"
-     ]
-    }
-   ],
-   "source": [
-    "x = 15\n",
-    "if x > 10:\n",
-    "    raise\n",
-    "else:\n",
-    "    print(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    print(x)\n",
-    "except:\n",
-    "    raise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.4142135623730951\n",
-      "1.4142135623730951\n"
-     ]
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "def f(x):\n",
-    "    print(math.sqrt(x))\n",
-    "\n",
-    "f(2)\n",
-    "print(math.sqrt(2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.4142135623730951\n"
-     ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'math' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-4ecc084d5cbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'math' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "def f(x):\n",
-    "    import math\n",
-    "    print(math.sqrt(x))\n",
-    "\n",
-    "f(2)\n",
-    "print(math.sqrt(2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 1\n",
-    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
-    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
-    "    * make use of zip and comprehension to make the dictionary\n",
-    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8]\n",
-    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def my_func(lst_a, lst_b):\n",
-    "    assert len(lst_a) == len(lst_b), \"lists are not equal length\"\n",
-    "    # this works also\n",
-    "    return dict(zip(lst_a,lst_b))\n",
-    "    return dict(a for a in zip(lst_a, lst_b))\n",
-    "    return {k:v for k,v in zip(a,b)}\n",
-    "    return dict((x[0],x[1]) for x in zip(lst_a, lst_b))\n",
-    "    # this is wrong why?\n",
-    "    return {(x[0],x[1]) for x in zip(lst_a, lst_b)}\n",
-    "\n",
-    "test = my_func(a,b)\n",
-    "test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 2\n",
-    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
-    "* The function should return 4 things"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import permutations, combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(lst):\n",
-    "    perms = list(permutations(lst, 2))\n",
-    "    combos = list(combinations(lst, 2))\n",
-    "    return len(perms), len(combos), perms, combos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# in the future we want our functions returning something for homework\n",
-    "def my_func(lst):\n",
-    "    perms = list(permutations(lst, 2))\n",
-    "    combos = list(combinations(lst, 2))\n",
-    "    print len(perms), len(combos), perms, combos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Permutation Count:20\n",
-      "Combination Count:10\n",
-      "Permutations:[(1, 2), (1, 3), (1, 4), (1, 5), (2, 1), (2, 3), (2, 4), (2, 5), (3, 1), (3, 2), (3, 4), (3, 5), (4, 1), (4, 2), (4, 3), (4, 5), (5, 1), (5, 2), (5, 3), (5, 4)]\n",
-      "Combinations:[(1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5), (4, 5)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "len_perms, len_combos, perms, combos = my_func(a)\n",
-    "\n",
-    "print(\"Permutation Count:{}\".format(len_perms))\n",
-    "print(\"Combination Count:{}\".format(len_combos))\n",
-    "print(\"Permutations:{}\".format(perms))\n",
-    "print(\"Combinations:{}\".format(combos))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 3\n",
-    "* Map a function to the below list that\n",
-    "    * returns the remainder of each number divided by 2 (51/2 = 25 with a remainder of 1, we want the 1 to be returned)\n",
-    "    * use lambdas for the mapping\n",
-    "    * hint, look at modulus (%) in python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst = list(range(25))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = list(map(lambda x: x%2, lst))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Value:0   Remainder:0\n",
-      "Value:1   Remainder:1\n",
-      "Value:2   Remainder:0\n",
-      "Value:3   Remainder:1\n",
-      "Value:4   Remainder:0\n",
-      "Value:5   Remainder:1\n",
-      "Value:6   Remainder:0\n",
-      "Value:7   Remainder:1\n",
-      "Value:8   Remainder:0\n",
-      "Value:9   Remainder:1\n",
-      "Value:10   Remainder:0\n",
-      "Value:11   Remainder:1\n",
-      "Value:12   Remainder:0\n",
-      "Value:13   Remainder:1\n",
-      "Value:14   Remainder:0\n",
-      "Value:15   Remainder:1\n",
-      "Value:16   Remainder:0\n",
-      "Value:17   Remainder:1\n",
-      "Value:18   Remainder:0\n",
-      "Value:19   Remainder:1\n",
-      "Value:20   Remainder:0\n",
-      "Value:21   Remainder:1\n",
-      "Value:22   Remainder:0\n",
-      "Value:23   Remainder:1\n",
-      "Value:24   Remainder:0\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(lst, a):\n",
-    "    print(\"Value:{}   Remainder:{}\".format(i[0], i[1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 4\n",
-    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
-    "* example below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = list(range(5))\n",
-    "dct = {\n",
-    "    0:\"a\",\n",
-    "    1:\"b\",\n",
-    "    2:\"e\",\n",
-    "    3:\"e\",\n",
-    "    4:\"e\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'e', 'e', 'e']"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = [dct[x] for x in a]\n",
-    "b = [x for x in dct.values()]\n",
-    "b = [dct.get(x) for x in dct.keys()]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Old Val:0   New Val:a\n",
-      "Old Val:1   New Val:b\n",
-      "Old Val:2   New Val:c\n",
-      "Old Val:3   New Val:d\n",
-      "Old Val:4   New Val:e\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(\"Old Val:{}   New Val:{}\".format(i[0], i[1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 5\n",
-    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
-    "* use comprehension to perform the squaring of each item\n",
-    "* hint, this should be doable in one line using args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(*args):\n",
-    "    return [x**2 for x in args]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Num:1   Num Squared:1\n",
-      "Num:2   Num Squared:4\n",
-      "Num:3   Num Squared:9\n",
-      "Num:4   Num Squared:16\n",
-      "Num:5   Num Squared:25\n"
-     ]
-    }
-   ],
-   "source": [
-    "b = my_func(1,2,3,4,5)\n",
-    "for i in zip([1,2,3,4,5],b):\n",
-    "    print(\"Num:{}   Num Squared:{}\".format(i[0], i[1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 6\n",
-    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7]\n",
-    "b = [2,3,4,6,8,10,11,21]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(a,b):\n",
-    "    v1 = set(a) & set(b)\n",
-    "    v2 = [i for i in a if i in b]\n",
-    "    v3 = set(a).intersection(b)\n",
-    "    return v1,v2,v3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{2, 3, 4, 6}\n",
-      "[2, 3, 4, 6]\n",
-      "{2, 3, 4, 6}\n"
-     ]
-    }
-   ],
-   "source": [
-    "v1,v2,v3 = my_func(a,b)\n",
-    "print(v1)\n",
-    "print(v2)\n",
-    "print(v3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 7\n",
-    "* write a function that takes a list of numbers and filters for odds only\n",
-    "* use the filter method and a lambda as the function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 3, 5, 7, 9, 11, 13, 15]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = list(filter(lambda x: x if x % 2 == 1 else False, a))\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 3, 5, 7, 9, 11, 13, 15]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(lambda x: x % 2, a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 8\n",
-    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
-    "* Use the function to map it to a list comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,-2,1,8,-10,15,-12]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, -2, 1, 8, -10, 15, -12]\n",
-      "[3, 4, 3, 10, 12, 17, 14]\n"
-     ]
-    }
-   ],
-   "source": [
-    "b = list(map(lambda x: abs(x) + 2, a))\n",
-    "print(a)\n",
-    "print(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 9\n",
-    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
-    "* note you may not use any third party modules.  use  comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [3,1]\n",
-    "# distance = ~2.23\n",
-    "\n",
-    "a = [1,2,3,4,1]\n",
-    "b = [3,1,2,4,3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_euclidean(a,b):\n",
-    "    return sum([(x[0]-x[1])**2 for x in zip(a,b)])**(1/2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.23606797749979\n"
-     ]
-    }
-   ],
-   "source": [
-    "dist = my_euclidean([1,2], [3,1])\n",
-    "print(dist)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.23606797749979\n"
-     ]
-    }
-   ],
-   "source": [
-    "dist = my_euclidean([1,4,1], [2,4,3])\n",
-    "print(dist)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.1622776601683795\n"
-     ]
-    }
-   ],
-   "source": [
-    "dist = my_euclidean(a,b)\n",
-    "print(dist)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff = lambda zipped: sum([(x[0]-x[1])**2 for x in zipped])**(1/2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.1622776601683795"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff(zip(a,b))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.1622776601683795"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(map(lambda x: (x[0]-x[1])**2, zip(a,b)))**(1/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 10\n",
-    "* write a function that takes a list, sorts it and finds the middle value.  \n",
-    "* use error handling to check the list is odd in length\n",
-    "* if it's even, return an error message\n",
-    "* feel free to use a try/except or assert\n",
-    "* show the output run on both lists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
-    "b = [4,6,1,3,12,41,4,1,24,12]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(lst):\n",
-    "    assert len(lst)%2 == 1, \"list is even in length\"\n",
-    "    return sorted(lst)[int(len(lst)/2)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# why wouldn't we want to use this one\n",
-    "def my_func(lst):\n",
-    "    sort_x = sorted(lst)\n",
-    "    assert len(lst)%2 == 1, \"list is even in length\"\n",
-    "    return sort_x[int(len(lst)/2)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tst_a = my_func(a)\n",
-    "tst_a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "list is even in length",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-159-88a4e2992475>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtst_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtst_b\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-156-ca744f5a6669>\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m(lst)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"list is even in length\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAssertionError\u001b[0m: list is even in length"
-     ]
-    }
-   ],
-   "source": [
-    "tst_b = my_func(b)\n",
-    "tst_b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Quesiton 11\n",
-    "* Make a generator that parses the list of transaction data\n",
-    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
-    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
-    "* make sure to skip the header somehow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transactions = [\n",
-    "    [\"user_id|item_id\"],\n",
-    "    [\"A|item_a\"],\n",
-    "    [\"B|item_a\"],\n",
-    "    [\"C|item_a\"],\n",
-    "    [\"C|item_b\"],\n",
-    "    [\"C|item_c\"],\n",
-    "    [\"B|item_c\"],\n",
-    "    [\"D|item_b\"],\n",
-    "    [\"D|item_b\"]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "user_id\n",
-      "A\n",
-      "B\n",
-      "C\n",
-      "C\n",
-      "C\n",
-      "B\n",
-      "D\n",
-      "D\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in transactions:\n",
-    "    print(i[0].split(\"|\")[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dct = {}\n",
-    "gen = (x[0].split(\"|\") for idx,x in enumerate(transactions) if idx != 0)\n",
-    "\n",
-    "del transactions # this would allow me to be memory efficient\n",
-    "\n",
-    "for item in gen:\n",
-    "    if item[0] in dct:\n",
-    "        dct[item[0]].append(item[1])\n",
-    "    else:\n",
-    "        dct[item[0]] = [item[1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': ['item_a'],\n",
-       " 'B': ['item_a', 'item_c'],\n",
-       " 'C': ['item_a', 'item_b', 'item_c'],\n",
-       " 'D': ['item_b', 'item_b']}"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': ['item_a'],\n",
-       " 'B': ['item_a', 'item_c'],\n",
-       " 'C': ['item_a', 'item_b', 'item_c'],\n",
-       " 'D': ['item_b', 'item_b']}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# why is this no good?\n",
-    "dct = {}\n",
-    "gen = (x[0] for idx,x in enumerate(transactions) if idx != 0)\n",
-    "gen = [x.split(\"|\") for x in gen]\n",
-    "\n",
-    "\n",
-    "for item in gen:\n",
-    "    if item[0] in dct:\n",
-    "        dct[item[0]].append(item[1])\n",
-    "    else:\n",
-    "        dct[item[0]] = [item[1]]\n",
-    "        \n",
-    "dct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fdab819a0d0>"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = (i[0].rstrip().split(\"|\") for i in transactions[1:])\n",
-    "dct = {}\n",
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, None, None, None, None, None, None, None]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[dct.setdefault(i[0], []).append(i[1]) for i in t]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': ['item_a'],\n",
-       " 'B': ['item_a', 'item_c'],\n",
-       " 'C': ['item_a', 'item_b', 'item_c'],\n",
-       " 'D': ['item_b', 'item_b']}"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 12\n",
-    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
-    "* Use error handling of some kind if the lists are not of equal length."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# should return [True, True, True, False, False]\n",
-    "a = [1,2,3,4,5]\n",
-    "b = [1,2,3,5,6]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(a,b):\n",
-    "    assert len(a) == len(b), \"lists are not of the same length\"\n",
-    "    return [True if x[0] == x[1] else False for x in zip(a,b)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c = my_func(a,b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[True, True, True, False, False]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 13\n",
-    "Briefly explain the use of the following words in python:\n",
-    "* assert\n",
-    "* raise\n",
-    "* yield\n",
-    "* def\n",
-    "* lambda\n",
-    "* return\n",
-    "* map\n",
-    "* filter\n",
-    "* try\n",
-    "* except\n",
-    "* continue\n",
-    "* pass\n",
-    "* break\n",
-    "* while\n",
-    "* do (in the context of a while loop)\n",
-    "* for"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* assert: used to check for a condition, if the condition isn't met, return an error message\n",
-    "* raise: allows for an exception to be thrown\n",
-    "* yield: used to return an item from a generator\n",
-    "* def: declare a function\n",
-    "* lambda: declare an annonymous function\n",
-    "* return: send a value back from a function\n",
-    "* map: apply a function to elements of an iterable\n",
-    "* filter: apply a function that can filter an itereablee\n",
-    "* try: indicatese a try/except block, the try portion is executed, if an error is caught, it goes to the except block\n",
-    "* except: if the try block of code can't be executed, the except block is executed instead.\n",
-    "* continue: goes to the next iteration of the loop\n",
-    "* pass: do nothing when condition is met\n",
-    "* break: end the loop if a condition is net\n",
-    "* while: iteration technique, continues until a condition is met\n",
-    "* used in a do-while loop, the code block is executed before the while condition is evaluated.  do x whil"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 14\n",
-    "Briefly describe the below:\n",
-    "* list\n",
-    "* tuple\n",
-    "* dictionary\n",
-    "* set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* list: a mutable collection of items\n",
-    "* tuple: an immutable collection of items\n",
-    "* dictionary: key,value pairs\n",
-    "* set: unique collection of items"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 15\n",
-    "* What two datatypes can be declared with { }?\n",
-    "* What two datatypes can be declared with ( )?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* set and dictionary\n",
-    "* generator and tuple"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 16\n",
-    "Describe what a generator is and how it differs from a list or tuple."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* generators store information on the state of an iterable and return a portion at a time.  not everything is stored in memory, like a list of tuple, which can be useful if you're dealing with a large group of data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/homework/Assignment 1.ipynb b/homework/Assignment 1.ipynb
deleted file mode 100644
index 4e9a4c7..0000000
--- a/homework/Assignment 1.ipynb	
+++ /dev/null
@@ -1,633 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assignment 1\n",
-    "* Due 1/28 by 5pm\n",
-    "* Topics: containers, udfs, comprehensions, error handling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
-    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
-    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sample Question\n",
-    "* Create a UDF that adds 2 to a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add_func(x):\n",
-    "    return(x+2)\n",
-    "\n",
-    "number = add_func(a)\n",
-    "print(number)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 1\n",
-    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
-    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
-    "    * make use of zip and comprehension to make the dictionary\n",
-    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [5,7]\n",
-    "# b = [\"x\", \"z\"]\n",
-    "# return {5:\"x\", 7:\"z\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8]\n",
-    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 2\n",
-    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
-    "* The function should return 4 things"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 3\n",
-    "* Map a function to the below list that\n",
-    "    * returns the remainder of each number divided by 2 (51/2 = 25 with a remainder of 1, we want the 1 to be returned)\n",
-    "    * use lambdas for the mapping\n",
-    "    * hint, look at modulus (%) in python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst = list(range(25))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 4\n",
-    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
-    "* example below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lst = [1,2]\n",
-    "# dict = {1:\"a\", 2:\"b\"}\n",
-    "# new_lst = [\"a\",\"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = list(range(5))\n",
-    "dct = {\n",
-    "    0:\"a\",\n",
-    "    1:\"b\",\n",
-    "    2:\"c\",\n",
-    "    3:\"d\",\n",
-    "    4:\"e\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 5\n",
-    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
-    "* use comprehension to perform the squaring of each item\n",
-    "* hint, this should be doable in one line using args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_func(1,2,3)\n",
-    "    # do some stuff\n",
-    "    \n",
-    "# return_lst = [1,4,9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for this pass in the numberse 1,2,3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 6\n",
-    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [2,3]\n",
-    "# return_lst = [2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7]\n",
-    "b = [2,3,4,6,8,10,11,21]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 7\n",
-    "* write a function that takes a list of numbers and filters for odds only\n",
-    "* use the filter method and a lambda as the function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,2]\n",
-    "# new_lst = [1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 8\n",
-    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
-    "* Use the function to map it to a list comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,-2,1]\n",
-    "# using a comprehension return [3,4,3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,-2,1,8,-10,15,-12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 9\n",
-    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
-    "* note you may not use any third party modules.  use  comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [3,1]\n",
-    "# distance = ~2.23"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,1]\n",
-    "b = [3,1,2,4,3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 10\n",
-    "* write a function that takes a list, sorts it and finds the middle value.  \n",
-    "* use error handling to check the list is odd in length\n",
-    "* if it's even, return an error message\n",
-    "* feel free to use a try/except or assert\n",
-    "* show the output run on both lists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [4,6,1]\n",
-    "# sort = [1,4,6]\n",
-    "# middle val = 4\n",
-    "# if even number in size, return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
-    "b = [4,6,1,3,12,41,4,1,24,12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 11\n",
-    "* Make a generator that parses the list of transaction data\n",
-    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
-    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
-    "* make sure to skip the header somehow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# transactions = [A, item_a], [A, item_b]\n",
-    "# dict = {A:[item_a, item_b]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transactions = [\n",
-    "    [\"user_id|item_id\"],\n",
-    "    [\"A|item_a\"],\n",
-    "    [\"B|item_a\"],\n",
-    "    [\"C|item_a\"],\n",
-    "    [\"C|item_b\"],\n",
-    "    [\"C|item_c\"],\n",
-    "    [\"B|item_c\"],\n",
-    "    [\"D|item_b\"],\n",
-    "    [\"D|item_b\"]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['D', 'item_b']"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"D|item_b\".split(\"|\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['A', 'item_a']\n",
-      "['B', 'item_a']\n",
-      "['C', 'item_a']\n",
-      "['C', 'item_b']\n",
-      "['C', 'item_c']\n",
-      "['B', 'item_c']\n",
-      "['D', 'item_b']\n",
-      "['D', 'item_b']\n"
-     ]
-    }
-   ],
-   "source": [
-    "dct = {}\n",
-    "for idx,i in enumerate(transactions):\n",
-    "    \n",
-    "    if idx != 0:\n",
-    "        tmp_lst = i[0].split(\"|\")\n",
-    "        # tmp_lst = [a,item_b] it's list\n",
-    "        \n",
-    "        \n",
-    "        \n",
-    "        #if tmp_lst[0] not in dct:\n",
-    "        #    dct[tmp_lst[0]] = [tmp_lst[1]]\n",
-    "        #else:\n",
-    "        #    dct[tmp_lst[0]].append(tmp_lst[1])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fe338260650>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct = {}\n",
-    "gen = (x[0].split(\"|\") for idx,x in enumerate(transactions) if idx != 0)\n",
-    "gen"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['A', 'item_a']"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['user_id', 'item_id']\n",
-      "['A', 'item_a']\n",
-      "['B', 'item_a']\n",
-      "['C', 'item_a']\n",
-      "['C', 'item_b']\n",
-      "['C', 'item_c']\n",
-      "['B', 'item_c']\n",
-      "['D', 'item_b']\n",
-      "['D', 'item_b']\n"
-     ]
-    }
-   ],
-   "source": [
-    "for item in gen:\n",
-    "    if item[0] in dct:\n",
-    "        dct[item[0]].append(item[1])\n",
-    "    else:\n",
-    "        dct[item[0]] = [item[1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 12\n",
-    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
-    "* Use error handling of some kind if the lists are not of equal length."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# should return [True, True, True, False, False]\n",
-    "a = [1,2,3,4,5]\n",
-    "b = [1,2,3,5,6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 13\n",
-    "Briefly explain the use of the following words in python:\n",
-    "* assert\n",
-    "* raise\n",
-    "* yield\n",
-    "* def\n",
-    "* lambda\n",
-    "* return\n",
-    "* map\n",
-    "* filter\n",
-    "* try\n",
-    "* except\n",
-    "* continue\n",
-    "* pass\n",
-    "* break\n",
-    "* while\n",
-    "* do (in the context of a while loop)\n",
-    "* for"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 14\n",
-    "Briefly describe the functionality of each:\n",
-    "* list\n",
-    "* tuple\n",
-    "* dictionary\n",
-    "* set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 15\n",
-    "* What two datatypes can be declared with { }?\n",
-    "* What two datatypes can be declared with ( )?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 16\n",
-    "Describe what a generator is and how it differs from a list or tuple."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/homework/Assignment 2.ipynb b/homework/Assignment 2.ipynb
deleted file mode 100644
index 02e5f80..0000000
--- a/homework/Assignment 2.ipynb	
+++ /dev/null
@@ -1,257 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assignment 2\n",
-    "* Due: Tuesday 2/18/2020 by  5 PM \n",
-    "* Topics: pandas and numpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 1\n",
-    "* sklearn has the iris dataset built in\n",
-    "* this is what we will be working with\n",
-    "* put the iris data into a dataframe where we have 5 columns (the 4 features and the target)\n",
-    "* but the target shouldn't be numerical, it should be the actual label (so put setosa not 0 in the target column) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import load_iris\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "iris = load_iris()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 2\n",
-    "* using the data array from the iris variable (should be a numpy array of size 150x4), for each row and column, find the sum, min and max\n",
-    "* print out the results, but for the rows, show only the first 5 elements of the array and display the length (otherwise it will be large arrays since each row has a value)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 3\n",
-    "* write a function to return the multiplication of two matrices\n",
-    "* use some sort of error handling to make sure the dimensions are compatible\n",
-    "* use teh function to multiply the iris data (don't use the label column) by it's transpose\n",
-    "* what are the dimensions of the result\n",
-    "* put the sum of each row of the resulting matrix into a vector, printing out the length and first 5 elements.\n",
-    "* put the sum of each column of the resulting matrix in a vector and print it out."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 4\n",
-    "* create a subset of the iris dataframe that doesn't have the label column\n",
-    "* for each row, find the index of the min and max feature value\n",
-    "    * if we have a row with values [10,2,3,4] we want to return 1 for min and 0 for max, as these are the indicies that align to the min and max values\n",
-    "* the result should be two arrays of length 150 (one for each row) with the indices of the min and max value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 5\n",
-    "* Use the above two arrays to make a dataframe with 2 columns, the first being the feature of minimum value and the second the feature name of the maximum value\n",
-    "* note, don't have the cell values be the index value of the min/max value, have it be the feature name\n",
-    "    * a row should be [min_val = sepal width (cm), max_val = petal length (cm)]\n",
-    "* show the distributions for max and min features (how many times is each feature the max and min value for a row)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 6\n",
-    "* Describe a situation the functionality from question 4 and 5 could be of use?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 7\n",
-    "* sort the feature values for each row and replace the indices with the feature names\n",
-    "    * so each row will have 4 columns, the first column being the feature name that is the highest value, the second column being the feature name that is the second highest value, etc.\n",
-    "* note, watch out how argsort in numpy works.  you will need to reverse the order somehow.  the sorted(reverse = True) functionality might be of help.\n",
-    "* make sure to replace the index values with the feature name, as we did above\n",
-    "* put the resulting 2-d array into a pandas dataframe and print the first 5 rows.\n",
-    "* hint, look at the apply_along_axis() method for numpy arrays"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 8\n",
-    "* apply z-score normalization to each column in the iris data (note you do not need the target/label column)\n",
-    "* column wise meaning, treat each column as an array, and find the standard deviations and means of that column\n",
-    "* note, you can use zscore from scipy.stats\n",
-    "* results should be a pandas dataframe, printing out 5 rows and running the describe() method on the dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 9\n",
-    "* make a function that takes in a 2d numpy array X, a 1d numpy array Y and the size the training data, test data and valiation datasets.  \n",
-    "* return 6 items\n",
-    "    * train_x, train_y, test_x, test_y, val_x, val_y\n",
-    "* do not use any modules.  this can be solved using bracket notation to subset.  Note // will take care of decimals in doing division. you could also use int() to convert the float to a whole number\n",
-    "* make the params for the training, test and val size be decimals, that repsent percentages of the data.  For instance .8, .1, .1 means an 80% training, 10% test and 10% validation split\n",
-    "* use an assert to make sure the numbers add to 1\n",
-    "* print out the dimensions of all 6 elements, using iris as a test case with an 80-10-10 split"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 10\n",
-    "* using pandas, find the sum of each feature by species type (label)\n",
-    "* do the same, but find the min, max and median as well"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 11\n",
-    "* mean center each column of the iris dataframe (excluding the label column of course)\n",
-    "* this means, for each column, the mean should be zero.  to accomplish this we can subtract the column mean from each element of the column\n",
-    "* note, broadcasting, which if we have say a 150 row and 4 column dataframe, can  take a row vector of size 4 and apply it to each element\n",
-    "*  thus, we can answer this questions doing something like df - df_col_means\n",
-    "* run the describe() method at the end to show the data has been mean center"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quesiton 12\n",
-    "* Explain what the axis mean in regards to a pandas dataframe and numpy array\n",
-    "* How would you groupby two columns in pandas?\n",
-    "* What functions are used to read in csvs and excel files in pandas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 13\n",
-    "* using Pandas, subset the dataset for only species setosa and petal length > 1.3\n",
-    "* sum the feature columns and display the results for each (excluding the label column)\n",
-    "* do the same, but change the and to an or, and repeat the same calculation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 14\n",
-    "* write a lambda that subtracts 2 from all elements of a pandas dataframe\n",
-    "* display the top 5 rows\n",
-    "* use the same lambda,  but applying it to the sepal length  column only\n",
-    "* so the output should be the top 5 rows of a dataframe where all cells have had 2 subtracted and then another dataframe where only sepal length has 2 subtracted from it"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 15\n",
-    "* normalize each column of pandas dataframe to 0-1 scale.\n",
-    "* hint\n",
-    "    * 0-1 is done by using (X - xmin)/(xmax - xmin)\n",
-    "    * hint, the approach should be similar to when we mean centered the dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 16\n",
-    "* assume the below 2 matrices, the first is observations and the second is cluster centers\n",
-    "* using cdist from scipy, create a matrix where the rows represent our 3 observations and the columns represent our 2 clusters and the cells values are the euclidean distances between a given observation and cluster\n",
-    "* when would this be of use?  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "observations = np.array([\n",
-    "    [1,2,3],\n",
-    "    [4,3,1],\n",
-    "    [2,3,4]\n",
-    "])\n",
-    "\n",
-    "cluster_centers = np.array([\n",
-    "    [2,3,1],\n",
-    "    [2,1,3]\n",
-    "])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/other_topics/multiprocessing_run.py b/other_topics/multiprocessing_run.py
deleted file mode 100644
index 9c7b7e9..0000000
--- a/other_topics/multiprocessing_run.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import multiprocessing
-import time
-
-def square(x):
-	return x**2
-
-# python doens't have a main() that is executed automatically
-# like some other languages, when a python script is executed from the shell
-# it sets some variables, in this case the __name__ which is the name of the current
-# module if we are running are module as the main program, for instance "python run.py"
-#  __name__ get's  assigned __main__
-# simply, we use this if the file is to be executed as a script
-# decent rule of thumb, if we intend our code/classes/function from a script to be
-# imported, we can forgo this piece of the code
-if __name__ == '__main__':
-	
-	start = time.time()
-
-	vect = list(range(100000000))
-
-	# Runtime: 104 seconds
-	#a = []
-	#for i in vect:
-	#	a.append(square(i))
-
-
-	# Runtime Square: 30 seconds
-	#vect = [square(i) for i in vect]
-
-	# Runtime Square: 32 seconds
-	#vect = list(map(square, vect))
-
-	# Runtime Square: 18 seconds
-	pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
-	results = pool.map(square, vect)
-
-	end = time.time()
-	print("Seconds:{}".format(end - start))
-

From b0fe6a8cec5e1ace3b01b16ea479560d1ee4d2e5 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 3 Mar 2020 15:17:06 -0600
Subject: [PATCH 29/36] rebase

---
 lectures/.DS_Store                            |  Bin 6148 -> 0 bytes
 ...ontainer, Iteration Tools-checkpoint.ipynb | 2182 ----
 ...ators, and Error Handling-checkpoint.ipynb | 2341 -----
 ...tetime and Saving Objects-checkpoint.ipynb | 1883 ----
 ... and 5 - Numpy and Pandas-checkpoint.ipynb | 8760 -----------------
 lectures/Lec8.ipynb.txt                       |    0
 lectures/Lecture 10 - Recursion.ipynb         |  595 ++
 lectures/Lecture 10 - RegExp.ipynb            |  324 +
 lectures/test_pickle                          |  Bin 24 -> 0 bytes
 lectures/test_pickle.pkl                      |  Bin 28 -> 0 bytes
 10 files changed, 919 insertions(+), 15166 deletions(-)
 delete mode 100644 lectures/.DS_Store
 delete mode 100644 lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb
 delete mode 100644 lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb
 delete mode 100644 lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb
 delete mode 100644 lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb
 delete mode 100644 lectures/Lec8.ipynb.txt
 create mode 100644 lectures/Lecture 10 - Recursion.ipynb
 create mode 100644 lectures/Lecture 10 - RegExp.ipynb
 delete mode 100644 lectures/test_pickle
 delete mode 100644 lectures/test_pickle.pkl

diff --git a/lectures/.DS_Store b/lectures/.DS_Store
deleted file mode 100644
index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000
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diff --git a/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb
deleted file mode 100644
index 5b04089..0000000
--- a/lectures/.ipynb_checkpoints/Lecture 1 - Container, Iteration Tools-checkpoint.ipynb	
+++ /dev/null
@@ -1,2182 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note\n",
-    "* throughout the notebooks you will see blue font.  that is your chance to pause and play around with some of the concepts we've just learned\n",
-    "* some will be easier, some harder and the appropriate amount of time will be given\n",
-    "* use this as an example to test concepts\n",
-    "* don't worry about you're code being perfect or getting errors, that's how you'll learn the mechanics of it\n",
-    "* be adventorous so you can learn how powerful the concepts can be"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assumptions\n",
-    "* I will asssume you know some basic python\n",
-    "    * syntax\n",
-    "    * conditionals\n",
-    "    * iteration\n",
-    "    * user defined functions\n",
-    "* though in the first few lectures as they come up I will quickly review them"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lecture 1\n",
-    "* review the basics of python\n",
-    "* python containers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### IDEs\n",
-    "* Anaconda (https://www.anaconda.com/distribution/)\n",
-    "* Jupyter Notebooks\n",
-    "* Spyder\n",
-    "* Sublime (https://www.sublimetext.com/)\n",
-    "* PyCharm (https://www.jetbrains.com/pycharm/download/#section=mac)\n",
-    "* Atom (https://atom.io/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lists\n",
-    "* array, collection, of unordered items of any data type\n",
-    "* can be changed (mutable)\n",
-    "* can have duplicates\n",
-    "* can put anything in them\n",
-    "* created using brackets [ ]\n",
-    "* concat a list of dataframes in pandas\n",
-    "* https://docs.python.org/3/tutorial/datastructures.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = [\"A\", 1, 2, 2.4, \"B\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(a, list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__delitem__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__getitem__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__iadd__',\n",
-       " '__imul__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__iter__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__mul__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__reversed__',\n",
-       " '__rmul__',\n",
-       " '__setattr__',\n",
-       " '__setitem__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'append',\n",
-       " 'clear',\n",
-       " 'copy',\n",
-       " 'count',\n",
-       " 'extend',\n",
-       " 'index',\n",
-       " 'insert',\n",
-       " 'pop',\n",
-       " 'remove',\n",
-       " 'reverse',\n",
-       " 'sort']"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### adding items to a list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 3, 4, 5, 5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "a.append(5)\n",
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### adding item to specific position"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['new_item', 'new_item', 'new_item', 'new_item', 1, 2, 3, 4, 5, 5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# list.insert(index, element)\n",
-    "a.insert(0, \"new_item\")\n",
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### size of list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(len(a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### accessing specific elements using bracket notation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "new_item\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(a[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "# last item of list\n",
-    "print(a[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### slicing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['new_item', 'new_item', 'new_item', 'new_item', 1, 2, 3, 4, 5, 5]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['new_item', 'new_item']"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# return 4th and 5th indexed item, not inclusive of the 6th\n",
-    "a[4:6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a list and</h3>\n",
-    "<p style=\"color:blue\">- find the len</p>\n",
-    "<p style=\"color:blue\">- append an item</p>\n",
-    "<p style=\"color:blue\">- select an item using the index value</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a nested list</h3>\n",
-    "<p style=\"color:blue\">- access the first item of the first neseted list element"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### map\n",
-    "* map lets you apply a function to each element of a python list\n",
-    "* map(func, list)\n",
-    "* returns map object which we can instantly convert to a list\n",
-    "* https://docs.python.org/3/library/functions.html#map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 3, 3]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [-3,-3,-3]\n",
-    "list(map(abs, a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### filter\n",
-    "* applies a function to each element of a list and returns a True or False, then subsets the list\n",
-    "* filter(function, list)\n",
-    "* https://docs.python.org/3/library/functions.html#filter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def my_func(x):\n",
-    "    return x > 0\n",
-    "\n",
-    "a = [1,2,3,-4]\n",
-    "\n",
-    "list(filter(my_func, a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a list of random numbers</h3>\n",
-    "<p style=\"color:blue\">- filter for items above 2</p>\n",
-    "<p style=\"color:blue\">- map the sqrt function from math to it to each element</p>\n",
-    "<p style=\"color:blue\">- use \"from math import sqrt\"</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### find the index of a given item"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"a\", \"b\"]\n",
-    "a.index(\"a\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"a\", \"b\", \"a\"]\n",
-    "a.index(\"a\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Tuple\n",
-    "* ordered and unchangable array\n",
-    "* ( ) parenths signal tuple\n",
-    "* https://docs.python.org/2/tutorial/datastructures.html#tuples-and-sequences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = (1,2,3,\"a\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(a, tuple)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tuple"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__getitem__',\n",
-       " '__getnewargs__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__iter__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__mul__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__rmul__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'count',\n",
-       " 'index']"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### access  elements using bracket notation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = (1,2,2,3,4,5,5)\n",
-    "a.count(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.index(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set\n",
-    "* unique elements\n",
-    "* unordered\n",
-    "* mixed types\n",
-    "* { } curly brackets indicate set\n",
-    "* could also use set()\n",
-    "* if we say x = { } this makes an empty dictionary not an empty set\n",
-    "* in such case we'd use set()\n",
-    "* https://docs.python.org/2/tutorial/datastructures.html#sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5}"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = {1,2,3,4,5,5,5,5,5}\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(a, set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__and__',\n",
-       " '__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__iand__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__ior__',\n",
-       " '__isub__',\n",
-       " '__iter__',\n",
-       " '__ixor__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__or__',\n",
-       " '__rand__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__ror__',\n",
-       " '__rsub__',\n",
-       " '__rxor__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__sub__',\n",
-       " '__subclasshook__',\n",
-       " '__xor__',\n",
-       " 'add',\n",
-       " 'clear',\n",
-       " 'copy',\n",
-       " 'difference',\n",
-       " 'difference_update',\n",
-       " 'discard',\n",
-       " 'intersection',\n",
-       " 'intersection_update',\n",
-       " 'isdisjoint',\n",
-       " 'issubset',\n",
-       " 'issuperset',\n",
-       " 'pop',\n",
-       " 'remove',\n",
-       " 'symmetric_difference',\n",
-       " 'symmetric_difference_update',\n",
-       " 'union',\n",
-       " 'update']"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 4}"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = {1,2,3,4}\n",
-    "b = (1,4,5,6)\n",
-    "a.intersection(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### lists can be converted to sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'a', 'b'}"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"a\", \"b\", \"a\"]\n",
-    "set(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3}"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_set = set([1,1,1,2,2,2,3,3,3])\n",
-    "my_set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = {}\n",
-    "type(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a list with some repeating elements</h3>\n",
-    "<p style=\"color:blue\">- find the length of the list</p>\n",
-    "<p style=\"color:blue\">- convert it to a set</p>\n",
-    "<p style=\"color:blue\">- find the size of the set</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dict\n",
-    "* key value pairs\n",
-    "* unordered\n",
-    "* no repeating keys\n",
-    "* curly brakets, though you add key value pairs, seperated by a colon\n",
-    "* https://docs.python.org/2/tutorial/datastructures.html#dictionaries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dict = {\n",
-    "    \"a\": 1,\n",
-    "    \"b\": 2,\n",
-    "    \"c\": 3\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(my_dict, dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(my_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__contains__',\n",
-       " '__delattr__',\n",
-       " '__delitem__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__getitem__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__iter__',\n",
-       " '__le__',\n",
-       " '__len__',\n",
-       " '__lt__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__setitem__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " 'clear',\n",
-       " 'copy',\n",
-       " 'fromkeys',\n",
-       " 'get',\n",
-       " 'items',\n",
-       " 'keys',\n",
-       " 'pop',\n",
-       " 'popitem',\n",
-       " 'setdefault',\n",
-       " 'update',\n",
-       " 'values']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(my_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### grab the keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['a', 'b', 'c'])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### grab the values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_values([1, 2, 3])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict.values()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### iterate a dictionary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a\n",
-      "b\n",
-      "c\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in my_dict:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_items([('a', 1), ('b', 2), ('c', 3)])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# list of tuples representing \n",
-    "my_dict.items()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a --> 1\n",
-      "b --> 2\n",
-      "c --> 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k,v in my_dict.items():\n",
-    "    print(k, \"-->\", v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### accessing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict[\"a\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### don't repeat keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dict = {\n",
-    "    1:\"Brian\",\n",
-    "    2:\"Jane\",\n",
-    "    2:\"John\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: 'Brian', 2: 'John'}"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### nested dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this is basically json\n",
-    "my_dict = {\n",
-    "    \n",
-    "    1:{\"first_name\":\"Brian\", \"last_name\":\"Craft\"},\n",
-    "    2:{\"first_name\":\"Jane\", \"last_name\":\"Doe\"},\n",
-    "    3:{\"first_name\":\"John\", \"last_name\":\"Doe\"}\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'first_name': 'Brian', 'last_name': 'Craft'}"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(my_dict[1], dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(my_dict[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Brian'"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_dict[1][\"first_name\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a dictionary</h3>\n",
-    "<p style=\"color:blue\">- access elements with the bracket notation</p>\n",
-    "<p style=\"color:blue\">- iterate over the dictionary using .items()</p>\n",
-    "<p style=\"color:blue\">- get the keys from the dictionary using .keys()</p>\n",
-    "<p style=\"color:blue\">- get the values from the dictionary using .values?()</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Eunumerate\n",
-    "* add an index value to your loops, or counter\n",
-    "* you can use the idx%1000 == 0 which returns the remainder of idx/1000 (modulus or clock math) which can be used as a way to monitor a time intensive loop\n",
-    "* https://docs.python.org/2/tutorial/datastructures.html#looping-techniques"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<enumerate at 0x7f9c682be5f0>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"a\", \"b\", \"c\"]\n",
-    "enumerate(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 --> a\n",
-      "1 --> b\n",
-      "2 --> c\n"
-     ]
-    }
-   ],
-   "source": [
-    "for idx, i in enumerate(a):\n",
-    "    print(idx,\"-->\", i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Zip\n",
-    "* Mash iterables together to make nested tuples\n",
-    "* you can use zip to mash together keys and values and put them into a dictionary\n",
-    "* https://docs.python.org/2/library/functions.html#zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3]\n",
-    "b = [\"a\", \"b\", \"c\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<zip at 0x7f9c682c3a50>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zip(a,b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 'a'), (2, 'b'), (3, 'c')]"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[x for x in zip(a,b)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1, 'a')\n",
-      "(2, 'b')\n",
-      "(3, 'c')\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'tuple'>\n",
-      "<class 'tuple'>\n",
-      "<class 'tuple'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(type(i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1, 'a')\n",
-      "(2, 'b')\n",
-      "(3, 'c')\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(i[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a\n",
-      "b\n",
-      "c\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in zip(a,b):\n",
-    "    print(i[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1, 'a', 'A'), (2, 'b', 'B'), (3, 'c', 'C')]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2,3]\n",
-    "b = [\"a\", \"b\", \"c\"]\n",
-    "c = [\"A\", \"B\", \"C\"]\n",
-    "\n",
-    "list(zip(a,b,c))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Continue, Break and Pass\n",
-    "* break: chance to exit a loop when a condition is met\n",
-    "* continue: continue to the next iteration of the loop\n",
-    "* pass: do nothing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 is not greater then 4\n",
-      "1 is not greater then 4\n",
-      "2 is not greater then 4\n",
-      "3 is not greater then 4\n",
-      "4 is not greater then 4\n",
-      "5 is greater then 4 so we will break out loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        print(\"{} is greater then 4 so we will break out loop\".format(x))\n",
-    "        break\n",
-    "    else:\n",
-    "        print(\"{} is not greater then 4\".format(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5 is greater then 4\n",
-      "6 is greater then 4\n",
-      "7 is greater then 4\n",
-      "8 is greater then 4\n",
-      "9 is greater then 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        print(\"{} is greater then 4\".format(x))\n",
-    "    else:\n",
-    "        pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 is not greater then 4\n",
-      "1 is not greater then 4\n",
-      "2 is not greater then 4\n",
-      "3 is not greater then 4\n",
-      "4 is not greater then 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        continue\n",
-    "    else:\n",
-    "        print(\"{} is not greater then 4\".format(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 is not greater then 4\n",
-      "print statement below\n",
-      "1 is not greater then 4\n",
-      "print statement below\n",
-      "2 is not greater then 4\n",
-      "print statement below\n",
-      "3 is not greater then 4\n",
-      "print statement below\n",
-      "4 is not greater then 4\n",
-      "print statement below\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        continue\n",
-    "    else:\n",
-    "        print(\"{} is not greater then 4\".format(x))\n",
-    "       \n",
-    "    print(\"print statement below\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 is not greater then 4\n",
-      "print statement below\n",
-      "1 is not greater then 4\n",
-      "print statement below\n",
-      "2 is not greater then 4\n",
-      "print statement below\n",
-      "3 is not greater then 4\n",
-      "print statement below\n",
-      "4 is not greater then 4\n",
-      "print statement below\n",
-      "print statement below\n",
-      "print statement below\n",
-      "print statement below\n",
-      "print statement below\n",
-      "print statement below\n"
-     ]
-    }
-   ],
-   "source": [
-    "lst = list(range(10))\n",
-    "\n",
-    "for x in lst:\n",
-    "    if x > 4:\n",
-    "        pass\n",
-    "    else:\n",
-    "        print(\"{} is not greater then 4\".format(x))\n",
-    "       \n",
-    "    print(\"print statement below\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Itertools\n",
-    "* tools for working with iterables\n",
-    "* additional building blocks\n",
-    "* chain pushes lists together\n",
-    "* https://docs.python.org/2/library/itertools.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### chain mashes nested lists into one"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [\n",
-    "    [\"a\", \"b\", \"c\"],\n",
-    "    [\"d\", \"e\", \"f\", \"b\"]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = list(chain.from_iterable(a))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c', 'd', 'e', 'f', 'b']"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### permutation\n",
-    "* https://www.mathsisfun.com/combinatorics/combinations-permutations.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import permutations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'b'), ('a', 'c'), ('b', 'a'), ('b', 'c'), ('c', 'a'), ('c', 'b')]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(permutations([\"a\",\"b\",\"c\"], 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'b', 'c'),\n",
-       " ('a', 'c', 'b'),\n",
-       " ('b', 'a', 'c'),\n",
-       " ('b', 'c', 'a'),\n",
-       " ('c', 'a', 'b'),\n",
-       " ('c', 'b', 'a')]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(permutations([\"a\",\"b\",\"c\"], 3))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### combinations\n",
-    "* https://www.mathsisfun.com/combinatorics/combinations-permutations.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import combinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'b'), ('a', 'c'), ('b', 'c')]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(combinations([\"a\",\"b\",\"c\"], 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'b', 'c')]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(combinations([\"a\",\"b\",\"c\"], 3))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [[\"a\", \"b\"], [\"c\", \"d\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd')]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(product(*a))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd')]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(product([\"a\", \"b\"],[\"c\", \"d\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Collections\n",
-    "* use counter to get frequencies of items in a list\n",
-    "* https://docs.python.org/2/library/collections.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counter = Counter(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({'a': 1, 'b': 2, 'c': 1, 'd': 1, 'e': 1, 'f': 1})"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "collections.Counter"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(counter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a 1\n",
-      "b 2\n",
-      "c 1\n",
-      "d 1\n",
-      "e 1\n",
-      "f 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k,v in counter.items():\n",
-    "    print(k,v)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a list</h3>\n",
-    "<p style=\"color:blue\">- find all the combinations</p>\n",
-    "<p style=\"color:blue\">- find all the permutations</p>\n",
-    "<p style=\"color:blue\">- create a counter for the list to get item frequencies</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb
deleted file mode 100644
index 65ce8e7..0000000
--- a/lectures/.ipynb_checkpoints/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling-checkpoint.ipynb	
+++ /dev/null
@@ -1,2341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lecture 2\n",
-    "* user defined functions\n",
-    "* comprehensions\n",
-    "* lambda functions\n",
-    "* generators\n",
-    "* exception handling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### User Defined Functions\n",
-    "* def means we are defining a function\n",
-    "* def function_name(param1, param2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#def my_function(param1, param2):\n",
-    "#    do some stuff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x):\n",
-    "    print(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "function"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(my_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### return will let you store output from a function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x):\n",
-    "    return x+2 # note you can just add on the fly here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "y = my_func(2)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### multiple params"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x,y):\n",
-    "    return x+y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "y = my_func(5,2)\n",
-    "print(y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### a bit more complicated"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x,y):\n",
-    "    \n",
-    "    output = None\n",
-    "    z = x+y\n",
-    "    \n",
-    "    if z < 5:\n",
-    "        output = True\n",
-    "    else:\n",
-    "        output = False\n",
-    "        \n",
-    "    return output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "output = my_func(2,1)\n",
-    "output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "output = my_func(2,5)\n",
-    "output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a function that</h3>\n",
-    "<p style=\"color:blue\">- takes as an input a list of integers</p>\n",
-    "<p style=\"color:blue\">- adds them all together</p>\n",
-    "<p style=\"color:blue\">- returns the final sum</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### note python will assume the positioning of params\n",
-    "* consider the below\n",
-    "* the first param is a string\n",
-    "* the second is a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x,y):\n",
-    "    x = x.lower()\n",
-    "    y = y + 2\n",
-    "    print(x)\n",
-    "    print(y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#my_func(5,\"str\") # won't work"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "str\n",
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(\"STR\", 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we could explicitly use the keyword, even if things are out of order"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "str\n",
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(y = 5, x = \"str\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### but we can't do a keyword followed by a positional argument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-3-f73cfd3c8222>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-f73cfd3c8222>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    my_func(y = 5, \"str\") # won't work\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(y = 5, \"str\") # won't work"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we can use args to have a variable amount of arguments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(*args):\n",
-    "    for arg in args:\n",
-    "        print(arg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(1,2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "5\n",
-      "6\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(1,2,5,6,2,3,4,5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a function that</h3>\n",
-    "<p style=\"color:blue\">- takes an inifinte amount of params</p>\n",
-    "<p style=\"color:blue\">- puts them all in a list</p>\n",
-    "<p style=\"color:blue\">- returns the list</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we can use kwargs to have keyword arguments that are optional\n",
-    "* arguments are put into a dictionary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(**kwargs):\n",
-    "    print(type(kwargs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'dict'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(x = 2, y = 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(**kwargs):\n",
-    "    print(kwargs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'x': 2, 'y': 3}\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(x = 2, y = 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x,y, **kwargs):\n",
-    "    val = x + y\n",
-    "    if \"z\" in kwargs:\n",
-    "        # remember, we can't access the value simply using\n",
-    "        # z, we have to grab it form the kwargs dictionary\n",
-    "        val+=kwargs[\"z\"]\n",
-    "    print(val)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(5,7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "my_func() takes 2 positional arguments but 3 were given",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-8751ad374ad8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: my_func() takes 2 positional arguments but 3 were given"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(5,2,4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(5,2,z=4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a function that takes two params</h3>\n",
-    "<p style=\"color:blue\">- divide the first param by the second</p>\n",
-    "<p style=\"color:blue\">- searches for a third named \"third\"</p>\n",
-    "<p style=\"color:blue\">- if it exists add that to the division of the first two params</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we can also set defaults for params\n",
-    "take a look at the link below in sklearn decision tree classifier\n",
-    "* the default can be overriden by passing in a value\n",
-    "* https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x, y = 2):\n",
-    "    print(x + y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(2,5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(x = 2, y = 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(x = 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### use of stars\n",
-    "* the star unpacks the tuple, saying this is a list of arguments, vs treat this list as one argument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(*args):\n",
-    "    for i in args:\n",
-    "        print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func([1,2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_func(*[1,2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generally this syntax isn't seen but it's worth being aware, as it does show up from time to time.  See keras example below.\n",
-    "* https://keras.io/getting-started/functional-api-guide/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def my_func(x):\n",
-    "    def my_func2(y):\n",
-    "        return x+y\n",
-    "    return my_func2\n",
-    "    \n",
-    "tst = my_func(5)\n",
-    "tst(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tst(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_func(2)(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lambda Functions\n",
-    "* annonymous, function without a name\n",
-    "* defined on the fly with one line\n",
-    "* multiple params but one expression\n",
-    "* commonly used in mapping, applying and filtering\n",
-    "* lambda params: expression\n",
-    "* https://docs.python.org/3/reference/expressions.html#lambda"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_lambda = lambda x: x + 2\n",
-    "my_lambda(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 4, 5]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_lambda = lambda x: x + 2\n",
-    "\n",
-    "a = [1,2,3]\n",
-    "\n",
-    "list(map(my_lambda, a))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda x: x + 2)(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### but we don't even need to store the function in my_lambda"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 4, 5]"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(map(lambda x: x+2, a))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[4, 5]"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [2,3,4,5]\n",
-    "\n",
-    "list(filter(lambda x: x > 3, a))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">write a lambda that</h3>\n",
-    "<p style=\"color:blue\">- maps to a list of strings and</p>\n",
-    "<p style=\"color:blue\">- replaces all the letter o with e</p>\n",
-    "<p style=\"color:blue\">- converts to upper case</p>\n",
-    "<p style=\"color:blue\">- .replace(item_replace, replace_with)</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## comprehension\n",
-    "* memory efficient way to run a for loop and put the results in a list or iterable\n",
-    "* on the fly apply functions to the elements of a list\n",
-    "* https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2,3,4,5]\n",
-    "b = []\n",
-    "for i in a:\n",
-    "    b.append(i)\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = [i for i in a]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('a', 'b', 'c')"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"A\", \"B\", \"C\"]\n",
-    "b = tuple(x.lower() for x in a)\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{(1, 'a'), (2, 'b'), (3, 'c')}"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2,3]\n",
-    "b = [\"a\",\"b\",\"c\"]\n",
-    "\n",
-    "my_dict = {(x[0],x[1]) for x in zip(a,b)}\n",
-    "my_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[4, 5]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = [i for i in a if i > 3]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-1, -2, -3, -4, -5]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = [i*-1 for i in a]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c']"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [\"A\", \"B\", \"C\"]\n",
-    "b = [x.lower() for x in a]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c']"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_func = lambda x: x.lower()\n",
-    "a = [\"A\", \"B\", \"C\"]\n",
-    "b = [my_func(x) for x in a]\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, -1, -2, -3, -4)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# we have to use tuple() becasue just using () will declare a generator\n",
-    "tup = tuple(-x for x in range(5))\n",
-    "tup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 1, 2, 3, 4)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = tuple(abs(x) for x in tup)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 1, 1: 2, 2: 3, 3: 4, 4: 5}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct = dict((idx,i+1) for idx,i in enumerate(range(5)))\n",
-    "dct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: 'A', 2: 'B', 3: 'C'}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2,3]\n",
-    "b = [\"A\", \"B\", \"C\"]\n",
-    "dct = dict((x[0], x[1]) for x in zip(a,b))\n",
-    "dct"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">write a lambda that</h3>\n",
-    "<p style=\"color:blue\">- use comprehension to apply to a list of numbers</p>\n",
-    "<p style=\"color:blue\">- the lambda should return the remainder of the number divided by 2</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "51%2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "50%2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generators\n",
-    "* special function that returns a lazy iterator.\n",
-    "* do not store contents in memory.\n",
-    "* Instead, the state of the function is remembered.\n",
-    "* Keep track of where we are in the iterator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst = [num for num in range(5)]\n",
-    "gen = (num for num in range(5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fcd988c25d0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in gen:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## use next() to grab the next item in the generator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gen = (num for num in range(2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "StopIteration",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### can use yield instead of return, to return the next item of the generator.  yield automatically tells python this is a generator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def infinite_sequence():\n",
-    "    for i in [num for num in range(5)]:\n",
-    "        yield i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gen = infinite_sequence()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object infinite_sequence at 0x7fcd988c2350>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in gen:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen = infinite_sequence()\n",
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### very memory efficient"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "859724480\n",
-      "0.85972448\n"
-     ]
-    }
-   ],
-   "source": [
-    "nums_squared_lc = [i * 2 for i in range(100000000)]\n",
-    "my_bytes = sys.getsizeof(nums_squared_lc)\n",
-    "gb = my_bytes / 1e+9\n",
-    "\n",
-    "print(my_bytes)\n",
-    "print(gb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "128\n",
-      "1.28e-07\n"
-     ]
-    }
-   ],
-   "source": [
-    "# returns bytes\n",
-    "nums_squared_lc = (i * 2 for i in range(100000000))\n",
-    "my_bytes = sys.getsizeof(nums_squared_lc)\n",
-    "gb = my_bytes / 1e+9\n",
-    "\n",
-    "print(my_bytes)\n",
-    "print(gb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### could use generator for dealing with text files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "csv = [\n",
-    "    \"permalink,company,numEmps,category,city,state,fundedDate,raisedAmt,raisedCurrency,round\",\n",
-    "    \"digg,Digg,60,web,San Francisco,CA,1-Dec-06,8500000,USD,b\",\n",
-    "    \"digg,Digg,60,web,San Francisco,CA,1-Oct-05,2800000,USD,a\",\n",
-    "    \"facebook,Facebook,450,web,Palo Alto,CA,1-Sep-04,500000,USD,angel\",\n",
-    "    \"facebook,Facebook,450,web,Palo Alto,CA,1-May-05,12700000,USD,a\",\n",
-    "    \"photobucket,Photobucket,60,web,Palo Alto,CA,1-Mar-05,3000000,USD,a\"\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#file_name = \"techcrunch.csv\"\n",
-    "\n",
-    "# declares a generator\n",
-    "#lines = (line for line in open(file_name))\n",
-    "\n",
-    "# split the lines on the comma\n",
-    "#list_line = (s.rstrip().split(\",\") for s in lines)\n",
-    "\n",
-    "# gives us the next line\n",
-    "#cols = next(list_line)\n",
-    "\n",
-    "# this will be helpful for filtering, keys are column names\n",
-    "# \n",
-    "#company_dicts = (dict(zip(cols, data)) for data in list_line)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fcd68774450>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lines = (line for line in csv)\n",
-    "lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fcd687745d0>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_line = (s.rstrip().split(\",\") for s in lines)\n",
-    "list_line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['permalink',\n",
-       " 'company',\n",
-       " 'numEmps',\n",
-       " 'category',\n",
-       " 'city',\n",
-       " 'state',\n",
-       " 'fundedDate',\n",
-       " 'raisedAmt',\n",
-       " 'raisedCurrency',\n",
-       " 'round']"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(list_line)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['digg',\n",
-       " 'Digg',\n",
-       " '60',\n",
-       " 'web',\n",
-       " 'San Francisco',\n",
-       " 'CA',\n",
-       " '1-Dec-06',\n",
-       " '8500000',\n",
-       " 'USD',\n",
-       " 'b']"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(list_line)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exception Handling and Errors\n",
-    "* https://docs.python.org/3/tutorial/errors.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### syntax errors\n",
-    "* missing parenth or bracket maybe, some formatting issue"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-76-35c08edd0df2>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-76-35c08edd0df2>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print((2)))\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "print((2)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### exception errors:\n",
-    "* you've tried to do something that can't be done"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-78-fad870a50e27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "print(5/0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "can only concatenate str (not \"int\") to str",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-79-89309f5042e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"string\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"string\" + 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### assert\n",
-    "* logical test\n",
-    "* if the test isn't met we raise an assertion error\n",
-    "* assert logical, error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "The length of x is not greater than 3",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-36-523a2a17546a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The length of x is not greater than 3\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m: The length of x is not greater than 3"
-     ]
-    }
-   ],
-   "source": [
-    "x = [2,3]\n",
-    "\n",
-    "assert len(x) > 3, \"The length of x is not greater than 3\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "x is empty",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-37-9bd7d8a13151>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"x is empty\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m: x is empty"
-     ]
-    }
-   ],
-   "source": [
-    "x = []\n",
-    "\n",
-    "assert len(x) != 0, \"x is empty\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### raise\n",
-    "* we could also raise an exception"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "Exception",
-     "evalue": "x should not exceed 5.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-f2f500004ca1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x should not exceed 5.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mException\u001b[0m: x should not exceed 5."
-     ]
-    }
-   ],
-   "source": [
-    "x = 10\n",
-    "if x > 5:\n",
-    "    raise Exception('x should not exceed 5.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### try and except\n",
-    "* tell python to try a block of code, if there is an error, revert to except"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found an error.  Item String isn't a number or float\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,2,3,4,\"String\"]\n",
-    "\n",
-    "for i in a:\n",
-    "    \n",
-    "    try:\n",
-    "        i = i+2\n",
-    "    except:\n",
-    "        print(\"Found an error.  Item {} isn't a number or float\".format(i))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### plenty of built in exception/errors classese in python\n",
-    "* for instance, IndexError\n",
-    "* NameError\n",
-    "* https://docs.python.org/3/library/exceptions.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-83-070992024e9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "a = [2]\n",
-    "a[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'z' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-84-c01ac3f50fdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'z' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "print(z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### excepting the error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n",
-      "this is a type error a + 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1, 2, 3] \n",
-    "try:  \n",
-    "    print(a[1]) \n",
-    "  \n",
-    "    # Throws error since there are only 3 elements in array \n",
-    "    #print(a[3])\n",
-    "    \n",
-    "    # throw another error\n",
-    "    #print(a + )\n",
-    "    \n",
-    "    print(\"A\" + 5)\n",
-    "    \n",
-    "except TypeError:\n",
-    "    print(\"this is a type error {}\".format(\"a + 5\"))\n",
-    "    #raise\n",
-    "    \n",
-    "except IndexError: \n",
-    "    print(\"This is an index error\")\n",
-    "    \n",
-    "except SyntaxError:\n",
-    "    print(\"This is a syntax error\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-954e4920b11e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# Throws error since there are only 3 elements in array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1, 2, 3] \n",
-    "try:  \n",
-    "    print(a[1]) \n",
-    "  \n",
-    "    # Throws error since there are only 3 elements in array \n",
-    "    print(a[3])\n",
-    "    \n",
-    "except IndexError: \n",
-    "    raise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### raise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-97-b6c41dcfb79b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"string\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0;31m# raises the error that was caught\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
-     ]
-    }
-   ],
-   "source": [
-    "a = 5\n",
-    "\n",
-    "try:\n",
-    "    print(a + \"string\")\n",
-    "except TypeError:\n",
-    "    raise # raises the error that was caught"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "can only concatenate str (not \"int\") to str",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-6529e87d2cea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"str\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    print(\"str\" + 2)\n",
-    "except Exception:\n",
-    "    raise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "unexpected EOF while parsing (<ipython-input-20-377b2fdb9f94>, line 4)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-20-377b2fdb9f94>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    \u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    print(\"str\" + 2)\n",
-    "except ValueError:\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### finally is always executed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "the finally block is executed\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 5\n",
-    "\n",
-    "try:\n",
-    "    print(x + 5)\n",
-    "except:\n",
-    "    raise\n",
-    "finally:\n",
-    "    print(\"the finally block is executed\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "the finally block is executed\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "can only concatenate str (not \"int\") to str",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-8ffd01c41ed7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
-     ]
-    }
-   ],
-   "source": [
-    "x = \"str\"\n",
-    "\n",
-    "try:\n",
-    "    print(x + 5)\n",
-    "except:\n",
-    "    raise\n",
-    "finally:\n",
-    "    print(\"the finally block is executed\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### optional else clause"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "the else get's executed if hte try doesn't cause an error\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 5\n",
-    "\n",
-    "try:\n",
-    "    print(x + 5)\n",
-    "except:\n",
-    "    raise\n",
-    "else:\n",
-    "    print(\"the else get's executed if hte try doesn't cause an error\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "can only concatenate str (not \"int\") to str",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-35-027e337063de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
-     ]
-    }
-   ],
-   "source": [
-    "x = \"str\"\n",
-    "\n",
-    "try:\n",
-    "    print(x + 5)\n",
-    "except:\n",
-    "    raise\n",
-    "else:\n",
-    "    print(\"the else get's executed if hte try doesn't cause an error\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "the else get's executed if hte try doesn't cause an error\n",
-      "here comes the finally clause just because\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = 5\n",
-    "\n",
-    "try:\n",
-    "    print(x + 5)\n",
-    "except:\n",
-    "    raise\n",
-    "else:\n",
-    "    print(\"the else get's executed if hte try doesn't cause an error\")\n",
-    "finally:\n",
-    "    print(\"here comes the finally clause just because\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "here comes the finally clause just because\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "can only concatenate str (not \"int\") to str",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-38-41b0f8110096>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
-     ]
-    }
-   ],
-   "source": [
-    "x = \"str\"\n",
-    "\n",
-    "try:\n",
-    "    print(x + 5)\n",
-    "except:\n",
-    "    raise\n",
-    "else:\n",
-    "    print(\"the else get's executed if hte try doesn't cause an error\")\n",
-    "finally:\n",
-    "    print(\"here comes the finally clause just because\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### just focus on:\n",
-    "* try and except and catching/raising your error somehow\n",
-    "* assert"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb
deleted file mode 100644
index ec37b19..0000000
--- a/lectures/.ipynb_checkpoints/Lecture 3 - OOO, Datetime and Saving Objects-checkpoint.ipynb	
+++ /dev/null
@@ -1,1883 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### OOO\n",
-    "* simple OOO example\n",
-    "* classes - template of objects\n",
-    "* objects - member or instance of class\n",
-    "* classes can have\n",
-    "    * characteristics/attribuets\n",
-    "    * methods"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simple framework for a dog\n",
-    "class dog:\n",
-    "    \n",
-    "    # these are params here, things we set when\n",
-    "    # we initialize our instance of our class\n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.breed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dir(my_dog)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(my_dog)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simple framework for a dog\n",
-    "class dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        \n",
-    "    def speak(self):\n",
-    "        print(\"this is speaking\")\n",
-    "        \n",
-    "    def change_color(self, color):\n",
-    "        self.color = color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.speak"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.speak()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.change_color(\"red\")\n",
-    "my_dog.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simple framework for a dog\n",
-    "class dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance += distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")\n",
-    "my_dog.distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.walk(5)\n",
-    "my_dog.distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.walk(12)\n",
-    "my_dog.distance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_dog.walk(18)\n",
-    "my_dog.distance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inheritence\n",
-    "* Inherit properties and functions from other objects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance += distance\n",
-    "        \n",
-    "class Retreiver(Dog):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r =  retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'blue'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r.color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__module__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " '__weakref__',\n",
-       " 'breed',\n",
-       " 'color',\n",
-       " 'distance',\n",
-       " 'name',\n",
-       " 'walk']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance += distance\n",
-    "        \n",
-    "class Retreiver(Dog):\n",
-    "    \n",
-    "    def speak(self):\n",
-    "        print(\"My name is {}\".format(self.name))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "My name is Rex\n"
-     ]
-    }
-   ],
-   "source": [
-    "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
-    "r.speak()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Dog' object has no attribute 'speak'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-800ba7303882>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mDog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"blue\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"retreiver\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Rex\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspeak\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'Dog' object has no attribute 'speak'"
-     ]
-    }
-   ],
-   "source": [
-    "r =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
-    "r.speak()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Super"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Dog:\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance += distance\n",
-    "        \n",
-    "class Retreiver(Dog):\n",
-    "    \n",
-    "    def __init__(self, color, breed, name, last_name):\n",
-    "        super().__init__(color, breed, name)    \n",
-    "        self.last_name = last_name\n",
-    "        \n",
-    "    def speak(self):\n",
-    "        print(\"My name is {}\".format(self.name))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\", last_name = \"Rex 2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "blue\n",
-      "retreiver\n",
-      "Rex\n",
-      "Rex 2\n",
-      "0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(r.color)\n",
-    "print(r.breed)\n",
-    "print(r.name)\n",
-    "print(r.last_name)\n",
-    "print(r.distance)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Overload\n",
-    "* have a function behave in different ways depending on how it is called or a param is passed to it\n",
-    "* can overload a UDF or a build in function"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Overload a UDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Someone is barking\n",
-      "Rex is barking\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Dog:\n",
-    "    def speak(self, name=None):\n",
-    "        if name is not None:\n",
-    "            print(name + \" is barking\")\n",
-    "        else:\n",
-    "            print('Someone is barking')\n",
-    "\n",
-    "# Creating a class instance\n",
-    "d = Dog()\n",
-    "\n",
-    "# Call the method\n",
-    "d.speak()\n",
-    "\n",
-    "# Call the method and pass a parameter\n",
-    "d.speak('Rex')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Overload Built In Function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Basket:\n",
-    "    def __init__(self, basket):\n",
-    "        self.basket = basket\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.basket)\n",
-    "\n",
-    "b = Basket(['a', 'b', 'c'])\n",
-    "print(len(b))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Basket:\n",
-    "    def __init__(self, basket):\n",
-    "        self.basket = basket\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return 50\n",
-    "\n",
-    "b = Basket(['a', 'b', 'c'])\n",
-    "print(len(b))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Class Methods\n",
-    "* takes cls as the first input\n",
-    "* bound to the class, not the instance of the object\n",
-    "* can access class variables though not instance variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Dog:\n",
-    "    \n",
-    "    class_atr = 10\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def speak(cls, speak):\n",
-    "        print(speak)\n",
-    "        \n",
-    "    @classmethod\n",
-    "    def add(cls,x,y):\n",
-    "        return x+y\n",
-    "    \n",
-    "    @classmethod\n",
-    "    def printer(cls):\n",
-    "        print(cls.class_atr)\n",
-    "        \n",
-    "    @classmethod\n",
-    "    def class_add(cls):\n",
-    "        cls.class_atr = cls.class_atr + 10\n",
-    "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name\n",
-    "        self.distance = 0\n",
-    "        \n",
-    "    def walk(self, distance):\n",
-    "        self.distance = Dog.add(self.distance, distance)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r =  Dog(color = \"blue\", breed = \"retreiver\", name = \"Rex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n"
-     ]
-    }
-   ],
-   "source": [
-    "r.printer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20\n"
-     ]
-    }
-   ],
-   "source": [
-    "r.class_add()\n",
-    "r.printer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "say something\n"
-     ]
-    }
-   ],
-   "source": [
-    "r.speak(\"say something\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "say something\n"
-     ]
-    }
-   ],
-   "source": [
-    "Dog.speak(\"say something\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r.walk(5)\n",
-    "r.walk(10)\n",
-    "r.distance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Static Method\n",
-    "* neither bound to the instance or class\n",
-    "* no access to the instance or class attribuets\n",
-    "* could be a standalone function but we want to keep it in the class\n",
-    "* for instance, distance measures inside a KNN class.  then we can reference the functions when we have a fit method\n",
-    "* aren't super useful due to limited access of information\n",
-    "* utility type funciton"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Distance:\n",
-    "    \n",
-    "    @staticmethod\n",
-    "    def add(a,b):\n",
-    "        print(a+b)\n",
-    "    \n",
-    "    @staticmethod\n",
-    "    def sub(a,b):\n",
-    "        print(a-b)\n",
-    "    \n",
-    "    def __init__(self,x,y):\n",
-    "        self.x = x\n",
-    "        self.y = y\n",
-    "        \n",
-    "    def operate(self, how):\n",
-    "        if how == \"add\":\n",
-    "            Distance.add(self.x, self.y)\n",
-    "        else:\n",
-    "            Distance.sub(self.x, self.y)\n",
-    "        \n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = Distance(10,5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "15\n"
-     ]
-    }
-   ],
-   "source": [
-    "d.operate(\"add\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "d.operate(\"sub\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Characteristics/Properties/Attributes\n",
-    "* accessed using dot notation\n",
-    "* methods are used using dot notation and parentheses ()\n",
-    "* we can now read the sklearn documentation that much better\n",
-    "* https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
-    "* since sklearn documentation is consistent, we can easily look at any if the page and disect how the data structure works\n",
-    "    * what methods we can use\n",
-    "    * what characteristics we can access\n",
-    "    * what params we can pass\n",
-    "* https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### DateTimes in Python\n",
-    "* https://docs.python.org/3/library/datetime.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "from datetime import date"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get Todays Date"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24\n"
-     ]
-    }
-   ],
-   "source": [
-    "today = date.today()\n",
-    "print(today)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "today.day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "today.month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2019"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "today.year"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Weekdays start at monday and index at 0\n",
-    "* 0:Monday\n",
-    "* 1:Tuesday\n",
-    "* 2:Wednesday\n",
-    "* 3:Thursday\n",
-    "* 4:Friday\n",
-    "* 5:Saturday\n",
-    "* 6:Sunday"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "today.weekday()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__radd__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__rsub__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__sub__',\n",
-       " '__subclasshook__',\n",
-       " 'ctime',\n",
-       " 'day',\n",
-       " 'fromisoformat',\n",
-       " 'fromordinal',\n",
-       " 'fromtimestamp',\n",
-       " 'isocalendar',\n",
-       " 'isoformat',\n",
-       " 'isoweekday',\n",
-       " 'max',\n",
-       " 'min',\n",
-       " 'month',\n",
-       " 'replace',\n",
-       " 'resolution',\n",
-       " 'strftime',\n",
-       " 'timetuple',\n",
-       " 'today',\n",
-       " 'toordinal',\n",
-       " 'weekday',\n",
-       " 'year']"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(today)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get Time and Date Now"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:06:33.330468\n"
-     ]
-    }
-   ],
-   "source": [
-    "now = datetime.now()\n",
-    "print(now)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__add__',\n",
-       " '__class__',\n",
-       " '__delattr__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__radd__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__rsub__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__sub__',\n",
-       " '__subclasshook__',\n",
-       " 'astimezone',\n",
-       " 'combine',\n",
-       " 'ctime',\n",
-       " 'date',\n",
-       " 'day',\n",
-       " 'dst',\n",
-       " 'fold',\n",
-       " 'fromisoformat',\n",
-       " 'fromordinal',\n",
-       " 'fromtimestamp',\n",
-       " 'hour',\n",
-       " 'isocalendar',\n",
-       " 'isoformat',\n",
-       " 'isoweekday',\n",
-       " 'max',\n",
-       " 'microsecond',\n",
-       " 'min',\n",
-       " 'minute',\n",
-       " 'month',\n",
-       " 'now',\n",
-       " 'replace',\n",
-       " 'resolution',\n",
-       " 'second',\n",
-       " 'strftime',\n",
-       " 'strptime',\n",
-       " 'time',\n",
-       " 'timestamp',\n",
-       " 'timetuple',\n",
-       " 'timetz',\n",
-       " 'today',\n",
-       " 'toordinal',\n",
-       " 'tzinfo',\n",
-       " 'tzname',\n",
-       " 'utcfromtimestamp',\n",
-       " 'utcnow',\n",
-       " 'utcoffset',\n",
-       " 'utctimetuple',\n",
-       " 'weekday',\n",
-       " 'year']"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(now)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "33"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.second"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.minute"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2019"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "now.weekday()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a variable of the current date</h3>\n",
-    "<p style=\"color:blue\">- make a list with the weekdays</p>\n",
-    "<p style=\"color:blue\">- convert the weekday() integer to the actual date name</p>\n",
-    "<p style=\"color:blue\">- print the below, without hard coding it</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Today is a Friday\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Today is a Friday\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Format Datetime output using strftime\n",
-    "* (%y/%Y – Year)\n",
-    "* (%a/%A- weekday)\n",
-    "* (%b/%B- month)\n",
-    "* (%d - day of month)\n",
-    "* %c- indicates the local date and time\n",
-    "* 12 hours time is declared [print now.strftime(\"%I:%M:%S %P) ]\n",
-    "* 24 hours time is declared [print now.strftime(\"%H:%M\")] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "now = datetime.now()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019\n",
-      "19\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%Y\"))\n",
-    "print(now.strftime(\"%y\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sunday\n",
-      "Sun\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%A\"))\n",
-    "print(now.strftime(\"%a\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "November\n",
-      "Nov\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%B\"))\n",
-    "print(now.strftime(\"%b\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11/24/19\n",
-      "24\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%D\"))\n",
-    "print(now.strftime(\"%d\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sun Nov 24 12:10:24 2019\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%c\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### we can string these together"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-November-24\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%Y-%B-%d\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019,November,24\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%Y,%B,%d\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "19-Nov-24\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(now.strftime(\"%y-%b-%d\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12:10:24\n"
-     ]
-    }
-   ],
-   "source": [
-    "# I is the hour, # M is minutes, #S is seconds\n",
-    "# army time\n",
-    "print(now.strftime(\"%I:%M:%S\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12:10\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 24 hour clock\n",
-    "print(now.strftime(\"%H:%M\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Use timedelta to add and subtract dates\n",
-    "* does not support years, because the duration of a year depends on which year (for example, leap years have Feb 29)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import timedelta"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:19:01.737490\n",
-      "2019-12-04 17:34:01.737781\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(datetime.now())\n",
-    "print(datetime.now() + timedelta(days=10, hours = 5, minutes = 15))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:19:09.017928\n",
-      "2019-11-14 07:04:09.018214\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(datetime.now())\n",
-    "print(datetime.now() - timedelta(days=10, hours = 5, minutes = 15))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:19:44.676823\n",
-      "2019-12-08 12:19:44.677028\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(datetime.now())\n",
-    "print(datetime.now() + timedelta(weeks=2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2019-11-24 12:19:54.454511\n",
-      "2019-11-10 12:19:54.454799\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(datetime.now())\n",
-    "print(datetime.now() - timedelta(weeks=2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.datetime"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# returns a datetime so we can extract info\n",
-    "x = datetime.now() - timedelta(weeks=2)\n",
-    "type(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2019"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x.weekday()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a variable with the current date</h3>\n",
-    "<p style=\"color:blue\">- add/subtract a year</p>\n",
-    "<p style=\"color:blue\">- add subtract a year and 2 weeks</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Saving Objects in Python\n",
-    "* serialization, marshaling, and flattening. \n",
-    "* same—to save an object to a file for later retrieval\n",
-    "* accomplishes this by writing the object as one long stream of bytes. \n",
-    "* pickling also used to save models in sklearn\n",
-    "* https://docs.python.org/3/library/pickle.html\n",
-    "* https://scikit-learn.org/stable/modules/model_persistence.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = [\"a\", \"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file = open('test_pickle.pkl', 'wb') \n",
-    "pickle.dump(test, file)\n",
-    "file.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# delete from memory\n",
-    "del test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file = open('test_pickle.pkl', 'rb') \n",
-    "test = pickle.load(file)\n",
-    "file.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b']"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### using with"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "item = \"some string I made\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"test_pickle.pkl\", \"wb\") as file:\n",
-    "    pickle.dump(item, file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#del item\n",
-    "#item"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"test_pickle.pkl\", \"rb\") as file:\n",
-    "    item = pickle.load(file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'some string I made'"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "item"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a python object</h3>\n",
-    "<p style=\"color:blue\">- save the object</p>\n",
-    "<p style=\"color:blue\">- delete the object from memory</p>\n",
-    "<p style=\"color:blue\">- reload it</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb b/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb
deleted file mode 100644
index c3a31f7..0000000
--- a/lectures/.ipynb_checkpoints/Lecture 4 and 5 - Numpy and Pandas-checkpoint.ipynb	
+++ /dev/null
@@ -1,8760 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Numpy Arrays\n",
-    "* multi dimensional arrays or data\n",
-    "* can be 1d, 2d, 3d\n",
-    "* very computationally efficient to perform linear algebra operations over\n",
-    "* elementwise operations\n",
-    "* row and column wise calculations\n",
-    "* https://docs.scipy.org/doc/numpy-1.11.0/numpy-user-1.11.0.pdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Arrays vs. Matrix Class\n",
-    "* \"Short Answer: Use Arrays.\"\n",
-    "* They are the standard vector/matrix/tensor type of numpy. Many numpy functions return arrays, not matrices.\n",
-    "* The array class is intended to be a general-purposen-dimensional array for many kinds of numerical computing, whilematrixis intended to facilitate linear algebracomputations specifically."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1-D arrays\n",
-    "* note his only has one demension\n",
-    "* we could use a method called reshape to change this (we will get into this more later on)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3,4,5,6])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(6,)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 2)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = np.reshape(a, (3,2))\n",
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2],\n",
-       "       [3, 4],\n",
-       "       [5, 6]])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[1,0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### indexing and slicing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3,4,5,6])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 3])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n",
-      "6\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in a:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### looping\n",
-    "* numpy loops through the left most axis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,4]\n",
-    "])\n",
-    "\n",
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 2 3]\n",
-      "[2 3 4]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# since we have 2 items in the first axis we will end up with 2 items in our iteration\n",
-    "for i in array:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,3,4,4],\n",
-    "    [2,3,4,4]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,7,4],\n",
-    "    [4,6,9,4],\n",
-    "    [2,3,4,4]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3, 4)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# depth 2\n",
-    "# rows 3\n",
-    "# columns 4\n",
-    "tensor = np.array([array, array1])\n",
-    "tensor.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* remember numpy will iterate left most index\n",
-    "* in this 3-d cube, it's iterating the dpeth\n",
-    "* so we end up printing our 2 initial matrices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3 4]\n",
-      " [2 3 4 4]\n",
-      " [2 3 4 4]] \n",
-      "\n",
-      "[[5 6 7 4]\n",
-      " [4 6 9 4]\n",
-      " [2 3 4 4]] \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in tensor:\n",
-    "    print(i, \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### performing matrix operations\n",
-    "* axis 0 is the y axis\n",
-    "* the up and down axis\n",
-    "* the number of rows\n",
-    "* so any operation done on axis 0 is going to be columns\n",
-    "* done up and down\n",
-    "* axis 1 is the length of our array\n",
-    "* any summing or anything is done across our dataframe\n",
-    "* doing some operation to our rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np \n",
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,3,4,4],\n",
-    "    [2,3,4,4]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A  B  C  D\n",
-       "0  1  2  3  4\n",
-       "1  2  3  4  4\n",
-       "2  2  3  4  4"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame(array)\n",
-    "df.columns = [\"A\", \"B\", \"C\", \"D\"]\n",
-    "df\n",
-    "#df.sum(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 5,  8, 11, 12])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "A     5\n",
-       "B     8\n",
-       "C    11\n",
-       "D    12\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.sum(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 4)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3, 4],\n",
-       "       [2, 3, 4, 4],\n",
-       "       [2, 3, 4, 4]])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert len(np.sum(array, axis = 0)) == array.shape[1], \"axis don't align\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 5,  8, 11, 12])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2., 3., 4., 4.])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.median(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2.5, 3.5, 3.5])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.median(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* the other option would be to loop which is far more costly of an operation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "13\n",
-      "13\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array:\n",
-    "    print(sum(i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3, 4],\n",
-       "       [2, 3, 4, 4],\n",
-       "       [2, 3, 4, 4]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 2],\n",
-       "       [2, 3, 3],\n",
-       "       [3, 4, 4],\n",
-       "       [4, 4, 4]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "8\n",
-      "11\n",
-      "12\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array.T:\n",
-    "    print(sum(i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3, 4],\n",
-       "       [2, 3, 4, 4],\n",
-       "       [2, 3, 4, 4]])"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### tranpose, turn rows into columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 2],\n",
-       "       [2, 3, 3],\n",
-       "       [3, 4, 4],\n",
-       "       [4, 4, 4]])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make a 2-d array in numpy:</h3>\n",
-    "<p style=\"color:blue\">- find the row wise mean</p>\n",
-    "<p style=\"color:blue\">- find the column wise mean</p>\n",
-    "<p style=\"color:blue\">- do the same for the transpose of the array you've made</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### other useful metrics\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.quantile.html\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1101, 2020, 3212],\n",
-       "       [2212, 3112, 4121]])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([555.5, 546. , 454.5])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1656.5, 2566. , 3666.5])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1101, 2020, 3212])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2212, 3112, 4121])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.max(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1656.5, 2566. , 3666.5])"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.quantile(array,.5, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1106.555, 2025.46 , 3216.545])"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.percentile(array,.5, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### reshape\n",
-    "* change the shape of your data\n",
-    "* adding indices\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# this is indexed by one element\n",
-    "array = np.array([1,2,3,4])\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4, 1)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.reshape(array, (4,1))\n",
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array[0][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we've now added two dimension using reshape\n",
-    "array1 = np.reshape(array,(2,2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2],\n",
-       "       [3, 4]])"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([1,2,3,4,5,6,7,8])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4, 5, 6, 7, 8])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array1 = np.reshape(array, (2,2,2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[1, 2],\n",
-       "        [3, 4]],\n",
-       "\n",
-       "       [[5, 6],\n",
-       "        [7, 8]]])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2, 2)"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2]\n",
-      " [3 4]] \n",
-      "\n",
-      "[[5 6]\n",
-      " [7 8]] \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array1:\n",
-    "    print(i, \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### norm of a vector\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.norm.html\n",
-    "* defaults to the l2 norm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.7416573867739413"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([1,2,3])\n",
-    "np.linalg.norm(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.7416573867739413"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sqrt(14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### matrix multiplication/dot product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2],\n",
-    "    [2,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6],\n",
-    "    [4,6]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# shortcut\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.matmul(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.dot(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### remember the dimension constraints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,2],\n",
-    "    [4,6,1]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3) (2, 3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(array.shape, array1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "The dimensions aren't aligned",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-48-fe46837ad8c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAssertionError\u001b[0m: The dimensions aren't aligned"
-     ]
-    }
-   ],
-   "source": [
-    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
-    "\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3) (3, 6)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[19, 27, 10, 17, 14, 21],\n",
-       "       [28, 39, 13, 25, 21, 28]])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5],\n",
-    "    [2,3,2,2,1,3]\n",
-    "])\n",
-    "\n",
-    "print(array.shape, array1.shape)\n",
-    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
-    "\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
-    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
-    "<p style=\"color:blue\">- tests to make sure they can be multipled using assert</p>\n",
-    "<p style=\"color:blue\">- return the dot product of the two matrices</p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### broadcasting\n",
-    "* expand the dimension to add to a matrix\n",
-    "* https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 1, 1, 1])"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,1],\n",
-    "    [2,3,3,1]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([1,1,1,1])\n",
-    "array1 = np.reshape(array1, (4,))\n",
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 3, 4, 2],\n",
-       "       [3, 4, 4, 2]])"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [2, 3, 3]])"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 1, 1]])"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 3, 4],\n",
-       "       [3, 4, 4]])"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.add(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [1, 2, 2]])"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.subtract(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 3, 4],\n",
-       "       [3, 4, 4]])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [1, 2, 2]])"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array - array1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### elementwise squaring\n",
-    "* this is the elementwise operations arrays that make arrays so powerful"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1,  32, 243],\n",
-       "       [ 32, 243, 243]])"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array**5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1,  32, 243],\n",
-       "       [ 32, 243, 243]])"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.power(array, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
-    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
-    "<p style=\"color:blue\">- iterates the rows and columns and squares each element</p>\n",
-    "<p style=\"color:blue\">- puts the squared items in a list</p>\n",
-    "<p style=\"color:blue\">- returns the list</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   0  1  2  3\n",
-       "0  1  2  3  1\n",
-       "1  2  3  3  1"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,1],\n",
-    "    [2,3,3,1]\n",
-    "])\n",
-    "\n",
-    "lst = [\n",
-    "    [1,2,3,1],\n",
-    "    [2,3,3,1]\n",
-    "]\n",
-    "\n",
-    "pd.DataFrame(lst)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([7, 9])"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = np.sum(lst, axis = 1)\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = x.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### argmax and min\n",
-    "* returns the index of the max/min value\n",
-    "* In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for user in range(100000000):\n",
-    "    \n",
-    "    for cluster in cluster in range(1000):\n",
-    "        \n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "user_matrix = []\n",
-    "\n",
-    "cluster_center_matrix = []\n",
-    "\n",
-    "distance matrix\n",
-    "\n",
-    "argsmix()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'C5'"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "feature = [\"C1\", \"C2\", \"C3\", \"C4\", \"C5\"]\n",
-    "array = np.array([1,2,3,4,15])\n",
-    "min_val = np.argmax(array)\n",
-    "feature[min_val]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmin(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3,4],\n",
-    "    [3,10,5,2],\n",
-    "    [15,8,9,2]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[2, 1, 2, 0]"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(np.argmax(array, axis = 0)) # column level"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 1, 0])"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(array, axis = 1) # row level"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### let's say these are distances\n",
-    "* each row is an item\n",
-    "* and each column is the distance to a cluster\n",
-    "array = np.array(["
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3],\n",
-    "    [3,10,5],\n",
-    "    [15,8,9]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 1])"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# this could then give us the index of the min distance\n",
-    "np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "items = [\"user_A\", \"user_B\", \"user_C\"]\n",
-    "clusters = [\"A\", \"B\", \"C\"]\n",
-    "closest_cluster_idx = np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 1])"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "closest_cluster_idx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['A', 'A', 'B']"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "closest_cluster_labels = [clusters[idx] for idx in closest_cluster_idx]\n",
-    "closest_cluster_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>user</th>\n",
-       "      <th>cluster</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>user_A</td>\n",
-       "      <td>A</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>user_B</td>\n",
-       "      <td>A</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>user_C</td>\n",
-       "      <td>B</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     user cluster\n",
-       "0  user_A       A\n",
-       "1  user_B       A\n",
-       "2  user_C       B"
-      ]
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "assign_df = pd.DataFrame(list(zip(items, closest_cluster_labels)))\n",
-    "assign_df.columns = [\"user\", \"cluster\"]\n",
-    "assign_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['user_A', 'A'],\n",
-       "       ['user_B', 'A'],\n",
-       "       ['user_C', 'B']], dtype='<U6')"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    items,\n",
-    "    closest_cluster_labels\n",
-    "]).T\n",
-    "\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['item_A', 'B'],\n",
-       "       ['item_B', 'B'],\n",
-       "       ['item_C', 'B']], dtype='<U6')"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    items,\n",
-    "    [clusters[idx] for idx in np.argmin(array, axis = 1)]\n",
-    "]).T\n",
-    "\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(np.array([1,1,2,2,5,5,5]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### argsort"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['A', 'B', 'C', 'D']"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(df.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([1,3,6,0,51,12])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sorted_args = np.argsort(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 0, 1, 2, 5, 4])"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted_args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3, 0, 1, 2, 5, 4])"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(df.columns)[idx] for idx in sorted_args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 3, 6, 12, 51]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[array[idx] for idx in sorted_args]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3],\n",
-    "    [3,10,5],\n",
-    "    [15,8,9]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "items = [\"item_A\", \"item_B\", \"item_C\"]\n",
-    "clusters = [\"A\", \"B\", \"C\"]\n",
-    "closest_cluster = np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1,  6,  3],\n",
-       "       [ 3, 10,  5],\n",
-       "       [15,  8,  9]])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 2, 1],\n",
-       "       [0, 2, 1],\n",
-       "       [1, 2, 0]])"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argsort(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def myfunc(x):\n",
-    "    items = [\"item_A\", \"item_B\", \"item_C\"]\n",
-    "    return items[x]\n",
-    "\n",
-    "vectorized_func = np.vectorize(myfunc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-150-86bed837003f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvectorized_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2089\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2090\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2091\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2092\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2093\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2165\u001b[0m                       for a in args]\n\u001b[1;32m   2166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2167\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2169\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-149-df07eceacbb6>\u001b[0m in \u001b[0;36mmyfunc\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmyfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"item_A\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"item_B\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"item_C\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvectorized_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvectorize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmyfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "vectorized_func(array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distance Measures\n",
-    "* used in many ML algorithms\n",
-    "* rec systems, kmeans, knn, decision trees\n",
-    "* https://docs.scipy.org/doc/scipy/reference/spatial.distance.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### euclidean distance\n",
-    "* magnitude makes a difference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3])\n",
-    "b = np.array([4,2,500])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "497.00905424348156"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sqrt(sum((a-b)**2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### cosine similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6415330278717848"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.dot(a, b)/(np.linalg.norm(a)*np.linalg.norm(b))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### jaccard coefficient"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [\"the\", \"old\", \"man\", \"by\", \"the\", \"sea\"]\n",
-    "b = [\"the\", \"old\", \"man\", \"by\", \"the\", \"chair\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def jaccard_similarity(a, b):\n",
-    "    return len(set(a).intersection(set(b))) / len(set(a).union(set(b)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6666666666666666"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "jaccard_similarity(a,b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### distance matrix\n",
-    "* pdist returns a reduced distance matrix, since they are symetrical\n",
-    "* squareform makes this a dense form"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.spatial.distance import pdist, squareform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,4,2,1],\n",
-    "    [2,3,4,5]\n",
-    "    \n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in array:\n",
-    "    for i2 in array:\n",
-    "        distance(i,i2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.        , 2.44948974, 2.82842712, 4.35889894],\n",
-       "       [2.44948974, 0.        , 2.44948974, 4.12310563],\n",
-       "       [2.82842712, 2.44948974, 0.        , 1.73205081],\n",
-       "       [4.35889894, 4.12310563, 1.73205081, 0.        ]])"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squareform(pdist(array.T, \"euclidean\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas\n",
-    "* data wrangling\n",
-    "* groupby\n",
-    "* extension of numpys to add a dataframe capability\n",
-    "* similar to R dataframes\n",
-    "* integrates with numpy\n",
-    "* https://pandas.pydata.org/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Series\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html\n",
-    "* One-dimensional ndarray with axis labels (including time series)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vals = [1,2,3,4,5]\n",
-    "idxs = [\"a\",\"b\",\"c\",\"d\",\"e\"]\n",
-    "\n",
-    "my_series = pd.Series(vals, idxs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1\n",
-       "b    2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 172,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_series[[\"a\",\"b\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    1\n",
-       "1    2\n",
-       "2    2\n",
-       "Name: A, dtype: int64"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"A\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1\n",
-       "b    2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_series[[\"a\", \"b\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dataframe\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\n",
-    "* core object in pandas\n",
-    "* 2 dimensional\n",
-    "* index and columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array1 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5],\n",
-    "    [2,3,2,2,1,3]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df.head(1)\n",
-    "#df.tail(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <td>4</td>\n",
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-       "    <tr>\n",
-       "      <td>2</td>\n",
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-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
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-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   0  1  2  3  4  5\n",
-       "0  5  6  2  5  3  2\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.columns = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  </tbody>\n",
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-      "text/plain": [
-       "   a  b  c  d  e  f\n",
-       "0  5  6  2  5  3  2\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
-      ]
-     },
-     "execution_count": 204,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "      <th>f</th>\n",
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-       "    <tr>\n",
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-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  c  d  e  f\n",
-       "b               \n",
-       "6  5  2  5  3  2\n",
-       "6  4  1  3  4  5\n",
-       "3  2  2  2  1  3"
-      ]
-     },
-     "execution_count": 205,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = df.set_index(\"b\")\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df\n",
-    "df_total = func(df)\n",
-    "df_total = func1(df_total)\n",
-    "df = func2(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  c  d  e  f\n",
-       "b               \n",
-       "6  5  2  5  3  2\n",
-       "6  4  1  3  4  5\n",
-       "3  2  2  2  1  3"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th></th>\n",
-       "      <th>aa</th>\n",
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-       "    <tr>\n",
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-       "      <td>1</td>\n",
-       "      <td>a</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>b</td>\n",
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-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  aa  bb\n",
-       "0  a   2\n",
-       "1  a   2\n",
-       "2  b   2"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = [\n",
-    "    [\"a\",2],\n",
-    "    [\"a\",2],\n",
-    "    [\"b\",2]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(array)\n",
-    "df.columns = [\"aa\", \"bb\"]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>a</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>b</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   aa\n",
-       "bb   \n",
-       "4   a\n",
-       "2   b"
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby(\"aa\").sum().reset_index().set_index(\"bb\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Int64Index([5, 4, 2], dtype='int64', name='a')"
-      ]
-     },
-     "execution_count": 187,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>e</th>\n",
-       "      <th>f</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
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-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   b  c  d  e  f\n",
-       "a               \n",
-       "5  6  2  5  3  2\n",
-       "4  6  1  3  4  5\n",
-       "2  3  2  2  1  3"
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### change headers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
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-       "      <th></th>\n",
-       "      <th>aa</th>\n",
-       "      <th>bb</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>b</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  aa  bb\n",
-       "0  a   2\n",
-       "1  a   2\n",
-       "2  b   2"
-      ]
-     },
-     "execution_count": 215,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Flavor/Scent'"
-      ]
-     },
-     "execution_count": 239,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"Flavor / Scent \".rstrip().replace(\" \", \"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.columns = [\"AfterHour\", \"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-233-7dd68b996720>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-233-7dd68b996720>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    df.`a.a.a`\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.a.a.a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    a\n",
-       "1    a\n",
-       "2    b\n",
-       "Name: a_a_a, dtype: object"
-      ]
-     },
-     "execution_count": 227,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.a_a_a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"10201020101\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"a\"] = df[\"a\"].apply(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert df.dtypes[\"b\"] == int, \"column must be integer\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### accessing columns\n",
-    "* returns series\n",
-    "* index still attached to the series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    5\n",
-       "1    4\n",
-       "2    2\n",
-       "Name: a, dtype: int64"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"a\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(df[\"a\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5, 4, 2]"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"a\"].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=3, step=1)"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"a\"].index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b\n",
-       "0  5  6\n",
-       "1  4  6\n",
-       "2  2  3"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[[\"a\", \"b\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### index subsetting\n",
-    "* iloc[] is position selecting\n",
-    "* iloc[rows, columns]\n",
-    "* rows/columns can be a list of indicies (integers) or use : to seperate a range\n",
-    "* [2:] means give me 2 and everything after for slicing\n",
-    "* [1] means give me index 1\n",
-    "* [1,2,3] means give me index 1,2,3\n",
-    "* [1],[0,1] means give me row 1 and columns 0 and 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    4\n",
-       "b    6\n",
-       "c    1\n",
-       "d    3\n",
-       "e    4\n",
-       "f    5\n",
-       "Name: 1, dtype: int64"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    4\n",
-       "b    6\n",
-       "c    1\n",
-       "Name: 1, dtype: int64"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# need integer based subsetting\n",
-    "df.iloc[1,[0,1,2]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "   a  b  c  d  e  f\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[[1,2]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
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-       "      <td>1</td>\n",
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-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c  d  e  f\n",
-       "0  5  6  2  5  3  2\n",
-       "2  2  3  2  2  1  3\n",
-       "1  4  6  1  3  4  5"
-      ]
-     },
-     "execution_count": 115,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[[0,2,1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
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-       "2  2  3  2  2  1  3"
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-    "df.iloc[[1],[0,1]]"
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-   "metadata": {},
-   "source": [
-    "#### loc\n",
-    "* used if we have labels and not integer indec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=3, step=1)"
-      ]
-     },
-     "execution_count": 119,
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-     "output_type": "execute_result"
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-    "df.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.index = [\"aa\", \"bb\", \"cc\"]"
-   ]
-  },
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-   "execution_count": 121,
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-       "    a  b  c  d  e  f\n",
-       "aa  5  6  2  5  3  2\n",
-       "bb  4  6  1  3  4  5\n",
-       "cc  2  3  2  2  1  3"
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-     "execution_count": 121,
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-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    5\n",
-       "b    6\n",
-       "c    2\n",
-       "d    5\n",
-       "e    3\n",
-       "f    2\n",
-       "Name: aa, dtype: int64"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.loc[\"aa\"]"
-   ]
-  },
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-   "execution_count": 123,
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-       "    a  b  c  d  e  f\n",
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-   "source": [
-    "df.loc[[\"aa\", \"bb\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
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-      "text/plain": [
-       "    a  b\n",
-       "aa  5  6"
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-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.loc[[\"aa\"], [\"a\",\"b\"]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">how might we</h3>\n",
-    "<p style=\"color:blue\">- filter if we have a large number of indices?</p>\n",
-    "<p style=\"color:blue\">- we don't want to type out a list of 1000s of index labels</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "      <td>bb</td>\n",
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-       "</div>"
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-      "text/plain": [
-       "    a  c\n",
-       "aa  5  2\n",
-       "bb  4  1"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rows = [\"aa\", \"bb\"]\n",
-    "cols = [\"a\", \"c\"]\n",
-    "\n",
-    "df.loc[rows,cols]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### subset for columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = df[[\"a\", \"b\", \"c\", \"d\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>2</td>\n",
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-      "text/plain": [
-       "   a  b  c  d\n",
-       "0  5  6  2  5\n",
-       "1  4  6  1  3\n",
-       "2  2  3  2  2"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### describe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>count</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>mean</td>\n",
-       "      <td>3.666667</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>1.666667</td>\n",
-       "      <td>3.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>std</td>\n",
-       "      <td>1.527525</td>\n",
-       "      <td>1.732051</td>\n",
-       "      <td>0.577350</td>\n",
-       "      <td>1.527525</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>min</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>25%</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>2.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>50%</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>75%</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>max</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              a         b         c         d\n",
-       "count  3.000000  3.000000  3.000000  3.000000\n",
-       "mean   3.666667  5.000000  1.666667  3.333333\n",
-       "std    1.527525  1.732051  0.577350  1.527525\n",
-       "min    2.000000  3.000000  1.000000  2.000000\n",
-       "25%    3.000000  4.500000  1.500000  2.500000\n",
-       "50%    4.000000  6.000000  2.000000  3.000000\n",
-       "75%    4.500000  6.000000  2.000000  4.000000\n",
-       "max    5.000000  6.000000  2.000000  5.000000"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### correlate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>a</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.944911</td>\n",
-       "      <td>-0.188982</td>\n",
-       "      <td>0.928571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>b</td>\n",
-       "      <td>0.944911</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>0.755929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>c</td>\n",
-       "      <td>-0.188982</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.188982</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>d</td>\n",
-       "      <td>0.928571</td>\n",
-       "      <td>0.755929</td>\n",
-       "      <td>0.188982</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          a         b         c         d\n",
-       "a  1.000000  0.944911 -0.188982  0.928571\n",
-       "b  0.944911  1.000000 -0.500000  0.755929\n",
-       "c -0.188982 -0.500000  1.000000  0.188982\n",
-       "d  0.928571  0.755929  0.188982  1.000000"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.corr()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### summations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    11\n",
-       "b    15\n",
-       "c     5\n",
-       "d    10\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.sum(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    18\n",
-       "1    14\n",
-       "2     9\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.sum(axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### median"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    4.0\n",
-       "b    6.0\n",
-       "c    2.0\n",
-       "d    3.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.median(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### new column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/conagrabrands/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n"
-     ]
-    }
-   ],
-   "source": [
-    "df1[\"class\"] = [\"AA\", \"AA\", \"BB\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>BB</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  c  d class\n",
-       "0  5  6  2  5    AA\n",
-       "1  4  6  1  3    AA\n",
-       "2  2  3  2  2    BB"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>AA</td>\n",
-       "      <td>9</td>\n",
-       "      <td>12</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>BB</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       a   b  c  d\n",
-       "class             \n",
-       "AA     9  12  3  8\n",
-       "BB     2   3  2  2"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>AA</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>BB</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       a  b  c  d\n",
-       "class            \n",
-       "AA     2  2  2  2\n",
-       "BB     1  1  1  1"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>AA</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>BB</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       a  b  c  d  class\n",
-       "class                   \n",
-       "AA     2  1  2  2      1\n",
-       "BB     1  1  1  1      1"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# count distinct\n",
-    "df1.groupby(\"class\").nunique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### reset index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['AA', 'BB'], dtype='object', name='class')"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").count().index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>AA</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>BB</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  class  a  b  c  d\n",
-       "0    AA  2  2  2  2\n",
-       "1    BB  1  1  1  1"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").count().reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RangeIndex(start=0, stop=2, step=1)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1.groupby(\"class\").count().reset_index().index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### reading in csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"data/iris.csv\"\n",
-    "df = pd.read_csv(path, sep = \",\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1          3.5           1.4          0.2  Setosa\n",
-       "1           4.9          3.0           1.4          0.2  Setosa\n",
-       "2           4.7          3.2           1.3          0.2  Setosa\n",
-       "3           4.6          3.1           1.5          0.2  Setosa\n",
-       "4           5.0          3.6           1.4          0.2  Setosa"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">read in the iris csv:</h3>\n",
-    "<p style=\"color:blue\">- find the descriptive statistics</p>\n",
-    "<p style=\"color:blue\">- create a correlation matrix </p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### subset on rules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(61, 5)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = df[df[\"sepal.length\"] > 6]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(23, 5)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = df[(df[\"sepal.length\"] > 6) & (df[\"sepal.width\"] > 3)]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(50, 5)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = df[df[\"variety\"] == \"Setosa\"]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### isin()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Setosa', 'Versicolor', 'Virginica'], dtype=object)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"variety\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(100, 5)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lst = [\"Setosa\", \"Versicolor\"]\n",
-    "\n",
-    "df1 = df[df[\"variety\"].isin(lst)]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### like"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(50, 5)"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = df[df[\"variety\"].str.contains(\"Versi\")]\n",
-    "df1.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### apply"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_func(x):\n",
-    "    return -x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1          3.5           1.4          0.2  Setosa"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(my_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>-5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>-4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>-4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>-4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0          -5.1          3.5           1.4          0.2  Setosa\n",
-       "1          -4.9          3.0           1.4          0.2  Setosa\n",
-       "2          -4.7          3.2           1.3          0.2  Setosa\n",
-       "3          -4.6          3.1           1.5          0.2  Setosa\n",
-       "4          -5.0          3.6           1.4          0.2  Setosa"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['sepal.length', 'sepal.width', 'petal.length', 'petal.width',\n",
-       "       'variety'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['sepal.length', 'sepal.width', 'petal.length', 'petal.width']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in cols:\n",
-    "    df[col] = df[col].apply(my_func)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>-3.5</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>-1.3</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>-3.1</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>-3.6</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1         -3.5          -1.4         -0.2  Setosa\n",
-       "1           4.9         -3.0          -1.4         -0.2  Setosa\n",
-       "2           4.7         -3.2          -1.3         -0.2  Setosa\n",
-       "3           4.6         -3.1          -1.5         -0.2  Setosa\n",
-       "4           5.0         -3.6          -1.4         -0.2  Setosa"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(lambda x: -x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>-5.1</td>\n",
-       "      <td>-3.5</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>-4.9</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>-4.7</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>-1.3</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>-4.6</td>\n",
-       "      <td>-3.1</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>-5.0</td>\n",
-       "      <td>-3.6</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>Setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0          -5.1         -3.5          -1.4         -0.2  Setosa\n",
-       "1          -4.9         -3.0          -1.4         -0.2  Setosa\n",
-       "2          -4.7         -3.2          -1.3         -0.2  Setosa\n",
-       "3          -4.6         -3.1          -1.5         -0.2  Setosa\n",
-       "4          -5.0         -3.6          -1.4         -0.2  Setosa"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### datatypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sepal.length    float64\n",
-       "sepal.width     float64\n",
-       "petal.length    float64\n",
-       "petal.width     float64\n",
-       "variety          object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(str)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sepal.length     object\n",
-       "sepal.width     float64\n",
-       "petal.length    float64\n",
-       "petal.width     float64\n",
-       "variety          object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### get dummies\n",
-    "* for string or object variables\n",
-    "* can specify specific columns\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">what might we need dummies for?</h3>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.get_dummies(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "      <th>variety_Setosa</th>\n",
-       "      <th>variety_Versicolor</th>\n",
-       "      <th>variety_Virginica</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width  variety_Setosa  \\\n",
-       "0           5.1          3.5           1.4          0.2               1   \n",
-       "1           4.9          3.0           1.4          0.2               1   \n",
-       "2           4.7          3.2           1.3          0.2               1   \n",
-       "3           4.6          3.1           1.5          0.2               1   \n",
-       "4           5.0          3.6           1.4          0.2               1   \n",
-       "\n",
-       "   variety_Versicolor  variety_Virginica  \n",
-       "0                   0                  0  \n",
-       "1                   0                  0  \n",
-       "2                   0                  0  \n",
-       "3                   0                  0  \n",
-       "4                   0                  0  "
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>BB</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>CC</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1 col_2\n",
-       "0     A    AA\n",
-       "1     B    BB\n",
-       "2     C    CC"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col_1 = np.array([\"A\", \"B\", \"C\"])\n",
-    "col_2 = np.array([\"AA\", \"BB\", \"CC\"])\n",
-    "\n",
-    "df = pd.DataFrame({\n",
-    "    \"col_1\":col_1,\n",
-    "    \"col_2\":col_2\n",
-    "})\n",
-    "\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">perform get dummies on col_1 only?</h3>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### iterate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 sepal.length          5.1\n",
-      "sepal.width           3.5\n",
-      "petal.length          1.4\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 0, dtype: float64\n",
-      "1 sepal.length          4.9\n",
-      "sepal.width           3.0\n",
-      "petal.length          1.4\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 1, dtype: float64\n",
-      "2 sepal.length          4.7\n",
-      "sepal.width           3.2\n",
-      "petal.length          1.3\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 2, dtype: float64\n",
-      "3 sepal.length          4.6\n",
-      "sepal.width           3.1\n",
-      "petal.length          1.5\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 3, dtype: float64\n",
-      "4 sepal.length          5.0\n",
-      "sepal.width           3.6\n",
-      "petal.length          1.4\n",
-      "petal.width           0.2\n",
-      "variety_Setosa        1.0\n",
-      "variety_Versicolor    0.0\n",
-      "variety_Virginica     0.0\n",
-      "Name: 4, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "for idx, r in df.head(5).iterrows():\n",
-    "    print(idx, r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.4\n",
-      "1.4\n",
-      "1.3\n",
-      "1.5\n",
-      "1.4\n"
-     ]
-    }
-   ],
-   "source": [
-    "for idx, r in df.head(5).iterrows():\n",
-    "    print(r[\"petal.length\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.4,\n",
-       " 0.3,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.1,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.1,\n",
-       " 0.1,\n",
-       " 0.2,\n",
-       " 0.4,\n",
-       " 0.4,\n",
-       " 0.3,\n",
-       " 0.3,\n",
-       " 0.3,\n",
-       " 0.2,\n",
-       " 0.4,\n",
-       " 0.2,\n",
-       " 0.5,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.4,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.4,\n",
-       " 0.1,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.1,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.3,\n",
-       " 0.3,\n",
-       " 0.2,\n",
-       " 0.6,\n",
-       " 0.4,\n",
-       " 0.3,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 0.2,\n",
-       " 1.4,\n",
-       " 1.5,\n",
-       " 1.5,\n",
-       " 1.3,\n",
-       " 1.5,\n",
-       " 1.3,\n",
-       " 1.6,\n",
-       " 1.0,\n",
-       " 1.3,\n",
-       " 1.4,\n",
-       " 1.0,\n",
-       " 1.5,\n",
-       " 1.0,\n",
-       " 1.4,\n",
-       " 1.3,\n",
-       " 1.4,\n",
-       " 1.5,\n",
-       " 1.0,\n",
-       " 1.5,\n",
-       " 1.1,\n",
-       " 1.8,\n",
-       " 1.3,\n",
-       " 1.5,\n",
-       " 1.2,\n",
-       " 1.3,\n",
-       " 1.4,\n",
-       " 1.4,\n",
-       " 1.7,\n",
-       " 1.5,\n",
-       " 1.0,\n",
-       " 1.1,\n",
-       " 1.0,\n",
-       " 1.2,\n",
-       " 1.6,\n",
-       " 1.5,\n",
-       " 1.6,\n",
-       " 1.5,\n",
-       " 1.3,\n",
-       " 1.3,\n",
-       " 1.3,\n",
-       " 1.2,\n",
-       " 1.4,\n",
-       " 1.2,\n",
-       " 1.0,\n",
-       " 1.3,\n",
-       " 1.2,\n",
-       " 1.3,\n",
-       " 1.3,\n",
-       " 1.1,\n",
-       " 1.3,\n",
-       " 2.5,\n",
-       " 1.9,\n",
-       " 2.1,\n",
-       " 1.8,\n",
-       " 2.2,\n",
-       " 2.1,\n",
-       " 1.7,\n",
-       " 1.8,\n",
-       " 1.8,\n",
-       " 2.5,\n",
-       " 2.0,\n",
-       " 1.9,\n",
-       " 2.1,\n",
-       " 2.0,\n",
-       " 2.4,\n",
-       " 2.3,\n",
-       " 1.8,\n",
-       " 2.2,\n",
-       " 2.3,\n",
-       " 1.5,\n",
-       " 2.3,\n",
-       " 2.0,\n",
-       " 2.0,\n",
-       " 1.8,\n",
-       " 2.1,\n",
-       " 1.8,\n",
-       " 1.8,\n",
-       " 1.8,\n",
-       " 2.1,\n",
-       " 1.6,\n",
-       " 1.9,\n",
-       " 2.0,\n",
-       " 2.2,\n",
-       " 1.5,\n",
-       " 1.4,\n",
-       " 2.3,\n",
-       " 2.4,\n",
-       " 1.8,\n",
-       " 1.8,\n",
-       " 2.1,\n",
-       " 2.4,\n",
-       " 2.3,\n",
-       " 1.9,\n",
-       " 2.3,\n",
-       " 2.5,\n",
-       " 2.3,\n",
-       " 1.9,\n",
-       " 2.0,\n",
-       " 2.3,\n",
-       " 1.8]"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"petal.width\"].tolist()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Using iris, group by variety and find the count of the other columns?</h3>\n",
-    "<h3 style=\"color:blue\">Using iris, group by variety and find the sum of the other columns?</h3>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_1_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>E</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_1_ind\n",
-       "0     A          1\n",
-       "1     B          1\n",
-       "2     C          1\n",
-       "3     D          1\n",
-       "4     E          1"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col_1 = np.array([\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
-    "col_2 = np.array([\"A\", \"B\", \"C\"])\n",
-    "\n",
-    "df = pd.DataFrame({\n",
-    "    \"col_1\":col_1,\n",
-    "    \"col_1_ind\":1\n",
-    "})\n",
-    "\n",
-    "df1 = pd.DataFrame({\n",
-    "    \"col_1\":col_2,\n",
-    "    \"col_2_ind\":1\n",
-    "})\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_2_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_2_ind\n",
-       "0     A          1\n",
-       "1     B          1\n",
-       "2     C          1"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### concat\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/conagrabrands/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
-      "of pandas will change to not sort by default.\n",
-      "\n",
-      "To accept the future behavior, pass 'sort=False'.\n",
-      "\n",
-      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
-      "\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_1_ind</th>\n",
-       "      <th>col_2_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>E</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_1_ind  col_2_ind\n",
-       "0     A        1.0        NaN\n",
-       "1     B        1.0        NaN\n",
-       "2     C        1.0        NaN\n",
-       "3     D        1.0        NaN\n",
-       "4     E        1.0        NaN\n",
-       "0     A        NaN        1.0\n",
-       "1     B        NaN        1.0\n",
-       "2     C        NaN        1.0"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([df, df1], axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### merge\n",
-    "* https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.merge.html\n",
-    "* assumes inner join"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "select *\n",
-    "from df inner join df2\n",
-    "    on df.col_1 = df2.col_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_1_ind</th>\n",
-       "      <th>col_2_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_1_ind  col_2_ind\n",
-       "0     A          1          1\n",
-       "1     B          1          1\n",
-       "2     C          1          1"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.merge(df1, how = \"inner\", left_on = \"col_1\", right_on = \"col_1\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "select *\n",
-    "from df left join df2\n",
-    "    on df.col_1 = df2.col_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>col_1</th>\n",
-       "      <th>col_1_ind</th>\n",
-       "      <th>col_2_ind</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>B</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>D</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>E</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  col_1  col_1_ind  col_2_ind\n",
-       "0     A          1        1.0\n",
-       "1     B          1        1.0\n",
-       "2     C          1        1.0\n",
-       "3     D          1        NaN\n",
-       "4     E          1        NaN"
-      ]
-     },
-     "execution_count": 141,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.merge(df1, how = \"left\", left_on = \"col_1\", right_on = \"col_1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Imputation\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.interpolate.html#pandas.DataFrame.interpolate\n",
-    "* missing values\n",
-    "* When summing data, NA (missing) values will be treated as zero.\n",
-    "* If the data are all NA, the result will be 0.\n",
-    "* Cumulative methods like cumsum() and cumprod() ignore NA values by default, but preserve them in the resulting arrays. To override this behaviour and include NA values, use skipna=False.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "data = [1,2,3,4,None,5,2,1]\n",
-    "df = pd.DataFrame(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0\n",
-       "0  1.0\n",
-       "1  2.0\n",
-       "2  3.0\n",
-       "3  4.0\n",
-       "4  NaN\n",
-       "5  5.0\n",
-       "6  2.0\n",
-       "7  1.0"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       0\n",
-       "0  False\n",
-       "1  False\n",
-       "2  False\n",
-       "3  False\n",
-       "4   True\n",
-       "5  False\n",
-       "6  False\n",
-       "7  False"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       0\n",
-       "0   True\n",
-       "1   True\n",
-       "2   True\n",
-       "3   True\n",
-       "4  False\n",
-       "5   True\n",
-       "6   True\n",
-       "7   True"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.notna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0\n",
-       "0  1.0\n",
-       "1  2.0\n",
-       "2  3.0\n",
-       "3  4.0\n",
-       "4  0.0\n",
-       "5  5.0\n",
-       "6  2.0\n",
-       "7  1.0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th></th>\n",
-       "      <th>0</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       0\n",
-       "0    1.0\n",
-       "1    2.0\n",
-       "2    3.0\n",
-       "3    4.0\n",
-       "4  100.0\n",
-       "5    5.0\n",
-       "6    2.0\n",
-       "7    1.0"
-      ]
-     },
-     "execution_count": 8,
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-       "   index         0\n",
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-     },
-     "execution_count": 20,
-     "metadata": {},
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-    }
-   ],
-   "source": [
-    "df[0].fillna(df[0].mean()).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Json data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"/Users/conagrabrands/Documents/PythonForAnalytics/Lectures/data/ca.json\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[\n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Toronto\", \n",
-      "\n",
-      "        \"admin\": \"Ontario\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"3934421\", \n",
-      "\n",
-      "        \"iso2\": \"CA\", \n",
-      "\n",
-      "        \"capital\": \"admin\", \n",
-      "\n",
-      "        \"lat\": \"43.666667\", \n",
-      "\n",
-      "        \"lng\": \"-79.416667\", \n",
-      "\n",
-      "        \"population\": \"5213000\"\n",
-      "\n",
-      "    }, \n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Montr\\u00e9al\", \n",
-      "\n",
-      "        \"admin\": \"Qu\\u00e9bec\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"2356556\", \n",
-      "\n",
-      "        \"iso2\": \"CA\", \n",
-      "\n",
-      "        \"capital\": \"\", \n",
-      "\n",
-      "        \"lat\": \"45.5\", \n",
-      "\n",
-      "        \"lng\": \"-73.583333\", \n",
-      "\n",
-      "        \"population\": \"3678000\"\n",
-      "\n",
-      "    }, \n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Vancouver\", \n",
-      "\n",
-      "        \"admin\": \"British Columbia\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"603502\", \n",
-      "\n",
-      "        \"iso2\": \"CA\", \n",
-      "\n",
-      "        \"capital\": \"\", \n",
-      "\n",
-      "        \"lat\": \"49.25\", \n",
-      "\n",
-      "        \"lng\": \"-123.133333\", \n",
-      "\n",
-      "        \"population\": \"2313328\"\n",
-      "\n",
-      "    }, \n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Ottawa\", \n",
-      "\n",
-      "        \"admin\": \"Ontario\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"812129\", \n",
-      "\n",
-      "        \"iso2\": \"CA\", \n",
-      "\n",
-      "        \"capital\": \"primary\", \n",
-      "\n",
-      "        \"lat\": \"45.416667\", \n",
-      "\n",
-      "        \"lng\": \"-75.7\", \n",
-      "\n",
-      "        \"population\": \"1145000\"\n",
-      "\n",
-      "    }, \n",
-      "\n",
-      "    {\n",
-      "\n",
-      "        \"city\": \"Calgary\", \n",
-      "\n",
-      "        \"admin\": \"Alberta\", \n",
-      "\n",
-      "        \"country\": \"Canada\", \n",
-      "\n",
-      "        \"population_proper\": \"915322\", \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(path, \"r\") as file:\n",
-    "    line = file.readlines()\n",
-    "    for l in line[0:50]:\n",
-    "        print(l)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "{\n",
-    "    \"brian\" {\n",
-    "        \"age\":31,\n",
-    "        \"job\":data scientist,\n",
-    "        \"city\"chicago\n",
-    "        },\n",
-    "        \n",
-    "   \"james\":{\n",
-    "        \"age\":28,\n",
-    "        \"job\":data scientist,\n",
-    "        \"city\"new york\n",
-    "        }\n",
-    "        \n",
-    "     \"james\":{\n",
-    "        \"age\":28,\n",
-    "        }\n",
-    "    \n",
-    "    \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "columns = [\"age\", \"job\", \"city\"]\n",
-    "data = [\n",
-    "    [31, \"data scientist\", \"chicago\"],\n",
-    "    [28, \"data scientist\", \"new york\"],\n",
-    "    [28,None,None]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data, columns = columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_json(path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html\n",
-    "* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.json.json_normalize.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* Nested JSON files can be time consuming and difficult process to flatten and load into Pandas.\n",
-    "* Let’s unpack the works column into a standalone dataframe. We’ll also grab the flat columns.\n",
-    "* nycphil = json_normalize(d['programs'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = [\n",
-    "        {'id': 1, 'name': \"Cole Volk\",'fitness': \n",
-    "             {'height': 130, 'weight': 60}},\n",
-    "    \n",
-    "        {'name': \"Mose Reg\",'fitness': \n",
-    "             {'height': 130, 'weight': 60}},\n",
-    "    \n",
-    "        {'id': 2, 'name': 'Faye Raker','fitness': \n",
-    "             {'height': 130, 'weight': 60}}\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas.io.json import json_normalize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <td>130</td>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Mose Reg</td>\n",
-       "      <td>130</td>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Faye Raker</td>\n",
-       "      <td>130</td>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id        name  fitness.height  fitness.weight\n",
-       "0  1.0   Cole Volk             130              60\n",
-       "1  NaN    Mose Reg             130              60\n",
-       "2  2.0  Faye Raker             130              60"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>name</th>\n",
-       "      <th>fitness</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Cole Volk</td>\n",
-       "      <td>{'height': 130, 'weight': 60}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Mose Reg</td>\n",
-       "      <td>{'height': 130, 'weight': 60}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Faye Raker</td>\n",
-       "      <td>{'height': 130, 'weight': 60}</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id        name                        fitness\n",
-       "0  1.0   Cole Volk  {'height': 130, 'weight': 60}\n",
-       "1  NaN    Mose Reg  {'height': 130, 'weight': 60}\n",
-       "2  2.0  Faye Raker  {'height': 130, 'weight': 60}"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data, max_level = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = [\n",
-    "        {'state': 'Florida','shortname': 'FL',\n",
-    "            'info': {'governor': 'Rick Scott', \"gender\":\"m\"},\n",
-    "            'counties': [{'name': 'Dade', 'population': 12345},\n",
-    "                         {'name': 'Broward', 'population': 40000},\n",
-    "                         {'name': 'Palm Beach', 'population': 60000}]},\n",
-    "    \n",
-    "         {'state': 'Ohio', 'shortname': 'OH',\n",
-    "          'info': {'governor': 'John Kasich', \"gender\":\"m\"},\n",
-    "          'counties': [{'name': 'Summit', 'population': 1234},\n",
-    "                       {'name': 'Cuyahoga', 'population': 1337}]}\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>shortname</th>\n",
-       "      <th>info</th>\n",
-       "      <th>counties</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Florida</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>{'governor': 'Rick Scott', 'gender': 'm'}</td>\n",
-       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Ohio</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>{'governor': 'John Kasich', 'gender': 'm'}</td>\n",
-       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     state shortname                                        info  \\\n",
-       "0  Florida        FL   {'governor': 'Rick Scott', 'gender': 'm'}   \n",
-       "1     Ohio        OH  {'governor': 'John Kasich', 'gender': 'm'}   \n",
-       "\n",
-       "                                            counties  \n",
-       "0  [{'name': 'Dade', 'population': 12345}, {'name...  \n",
-       "1  [{'name': 'Summit', 'population': 1234}, {'nam...  "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data, max_level = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>shortname</th>\n",
-       "      <th>counties</th>\n",
-       "      <th>info.governor</th>\n",
-       "      <th>info.gender</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Florida</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Ohio</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
-       "      <td>John Kasich</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     state shortname                                           counties  \\\n",
-       "0  Florida        FL  [{'name': 'Dade', 'population': 12345}, {'name...   \n",
-       "1     Ohio        OH  [{'name': 'Summit', 'population': 1234}, {'nam...   \n",
-       "\n",
-       "  info.governor info.gender  \n",
-       "0    Rick Scott           m  \n",
-       "1   John Kasich           m  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data, max_level = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>shortname</th>\n",
-       "      <th>counties</th>\n",
-       "      <th>info.governor</th>\n",
-       "      <th>info.gender</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Florida</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Ohio</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
-       "      <td>John Kasich</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     state shortname                                           counties  \\\n",
-       "0  Florida        FL  [{'name': 'Dade', 'population': 12345}, {'name...   \n",
-       "1     Ohio        OH  [{'name': 'Summit', 'population': 1234}, {'nam...   \n",
-       "\n",
-       "  info.governor info.gender  \n",
-       "0    Rick Scott           m  \n",
-       "1   John Kasich           m  "
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json_normalize(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>info.governor</th>\n",
-       "      <th>info.gender</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>Dade</td>\n",
-       "      <td>12345</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>Broward</td>\n",
-       "      <td>40000</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>Palm Beach</td>\n",
-       "      <td>60000</td>\n",
-       "      <td>Rick Scott</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>Summit</td>\n",
-       "      <td>1234</td>\n",
-       "      <td>John Kasich</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>Cuyahoga</td>\n",
-       "      <td>1337</td>\n",
-       "      <td>John Kasich</td>\n",
-       "      <td>m</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         name  population info.governor info.gender\n",
-       "0        Dade       12345    Rick Scott           m\n",
-       "1     Broward       40000    Rick Scott           m\n",
-       "2  Palm Beach       60000    Rick Scott           m\n",
-       "3      Summit        1234   John Kasich           m\n",
-       "4    Cuyahoga        1337   John Kasich           m"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# make each row a county, then start parsing data as such\n",
-    "json_normalize(data, \"counties\", [[\"info\",\"governor\"], [\"info\", \"gender\"]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chunks\n",
-    "* Can use chunks to process pieces of a dataframe at a time if it won't fit into memory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_size = 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"/Users/conagrabrands/Documents/PythonForAnalytics/Lectures/data/iris.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 5)\n",
-      "(50, 5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for c in pd.read_csv(path, chunksize = chunk_size):\n",
-    "    print(c.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 4)\n",
-      "(1, 4)\n"
-     ]
-    }
-   ],
-   "source": [
-    "for c in pd.read_csv(path, chunksize = chunk_size):\n",
-    "    df = c.groupby(\"variety\").sum()\n",
-    "    print(df.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lectures/Lec8.ipynb.txt b/lectures/Lec8.ipynb.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/lectures/Lecture 10 - Recursion.ipynb b/lectures/Lecture 10 - Recursion.ipynb
new file mode 100644
index 0000000..065dc19
--- /dev/null
+++ b/lectures/Lecture 10 - Recursion.ipynb	
@@ -0,0 +1,595 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recursion\n",
+    "\n",
+    "Recursion is a mind-bending but fun a powerful concept. What happens if a function calls...itself?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_list = [1,2,3,4,5,6,7,8,9,10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_list_normally(lst):\n",
+    "    return sum(lst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_list_normally(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_list_loop(lst):\n",
+    "    sum_ = 0\n",
+    "    for e in lst:\n",
+    "        sum_ += e\n",
+    "    return sum_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_list_loop(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_list_recursively(lst):\n",
+    "    if len(lst) == 0: return 0\n",
+    "    return lst[0] + add_list_recursively(lst[1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_list_recursively(my_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### ... WHAT??\n",
+    "\n",
+    "### Real-world recursion\n",
+    "\n",
+    "#### Opposing mirrors\n",
+    "![](images/infinitemirror.jpg)\n",
+    "\n",
+    "#### Feedback loops\n",
+    "![](images/how-to-control-feedback-in-a-sound-system_header.jpg)\n",
+    "\n",
+    "#### Movies\n",
+    "![](images/inception.jpg)\n",
+    "\n",
+    "#### Nerd joke: Google \"recursion\"\n",
+    "\n",
+    "#### Not a nerd joke:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def blowup(): \n",
+    "    blowup()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-8-ea38bd27bcd1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-7-353df51d9689>\u001b[0m in \u001b[0;36mblowup\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[1;32m<ipython-input-7-353df51d9689>\u001b[0m in \u001b[0;36mblowup\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
+     ]
+    }
+   ],
+   "source": [
+    "blowup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Why did this function blow up?\n",
+    "\n",
+    "Recall the following example from an earlier lecture:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error_func(filename):\n",
+    "    for line in open(filename, 'r').readlines():\n",
+    "        wont_even_get_here = line.split(\",\")\n",
+    "\n",
+    "def error_func2(filename):\n",
+    "    error_func(filename)\n",
+    "\n",
+    "def error_func3(filename):\n",
+    "    error_func2(filename)\n",
+    "\n",
+    "def error_func4(filename):\n",
+    "    error_func3(filename)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'this_file_doesnt_exist.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-f3090ea13960>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0merror_func4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"this_file_doesnt_exist.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-21-3d87cffa593b>\u001b[0m in \u001b[0;36merror_func4\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0merror_func3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-21-3d87cffa593b>\u001b[0m in \u001b[0;36merror_func3\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0merror_func2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-21-3d87cffa593b>\u001b[0m in \u001b[0;36merror_func2\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0merror_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-21-3d87cffa593b>\u001b[0m in \u001b[0;36merror_func\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m         \u001b[0mwont_even_get_here\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror_func2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'this_file_doesnt_exist.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "error_func4(\"this_file_doesnt_exist.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python not only told us that the file wasn't found, but it remembered the full history of how we arrived at the line which caused the error. This _history_ is called the _call stack_. Most languages have similar call stacks and they serve serveral purposes, one of them being to keep track of which function calls led us to our current location in the executing program.\n",
+    "\n",
+    "In the exaple above, we are four functions deep when the error occurs. What if we were 100 or 10k or a million functions deep? The call stack would have to remember all of thos functions.\n",
+    "\n",
+    "What happens when the function `blowup` keeps calling itself? The stack keeps growing, until it runs out of space!\n",
+    "\n",
+    "As you saw, Python terminates the function after a number of recursions, some languages keep going until they _blow their stack_."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Ask** What is this error called in Java?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rules of recursion\n",
+    "\n",
+    "There are two rules of safe recursion:\n",
+    "1. Always handle the base case\n",
+    "2. Each recursion must move computation towards the base case\n",
+    "\n",
+    "Notice in the example above:\n",
+    "\n",
+    "```python\n",
+    "def add_list_recursively(lst):\n",
+    "    if len(lst) == 0: return 0\n",
+    "    return lst[0] + add_list_recursively(lst[1:])\n",
+    "```\n",
+    "\n",
+    "When the function is called recursively, it isn't called on the whole list, it is called on _the whole list, minus one item_: `add_list_recursively(lst[1:])`. This means that each recursion call reduces the size of the list it is processing. Logically, the first line in the funciton handle the case when there are no more items to process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Processing recursive data structures\n",
+    "\n",
+    "Since this is an intro class, we haven't covered many advanced data structures. However, lets take a brief look at a binary tree:\n",
+    "![](images/binary_tree.svg)\n",
+    "\n",
+    "Such trees are extremely popular and useful in Computer Science and AI. Many games use similar trees to find best paths for their agents, optimization algorithms use similar trees to reduce the amount of computation which needs to be done, programmers use such trees to build data structures such as dictionaries!\n",
+    "\n",
+    "Here is an example of how such a tree might be represented in Python:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tree = [2,\n",
+    "        [7,\n",
+    "             [2, [], []],\n",
+    "             [6,\n",
+    "                  [5, [], []],\n",
+    "                  [11, [], []]\n",
+    "             ]\n",
+    "        ],\n",
+    "        [5,\n",
+    "             [],\n",
+    "             [9,\n",
+    "                  [4, [], []],\n",
+    "                  []\n",
+    "             ]\n",
+    "        ]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2,\n",
+       " [7, [2, [], []], [6, [5, [], []], [11, [], []]]],\n",
+       " [5, [], [9, [4, [], []], []]]]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([2, 7, 2, 6, 5, 11, 5, 9, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'int' and 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-14-34f92fb309c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtree\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "sum(tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Some algorithms are _much_ easier to implement via recursion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tree_sum(t):\n",
+    "    # add the root, left tree and right tree\n",
+    "    if len(t) == 0: return 0\n",
+    "    return t[0] + tree_sum(t[1]) + tree_sum(t[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tree_sum(tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Count children"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count_children(t):\n",
+    "    if len(t) == 0: return ([], 0)\n",
+    "    else:\n",
+    "        left_val, left_children = count_children(t[1])\n",
+    "        right_val, right_children = count_children(t[2])\n",
+    "        children = 1 + left_children + right_children\n",
+    "        print(t[0], children-1)\n",
+    "        return (t[0], children)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 0\n",
+      "5 0\n",
+      "11 0\n",
+      "6 2\n",
+      "7 4\n",
+      "4 0\n",
+      "9 1\n",
+      "5 2\n",
+      "2 8\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(2, 9)"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count_children(tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Print a tree, recursively"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_tree(t, indent=0):\n",
+    "    if len(t) > 0:\n",
+    "        print(\"-\" * indent, t[0])\n",
+    "        print_tree(t[1], indent+1)\n",
+    "        print_tree(t[2], indent+1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 2\n",
+      "- 7\n",
+      "-- 2\n",
+      "-- 6\n",
+      "--- 5\n",
+      "--- 11\n",
+      "- 5\n",
+      "-- 9\n",
+      "--- 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_tree(tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More on recursion\n",
+    "\n",
+    "![](images/david_chang.jpg)\n",
+    "\n",
+    "> And recently I started seeing patterns in our most successful dishes that suggested our hits weren’t entirely random; there’s a set of underlying laws that links them together. I’ve struggled to put this into words, and I haven’t talked to my fellow chefs about it, because I worry they’ll think I’m crazy. But I think there’s something to it, and so I’m sharing it now for the first time. I call it the Unified Theory of Deliciousness.\n",
+    "\n",
+    "> This probably sounds absolutely ridiculous, but the theory is rooted in a class I took in college called Advanced Logic. A philosopher named Howard DeLong taught it; he wrote one of the books that directly inspired Douglas Hofstadter to write [Gödel, Escher, Bach](https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567). The first day, he said, “This class will change your life,” and I was like, “What kind of asshole is this?” But he was right. I would never pretend to be an expert in logic, and I never made it all the way through Gödel, Escher, Bach. But the ideas and concepts I took away from that class have haunted me ever since.\n",
+    "\n",
+    "Gödel, Escher, Bach\n",
+    "\n",
+    "Wikipedia article: https://en.wikipedia.org/wiki/G%C3%B6del,_Escher,_Bach\n",
+    "\n",
+    "Book: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567\n",
+    "\n",
+    "MIT lectures: https://ocw.mit.edu/high-school/humanities-and-social-sciences/godel-escher-bach/video-lectures/\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise** Write a recursive function which multiplies numbers in a list (similar to the summing function above)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise** Write a recursive function which multplies all values in a tree by 2, then print those values (just print them as a list of numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reference\n",
+    "\n",
+    "Elevator picture: http://steve-patterson.com/logic-and-infinity/\n",
+    "\n",
+    "Feedback Loop: https://www.shure.com/en-US/performance-production/louder/how-to-control-feedback-in-a-sound-system\n",
+    "\n",
+    "Inception poster: https://www.amazon.com/Posters-USA-Inception-Poster-GLOSSY/dp/B01MRP0KEW/\n",
+    "\n",
+    "Binary tree: https://en.wikipedia.org/wiki/File:Binary_tree.svg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lectures/Lecture 10 - RegExp.ipynb b/lectures/Lecture 10 - RegExp.ipynb
new file mode 100644
index 0000000..605a820
--- /dev/null
+++ b/lectures/Lecture 10 - RegExp.ipynb	
@@ -0,0 +1,324 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regular Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Regular expressions are a mini-language, used to parse and extract information from strings.\n",
+    "\n",
+    "### Motivation: slicing vs split vs regex\n",
+    "\n",
+    "Given a strings, such as:\n",
+    "\n",
+    "\"01/09/2008\", \"05/12/2012\"\n",
+    "\n",
+    "we know we can get extract the year this way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2008\n",
+      "2012\n"
+     ]
+    }
+   ],
+   "source": [
+    "dates = [\"01/09/2008\", \"05/12/2012\"]\n",
+    "\n",
+    "for d in dates:\n",
+    "    print(d[-4:]) # use normal indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we had a strings, such as:\n",
+    "\n",
+    "\"In the year 2008 we did such as such\"\n",
+    "\"After the year 2009 we continued something else\"\n",
+    "\n",
+    "We can no longer use slicing, but we can just split the string and get the 4th value to get the year:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2008\n",
+      "2009\n"
+     ]
+    }
+   ],
+   "source": [
+    "sentences = [\"In the year 2008 we did such as such\"\n",
+    "             , \"After the year 2009 we continued something else\"]\n",
+    "\n",
+    "for s in sentences:\n",
+    "    print(s.split(\" \")[3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How do we extract dates in the following sentences?\n",
+    "\n",
+    "\"2019: After the Fall of New York\"\n",
+    "\n",
+    "\"The exterminators of the year 3000\"\n",
+    "\n",
+    "\"1990: The Bronx Warriors\"\n",
+    "\n",
+    "The first inclination of novice programmers would be to split the movie title above, go through each title and check to see if it is just numbers. If it is, extract that token as the year.\n",
+    "\n",
+    "This pattern of coding comes up so often that there is a special way of extracting such information: regular expressions!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2019\n",
+      "3000\n",
+      "1990\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re # <= regular expression library\n",
+    "\n",
+    "movies = [\"2019: After the Fall of New York\"\n",
+    "          , \"The exterminators of the year 3000\"\n",
+    "          , \"1990: The Bronx Warriors\"]\n",
+    "\n",
+    "for m in movies:\n",
+    "    print(re.search(\"(\\d\\d\\d\\d)\", m).group(0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**...what??**\n",
+    "\n",
+    "Some people don't like regular expressions:\n",
+    "\n",
+    "> Some people, when confronted with a problem, think\n",
+    "“I know, I'll use regular expressions.”   Now they have two problems.\n",
+    "\n",
+    "\n",
+    "- Jamie Zawinski"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regular expressions in context\n",
+    "\n",
+    "Regular expressions were invented, in their modern form, in 1951 by Stephen Kleene. They have their roots in theoretical computer science, although they have extremely useful as a text parsing tool.\n",
+    "\n",
+    "Practically every language has regular expressions built-in. They are often super optimized and always expressed in an archaic syntax.\n",
+    "\n",
+    "Regular expressiosn allow you to use basic components to parse a language. Here are some pseudo-code examples of regex expressions:\n",
+    "\n",
+    "Find all characters which are digits\n",
+    "\n",
+    "Find all characters which are digits, followed by another digit\n",
+    "\n",
+    "Find all characters which are at the beginning of a line, are of one of the following characters: [,.!;:], followed by 3 digits, followed by a comma, followed by three characters which are NOT digits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample regular expressions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Match starts at 9 ends at 10 and contains 3\n",
+      "Match starts at 10 ends at 11 and contains 8\n",
+      "Match starts at 32 ends at 33 and contains 3\n",
+      "Match starts at 33 ends at 34 and contains 6\n",
+      "Match starts at 54 ends at 55 and contains 1\n",
+      "Match starts at 55 ends at 56 and contains 0\n",
+      "Match starts at 76 ends at 77 and contains 8\n",
+      "Match starts at 102 ends at 103 and contains 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "ages = \"Homer is 38 years old, Marge is 36 years old, Bart is 10 years old, Lisa is 8 years old and Maggie is 3.\"\n",
+    "\n",
+    "# Task: Extract all ages\n",
+    "# Thinking: Find all numbers\n",
+    "# Regex pseudo code: find digits\n",
+    "\n",
+    "regex_attempt1 = \"(\\d)\" # <= Find digits\n",
+    "\n",
+    "for m in re.finditer(regex_attempt1, ages): \n",
+    "    print(\"Match starts at\",m.start(), \"ends at\", m.end(), \"and contains\", m.group())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Match starts at 9 ends at 11 and contains 38\n",
+      "Match starts at 32 ends at 34 and contains 36\n",
+      "Match starts at 54 ends at 56 and contains 10\n",
+      "Match starts at 76 ends at 77 and contains 8\n",
+      "Match starts at 102 ends at 103 and contains 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Task: Extract all ages\n",
+    "# Thinking: Find all numbers\n",
+    "# Regex pseudo code: find digits, clump consecutive digits together\n",
+    "\n",
+    "regex_attempt1 = \"(\\d+)\" # <= Find digits and 1 or more repititions\n",
+    "\n",
+    "for m in re.finditer(regex_attempt1, ages): \n",
+    "    print(\"Match starts at\",m.start(), \"ends at\", m.end(), \"and contains\", m.group())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Just use http://www.pyregex.com/ or https://www.debuggex.com/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise** Extract area codes from the following phone numbers. _Must_ write a single regex which is able to extract regular expressions from the following numbers (in a loop):\n",
+    "\n",
+    "1-201-123-1234\n",
+    "\n",
+    "98-708-567-7890\n",
+    "\n",
+    "0-708-333-4444\n",
+    "\n",
+    "In the above numbers, the area codes are 201, 708 and 708, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n",
+      "[]\n",
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "area_code_regex = r\"type_correct_regex_expression_here\"\n",
+    "\n",
+    "for ac in [\"1-201-123-1234\", \"98-708-567-7890\", \"0-708-333-4444\"]:\n",
+    "    print(re.findall(area_code_regex, ac))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hint: Look for the start of string, then one or more digits, then a dash, THEN the digits which contain our area code. Ignore the rest.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What regular expressions can't do\n",
+    "\n",
+    "Regular expressions are part of a theoretical framework which define languages. There are languages which are less or more powerful than regular expressions.\n",
+    "\n",
+    "For example, regular expressions are not able to correctly parse this expressions:\n",
+    "\n",
+    "`1 + (2 * (3 + 8))`\n",
+    "\n",
+    "In order to parse the expression above, after each left parenthesis, we would have to use recursion. Regular expressions are not designed to parse such recursive expressions.\n",
+    "\n",
+    "Practically speaking, although _many_ poeple attempt it, regular expressions are not the correct choise to parse html (web) pages or xml documents.\n",
+    "\n",
+    "Computer science students often learn about context free grammars. CFGs _can_ parse recursive strings and are often used to parse programming languages. Unfortunately, CFGs are out of scope for this course."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lectures/test_pickle b/lectures/test_pickle
deleted file mode 100644
index e17a93b6af0d59a475ec02f597f59da347850f67..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 24
bcmZo*jxA)+h+t%3U`Q-v1k*`{OsRSRIYI<y

diff --git a/lectures/test_pickle.pkl b/lectures/test_pickle.pkl
deleted file mode 100644
index eab9232e01267e95d84ff2610974276103c19880..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 28
jcmZo*ju2vCU?|SdO;so^Day=CSMXHGO-xBGWY7ZuZpa82


From 169adcd9d58d9536489c3dcb026f4170e50fcadd Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 10 Mar 2020 18:08:13 -0500
Subject: [PATCH 30/36] assign3

---
 assigments/NAME_SURNAME_ASSIGN_3.ipynb |  55 +++++++++++
 lectures/Lecture 10 - Recursion.ipynb  | 100 ++++++++++++++------
 lectures/Lecture 10 - RegExp.ipynb     | 122 +++++++++++++++++--------
 3 files changed, 210 insertions(+), 67 deletions(-)
 create mode 100644 assigments/NAME_SURNAME_ASSIGN_3.ipynb

diff --git a/assigments/NAME_SURNAME_ASSIGN_3.ipynb b/assigments/NAME_SURNAME_ASSIGN_3.ipynb
new file mode 100644
index 0000000..1429933
--- /dev/null
+++ b/assigments/NAME_SURNAME_ASSIGN_3.ipynb
@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ASSIGMENT 3\n",
+    "\n",
+    "1. Create a github repo\n",
+    "2. Create a python package\n",
+    "3. Create a module inside the package called msca\n",
+    "4. Create a processing \"class\"\n",
+    "5. Create some UDF's\n",
+    "6. Install / Reinstall your module on your python env.\n",
+    "7. Write a script that uses your module and save some files\n",
+    "8. Write a second script that reads this files using RegExp\n",
+    "9. Generate some visualization of your thingy.\n",
+    "10. Benchmark and make it multprocess (even if is just sequential chunking).\n",
+    "\n",
+    "## Things to consider\n",
+    "\n",
+    "1. What is the file structure?\n",
+    "2. Find examples of where your module could be useful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lectures/Lecture 10 - Recursion.ipynb b/lectures/Lecture 10 - Recursion.ipynb
index 065dc19..3b2555c 100644
--- a/lectures/Lecture 10 - Recursion.ipynb	
+++ b/lectures/Lecture 10 - Recursion.ipynb	
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -39,7 +39,7 @@
        "55"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -83,27 +83,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "def add_list_recursively(lst):\n",
+    "    print(lst, len(lst))\n",
     "    if len(lst) == 0: return 0\n",
     "    return lst[0] + add_list_recursively(lst[1:])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 10\n",
+      "[2, 3, 4, 5, 6, 7, 8, 9, 10] 9\n",
+      "[3, 4, 5, 6, 7, 8, 9, 10] 8\n",
+      "[4, 5, 6, 7, 8, 9, 10] 7\n",
+      "[5, 6, 7, 8, 9, 10] 6\n",
+      "[6, 7, 8, 9, 10] 5\n",
+      "[7, 8, 9, 10] 4\n",
+      "[8, 9, 10] 3\n",
+      "[9, 10] 2\n",
+      "[10] 1\n",
+      "[] 0\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "55"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -156,10 +174,10 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-8-ea38bd27bcd1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m<ipython-input-7-353df51d9689>\u001b[0m in \u001b[0;36mblowup\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-22-ea38bd27bcd1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-21-353df51d9689>\u001b[0m in \u001b[0;36mblowup\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[1;32m<ipython-input-7-353df51d9689>\u001b[0m in \u001b[0;36mblowup\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-21-353df51d9689>\u001b[0m in \u001b[0;36mblowup\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mblowup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
      ]
     }
@@ -279,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -314,7 +332,7 @@
        " [5, [], [9, [4, [], []], []]]]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -334,7 +352,7 @@
        "51"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -345,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -355,7 +373,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-14-34f92fb309c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtree\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-28-34f92fb309c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtree\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'list'"
      ]
     }
@@ -373,28 +391,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "def tree_sum(t):\n",
     "    # add the root, left tree and right tree\n",
+    "    print(t)\n",
     "    if len(t) == 0: return 0\n",
     "    return t[0] + tree_sum(t[1]) + tree_sum(t[2])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, [7, [2, [], []], [6, [5, [], []], [11, [], []]]], [5, [], [9, [4, [], []], []]]]\n",
+      "[7, [2, [], []], [6, [5, [], []], [11, [], []]]]\n",
+      "[2, [], []]\n",
+      "[]\n",
+      "[]\n",
+      "[6, [5, [], []], [11, [], []]]\n",
+      "[5, [], []]\n",
+      "[]\n",
+      "[]\n",
+      "[11, [], []]\n",
+      "[]\n",
+      "[]\n",
+      "[5, [], [9, [4, [], []], []]]\n",
+      "[]\n",
+      "[9, [4, [], []], []]\n",
+      "[4, [], []]\n",
+      "[]\n",
+      "[]\n",
+      "[]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "51"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -452,7 +496,7 @@
        "(2, 9)"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -483,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -587,7 +631,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/lectures/Lecture 10 - RegExp.ipynb b/lectures/Lecture 10 - RegExp.ipynb
index 605a820..3257686 100644
--- a/lectures/Lecture 10 - RegExp.ipynb	
+++ b/lectures/Lecture 10 - RegExp.ipynb	
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -96,28 +96,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2019\n",
-      "3000\n",
-      "1990\n"
+      "<re.Match object; span=(0, 2), match='01'>\n",
+      "01 01 None\n",
+      "<re.Match object; span=(0, 2), match='02'>\n",
+      "02 02 None\n",
+      "<re.Match object; span=(0, 2), match='03'>\n",
+      "03 03 None\n"
      ]
     }
    ],
    "source": [
     "import re # <= regular expression library\n",
     "\n",
-    "movies = [\"2019: After the Fall of New York\"\n",
-    "          , \"The exterminators of the year 3000\"\n",
-    "          , \"1990: The Bronx Warriors\"]\n",
+    "\n",
+    "movies = [\"01 - 2019: After the Fall of New York\"\n",
+    "          , \"02 - The exterminators of the year 3000\"\n",
+    "          , \"03 - 1990: The Bronx Warriors\"]\n",
+    "\n",
     "\n",
     "for m in movies:\n",
-    "    print(re.search(\"(\\d\\d\\d\\d)\", m).group(0))"
+    "     a = re.match(r\"(\\d+)*(\\d+)*\",m)\n",
+    "     print(a)\n",
+    "     print(a.group(0),a.group(1),a.group(2))"
    ]
   },
   {
@@ -163,19 +170,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Match starts at 9 ends at 10 and contains 3\n",
-      "Match starts at 10 ends at 11 and contains 8\n",
-      "Match starts at 32 ends at 33 and contains 3\n",
-      "Match starts at 33 ends at 34 and contains 6\n",
-      "Match starts at 54 ends at 55 and contains 1\n",
-      "Match starts at 55 ends at 56 and contains 0\n",
+      "Match starts at 9 ends at 11 and contains 38\n",
+      "Match starts at 32 ends at 34 and contains 36\n",
+      "Match starts at 54 ends at 56 and contains 10\n",
       "Match starts at 76 ends at 77 and contains 8\n",
       "Match starts at 102 ends at 103 and contains 3\n"
      ]
@@ -188,7 +192,7 @@
     "# Thinking: Find all numbers\n",
     "# Regex pseudo code: find digits\n",
     "\n",
-    "regex_attempt1 = \"(\\d)\" # <= Find digits\n",
+    "regex_attempt1 = \"(\\d+)\" # <= Find digits\n",
     "\n",
     "for m in re.finditer(regex_attempt1, ages): \n",
     "    print(\"Match starts at\",m.start(), \"ends at\", m.end(), \"and contains\", m.group())"
@@ -196,27 +200,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 65,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Match starts at 9 ends at 11 and contains 38\n",
-      "Match starts at 32 ends at 34 and contains 36\n",
-      "Match starts at 54 ends at 56 and contains 10\n",
-      "Match starts at 76 ends at 77 and contains 8\n",
-      "Match starts at 102 ends at 103 and contains 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Task: Extract all ages\n",
     "# Thinking: Find all numbers\n",
     "# Regex pseudo code: find digits, clump consecutive digits together\n",
     "\n",
-    "regex_attempt1 = \"(\\d+)\" # <= Find digits and 1 or more repititions\n",
+    "regex_attempt1 = \"(\\d\\d\\d)\" # <= Find digits and 1 or more repititions\n",
     "\n",
     "for m in re.finditer(regex_attempt1, ages): \n",
     "    print(\"Match starts at\",m.start(), \"ends at\", m.end(), \"and contains\", m.group())"
@@ -246,21 +238,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[]\n",
-      "[]\n",
-      "[]\n"
+      "['201']\n",
+      "['708']\n",
+      "['708']\n"
      ]
     }
    ],
    "source": [
-    "area_code_regex = r\"type_correct_regex_expression_here\"\n",
+    "area_code_regex = r\"\\d+-(\\d+)-\\d+-\\d+\"\n",
     "\n",
     "for ac in [\"1-201-123-1234\", \"98-708-567-7890\", \"0-708-333-4444\"]:\n",
     "    print(re.findall(area_code_regex, ac))"
@@ -289,9 +281,61 @@
     "\n",
     "Practically speaking, although _many_ poeple attempt it, regular expressions are not the correct choise to parse html (web) pages or xml documents.\n",
     "\n",
+    "\n",
     "Computer science students often learn about context free grammars. CFGs _can_ parse recursive strings and are often used to parse programming languages. Unfortunately, CFGs are out of scope for this course."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rafael\n",
+      "Isaac\n"
+     ]
+    }
+   ],
+   "source": [
+    "bestphysicist = ['Rafael Vescovi, PhD',\n",
+    "                 'Isaac Newton, physicist']\n",
+    "\n",
+    "if data < armagedon:\n",
+    "    rexp = \"(?P<x>\\d)(?P<y>\\d)\"\n",
+    "else:\n",
+    "    rexp = \"(?P<y>\\d)(?P<x>\\d)\"\n",
+    "    \n",
+    "for k in bestphysicist:\n",
+    "    m = re.match(r\"(?P<Name>\\w+) (?P<surname>\\w+), \\w+\", k)\n",
+    "    print(m.group('Name'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import multiprocessing as mp\n",
+    "import time\n",
+    "\n",
+    "def run(x):\n",
+    "  return x**2\n",
+    "\n",
+    "start = time.time()\n",
+    "\n",
+    "vect = list(range(2))\n",
+    "print(vect)\n",
+    "pool = mp.Pool(processes=1)\n",
+    "results = pool.map(run, vect)\n",
+    "print(results)\n",
+    "end = time.time()\n",
+    "print(\"Seconds:{}\".format(end - start))\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -316,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

From 9405614791aaaa106828828a7bc67c5f047cb13c Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Thu, 9 Apr 2020 15:15:44 -0500
Subject: [PATCH 31/36] New quarter

---
 .../NAME_SURNAME_ASSIGN_2-checkpoint.ipynb    | 570 ------------------
 assigments/NAME_SURNAME_ASSIGN_1.ipynb        | 570 ------------------
 assigments/NAME_SURNAME_ASSIGN_1_1.ipynb      | 230 +++++++
 assigments/NAME_SURNAME_ASSIGN_1_2.ipynb      | 203 +++++++
 assigments/NAME_SURNAME_ASSIGN_1_3.ipynb      | 186 ++++++
 ..._2.ipynb => NAME_SURNAME_ASSIGN_2_1.ipynb} |   0
 ..._3.ipynb => NAME_SURNAME_ASSIGN_3_1.ipynb} |   0
 ...cture 1 - Container, Iteration Tools.ipynb | 460 +++++++++-----
 8 files changed, 944 insertions(+), 1275 deletions(-)
 delete mode 100644 assigments/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_2-checkpoint.ipynb
 delete mode 100644 assigments/NAME_SURNAME_ASSIGN_1.ipynb
 create mode 100644 assigments/NAME_SURNAME_ASSIGN_1_1.ipynb
 create mode 100644 assigments/NAME_SURNAME_ASSIGN_1_2.ipynb
 create mode 100644 assigments/NAME_SURNAME_ASSIGN_1_3.ipynb
 rename assigments/{NAME_SURNAME_ASSIGN_2.ipynb => NAME_SURNAME_ASSIGN_2_1.ipynb} (100%)
 rename assigments/{NAME_SURNAME_ASSIGN_3.ipynb => NAME_SURNAME_ASSIGN_3_1.ipynb} (100%)

diff --git a/assigments/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_2-checkpoint.ipynb b/assigments/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_2-checkpoint.ipynb
deleted file mode 100644
index 51aa0de..0000000
--- a/assigments/.ipynb_checkpoints/NAME_SURNAME_ASSIGN_2-checkpoint.ipynb
+++ /dev/null
@@ -1,570 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assignment 2\n",
-    "* Due 2/25 by 9pm\n",
-    "* Topics: Numpy and Pandas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_2\n",
-    "* Finish the test on this notebook\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_2.pdf and RAFAEL_VESCOVI_ASSIGN_2.ipynb)\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
-    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
-    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sample Question\n",
-    "* Create a UDF that adds 2 to a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add_func(x):\n",
-    "    return(x+2)\n",
-    "\n",
-    "number = add_func(a)\n",
-    "print(number)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 1\n",
-    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
-    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
-    "    * make use of zip and comprehension to make the dictionary\n",
-    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [5,7]\n",
-    "# b = [\"x\", \"z\"]\n",
-    "# return {5:\"x\", 7:\"z\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8]\n",
-    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 2\n",
-    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
-    "* The function should return 4 things"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 3\n",
-    "* Map a function to the below list that\n",
-    "    * returns the remainder of each number divided by 3 (52/3 = 17 with a remainder of 1, we want the 1 to be returned)\n",
-    "    * use lambdas for the mapping\n",
-    "    * hint, look at modulus (%) in python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst = list(range(100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 4\n",
-    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
-    "* example below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lst = [1,2]\n",
-    "# dict = {1:\"a\", 2:\"b\"}\n",
-    "# new_lst = [\"a\",\"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = list(range(5))\n",
-    "dct = {\n",
-    "    0:\"a\",\n",
-    "    1:\"b\",\n",
-    "    2:\"c\",\n",
-    "    3:\"d\",\n",
-    "    4:\"e\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 5\n",
-    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
-    "* use comprehension to perform the squaring of each item\n",
-    "* hint, this should be doable in one line using args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_func(1,2,3)\n",
-    "    # do some stuff\n",
-    "    \n",
-    "# return_lst = [1,4,9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for this pass in the numberse 1,2,3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 6\n",
-    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [2,3]\n",
-    "# return_lst = [2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7]\n",
-    "b = [2,3,4,6,8,10,11,21]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 7\n",
-    "* write a function that takes a list of numbers and filters for odds only\n",
-    "* use the filter method and a lambda as the function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,2]\n",
-    "# new_lst = [1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 8\n",
-    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
-    "* Use the function to map it to a list comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,-2,1]\n",
-    "# using a comprehension return [3,4,3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,-2,1,8,-10,15,-12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 9\n",
-    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
-    "* note you may not use any third party modules.  use  comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [3,1]\n",
-    "# distance = ~2.23"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,1]\n",
-    "b = [3,1,2,4,3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 10\n",
-    "* write a function that takes a list, sorts it and finds the middle value.  \n",
-    "* use error handling to check the list is odd in length\n",
-    "* if it's even, return an error message\n",
-    "* feel free to use a try/except or assert\n",
-    "* show the output run on both lists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [4,6,1]\n",
-    "# sort = [1,4,6]\n",
-    "# middle val = 4\n",
-    "# if even number in size, return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
-    "b = [4,6,1,3,12,41,4,1,24,12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 11\n",
-    "* Make a generator that parses the list of transaction data\n",
-    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
-    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
-    "* make sure to skip the header somehow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# transactions = [A, item_a], [A, item_b]\n",
-    "# dict = {A:[item_a, item_b]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transactions = [\n",
-    "    [\"user_id|item_id\"],\n",
-    "    [\"A|item_a\"],\n",
-    "    [\"B|item_a\"],\n",
-    "    [\"C|item_a\"],\n",
-    "    [\"C|item_b\"],\n",
-    "    [\"C|item_c\"],\n",
-    "    [\"B|item_c\"],\n",
-    "    [\"D|item_b\"],\n",
-    "    [\"D|item_b\"]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 12\n",
-    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
-    "* Use error handling of some kind if the lists are not of equal length."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# should return [True, True, True, False, False]\n",
-    "a = [1,2,3,4,5]\n",
-    "b = [1,2,3,5,6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 13\n",
-    "Briefly explain the use of the following words in python:\n",
-    "* assert\n",
-    "* raise\n",
-    "* yield\n",
-    "* def\n",
-    "* lambda\n",
-    "* return\n",
-    "* map\n",
-    "* filter\n",
-    "* try\n",
-    "* except\n",
-    "* continue\n",
-    "* pass\n",
-    "* break\n",
-    "* while\n",
-    "* do (in the context of a while loop)\n",
-    "* for"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 14\n",
-    "Briefly describe the below:\n",
-    "* list\n",
-    "* tuple\n",
-    "* dictionary\n",
-    "* set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 15\n",
-    "* What two datatypes can be declared with { }?\n",
-    "* What two datatypes can be declared with ( )?\n",
-    "* Provide examples of each of the 4 above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 16\n",
-    "Describe what a generator is and how it differs from a list or tuple."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 17 \n",
-    "Show three ways of attaching a list to itself (one of then should use the function append) . \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'list' object has no attribute 'chain'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-11-449d8e80d3a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'chain'"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,2,3]\n",
-    "a.append(a)\n",
-    "\n",
-    "print(a.chain())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, [1, 2, 3, [...]], 1, 2, 3, [1, 2, 3, [...]]]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a+a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 3, [...]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/assigments/NAME_SURNAME_ASSIGN_1.ipynb b/assigments/NAME_SURNAME_ASSIGN_1.ipynb
deleted file mode 100644
index e118edf..0000000
--- a/assigments/NAME_SURNAME_ASSIGN_1.ipynb
+++ /dev/null
@@ -1,570 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assignment 1\n",
-    "* Due 1/31 by 5pm\n",
-    "* Topics: containers, udfs, comprehensions, error handling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_1\n",
-    "* Finish the test on this notebook\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_1.pdf and RAFAEL_VESCOVI_ASSIGN_1.ipynb)\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
-    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
-    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sample Question\n",
-    "* Create a UDF that adds 2 to a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add_func(x):\n",
-    "    return(x+2)\n",
-    "\n",
-    "number = add_func(a)\n",
-    "print(number)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 1\n",
-    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
-    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
-    "    * make use of zip and comprehension to make the dictionary\n",
-    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [5,7]\n",
-    "# b = [\"x\", \"z\"]\n",
-    "# return {5:\"x\", 7:\"z\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8]\n",
-    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 2\n",
-    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
-    "* The function should return 4 things"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 3\n",
-    "* Map a function to the below list that\n",
-    "    * returns the remainder of each number divided by 3 (52/3 = 17 with a remainder of 1, we want the 1 to be returned)\n",
-    "    * use lambdas for the mapping\n",
-    "    * hint, look at modulus (%) in python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst = list(range(100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 4\n",
-    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
-    "* example below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lst = [1,2]\n",
-    "# dict = {1:\"a\", 2:\"b\"}\n",
-    "# new_lst = [\"a\",\"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = list(range(5))\n",
-    "dct = {\n",
-    "    0:\"a\",\n",
-    "    1:\"b\",\n",
-    "    2:\"c\",\n",
-    "    3:\"d\",\n",
-    "    4:\"e\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 5\n",
-    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
-    "* use comprehension to perform the squaring of each item\n",
-    "* hint, this should be doable in one line using args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_func(1,2,3)\n",
-    "    # do some stuff\n",
-    "    \n",
-    "# return_lst = [1,4,9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for this pass in the numberse 1,2,3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 6\n",
-    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [2,3]\n",
-    "# return_lst = [2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7]\n",
-    "b = [2,3,4,6,8,10,11,21]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 7\n",
-    "* write a function that takes a list of numbers and filters for odds only\n",
-    "* use the filter method and a lambda as the function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,2]\n",
-    "# new_lst = [1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 8\n",
-    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
-    "* Use the function to map it to a list comprehension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# my_lst = [1,-2,1]\n",
-    "# using a comprehension return [3,4,3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,-2,1,8,-10,15,-12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 9\n",
-    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
-    "* note you may not use any third party modules.  use  comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [1,2]\n",
-    "# b = [3,1]\n",
-    "# distance = ~2.23"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [1,2,3,4,1]\n",
-    "b = [3,1,2,4,3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 10\n",
-    "* write a function that takes a list, sorts it and finds the middle value.  \n",
-    "* use error handling to check the list is odd in length\n",
-    "* if it's even, return an error message\n",
-    "* feel free to use a try/except or assert\n",
-    "* show the output run on both lists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [4,6,1]\n",
-    "# sort = [1,4,6]\n",
-    "# middle val = 4\n",
-    "# if even number in size, return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
-    "b = [4,6,1,3,12,41,4,1,24,12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 11\n",
-    "* Make a generator that parses the list of transaction data\n",
-    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
-    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
-    "* make sure to skip the header somehow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# transactions = [A, item_a], [A, item_b]\n",
-    "# dict = {A:[item_a, item_b]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transactions = [\n",
-    "    [\"user_id|item_id\"],\n",
-    "    [\"A|item_a\"],\n",
-    "    [\"B|item_a\"],\n",
-    "    [\"C|item_a\"],\n",
-    "    [\"C|item_b\"],\n",
-    "    [\"C|item_c\"],\n",
-    "    [\"B|item_c\"],\n",
-    "    [\"D|item_b\"],\n",
-    "    [\"D|item_b\"]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 12\n",
-    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
-    "* Use error handling of some kind if the lists are not of equal length."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# should return [True, True, True, False, False]\n",
-    "a = [1,2,3,4,5]\n",
-    "b = [1,2,3,5,6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 13\n",
-    "Briefly explain the use of the following words in python:\n",
-    "* assert\n",
-    "* raise\n",
-    "* yield\n",
-    "* def\n",
-    "* lambda\n",
-    "* return\n",
-    "* map\n",
-    "* filter\n",
-    "* try\n",
-    "* except\n",
-    "* continue\n",
-    "* pass\n",
-    "* break\n",
-    "* while\n",
-    "* do (in the context of a while loop)\n",
-    "* for"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 14\n",
-    "Briefly describe the below:\n",
-    "* list\n",
-    "* tuple\n",
-    "* dictionary\n",
-    "* set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 15\n",
-    "* What two datatypes can be declared with { }?\n",
-    "* What two datatypes can be declared with ( )?\n",
-    "* Provide examples of each of the 4 above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 16\n",
-    "Describe what a generator is and how it differs from a list or tuple."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 17 \n",
-    "Show three ways of attaching a list to itself (one of then should use the function append) . \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'list' object has no attribute 'chain'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-11-449d8e80d3a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'chain'"
-     ]
-    }
-   ],
-   "source": [
-    "a = [1,2,3]\n",
-    "a.append(a)\n",
-    "\n",
-    "print(a.chain())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, [1, 2, 3, [...]], 1, 2, 3, [1, 2, 3, [...]]]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a+a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 3, [...]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/assigments/NAME_SURNAME_ASSIGN_1_1.ipynb b/assigments/NAME_SURNAME_ASSIGN_1_1.ipynb
new file mode 100644
index 0000000..979f53c
--- /dev/null
+++ b/assigments/NAME_SURNAME_ASSIGN_1_1.ipynb
@@ -0,0 +1,230 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 1 - 1\n",
+    "* Topics: containers, udfs, comprehensions, error handling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instructions\n",
+    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_1\n",
+    "* Finish the test on this notebook\n",
+    "* Download your notebook as a PDF\n",
+    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_1.pdf and RAFAEL_VESCOVI_ASSIGN_1.ipynb)\n",
+    "* Show the output of your code working on the sample data, like below.  \n",
+    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
+    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
+    "* You have to show your work (code).\n",
+    "* You will be graded:\n",
+    "    * The functionality of your code (Does it do what it was meant to)\n",
+    "    * Showing the output on the sample data provided\n",
+    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
+    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
+    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
+    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample Question\n",
+    "* Create a UDF that adds 2 to a number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add_func(x):\n",
+    "    return(x+2)\n",
+    "\n",
+    "number = add_func(a)\n",
+    "print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 1\n",
+    "* Create a function that takes 2 lists as params, of equal length and does the following:\n",
+    "    * returns a dictionary where the keys are the first list and the values are the second list\n",
+    "    * make use of zip and comprehension to make the dictionary\n",
+    "    * if the lists are not of equal length, use error handling to indicate the mismatch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [5,7]\n",
+    "# b = [\"x\", \"z\"]\n",
+    "# return {5:\"x\", 7:\"z\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7,8]\n",
+    "b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 2\n",
+    "* Create a UDF that takes a list and returns the permutations and combinations (length 2) as nested lists, along with the number of permutations and combinations.  \n",
+    "* The function should return 4 things"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 3\n",
+    "* Map a function to the below list that\n",
+    "    * returns the remainder of each number divided by 3 (52/3 = 17 with a remainder of 1, we want the 1 to be returned)\n",
+    "    * use lambdas for the mapping\n",
+    "    * hint, look at modulus (%) in python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst = list(range(100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 4\n",
+    "* Using comprehension, replace the keys in the list with their value in the dictionary\n",
+    "* example below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lst = [1,2]\n",
+    "# dict = {1:\"a\", 2:\"b\"}\n",
+    "# new_lst = [\"a\",\"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = list(range(5))\n",
+    "dct = {\n",
+    "    0:\"a\",\n",
+    "    1:\"b\",\n",
+    "    2:\"c\",\n",
+    "    3:\"d\",\n",
+    "    4:\"e\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 5\n",
+    "* Make a UDF that takes a variable amount of numbers and returns a list with the numbers having been squared\n",
+    "* use comprehension to perform the squaring of each item\n",
+    "* hint, this should be doable in one line using args"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_func(1,2,3)\n",
+    "    # do some stuff\n",
+    "    \n",
+    "# return_lst = [1,4,9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for this pass in the numberse 1,2,3"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assigments/NAME_SURNAME_ASSIGN_1_2.ipynb b/assigments/NAME_SURNAME_ASSIGN_1_2.ipynb
new file mode 100644
index 0000000..c5071d8
--- /dev/null
+++ b/assigments/NAME_SURNAME_ASSIGN_1_2.ipynb
@@ -0,0 +1,203 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 1 - Part 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instructions\n",
+    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_1\n",
+    "* Finish the test on this notebook\n",
+    "* Download your notebook as a PDF\n",
+    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_1.pdf and RAFAEL_VESCOVI_ASSIGN_1.ipynb)\n",
+    "* Show the output of your code working on the sample data, like below.  \n",
+    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
+    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
+    "* You have to show your work (code).\n",
+    "* You will be graded:\n",
+    "    * The functionality of your code (Does it do what it was meant to)\n",
+    "    * Showing the output on the sample data provided\n",
+    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
+    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
+    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
+    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 6\n",
+    "* Make a UDF that takes two lists as params and returns a list of the intersecting items (items that appear in both lists)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [1,2]\n",
+    "# b = [2,3]\n",
+    "# return_lst = [2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7]\n",
+    "b = [2,3,4,6,8,10,11,21]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 7\n",
+    "* write a function that takes a list of numbers and filters for odds only\n",
+    "* use the filter method and a lambda as the function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_lst = [1,2]\n",
+    "# new_lst = [1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 8\n",
+    "* Write a function (lambda or not) that returns the absolute value of a number and adds 2\n",
+    "* Use the function to map it to a list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# my_lst = [1,-2,1]\n",
+    "# using a comprehension return [3,4,3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,-2,1,8,-10,15,-12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 9\n",
+    "* write a function that takes 2 lists and finds the euclidean distance between then\n",
+    "* note you may not use any third party modules.  use  comprehensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [1,2]\n",
+    "# b = [3,1]\n",
+    "# distance = ~2.23"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3,4,1]\n",
+    "b = [3,1,2,4,3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 10\n",
+    "* write a function that takes a list, sorts it and finds the middle value.  \n",
+    "* use error handling to check the list is odd in length\n",
+    "* if it's even, return an error message\n",
+    "* feel free to use a try/except or assert\n",
+    "* show the output run on both lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [4,6,1]\n",
+    "# sort = [1,4,6]\n",
+    "# middle val = 4\n",
+    "# if even number in size, return error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
+    "b = [4,6,1,3,12,41,4,1,24,12]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assigments/NAME_SURNAME_ASSIGN_1_3.ipynb b/assigments/NAME_SURNAME_ASSIGN_1_3.ipynb
new file mode 100644
index 0000000..eb2915f
--- /dev/null
+++ b/assigments/NAME_SURNAME_ASSIGN_1_3.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 1 - Part 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instructions\n",
+    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_1\n",
+    "* Finish the test on this notebook\n",
+    "* Download your notebook as a PDF\n",
+    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_1.pdf and RAFAEL_VESCOVI_ASSIGN_1.ipynb)\n",
+    "* Show the output of your code working on the sample data, like below.  \n",
+    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
+    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
+    "* You have to show your work (code).\n",
+    "* You will be graded:\n",
+    "    * The functionality of your code (Does it do what it was meant to)\n",
+    "    * Showing the output on the sample data provided\n",
+    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
+    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
+    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
+    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 10\n",
+    "\n",
+    "* write a function that takes a list, sorts it and finds the middle value.  \n",
+    "* use error handling to check the list is odd in length\n",
+    "* if it's even, return an error message\n",
+    "* feel free to use a try/except or assert\n",
+    "* show the output run on both lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a = [4,6,1]\n",
+    "# sort = [1,4,6]\n",
+    "# middle val = 4\n",
+    "# if even number in size, return error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
+    "b = [4,6,1,3,12,41,4,1,24,12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 11\n",
+    "* Make a generator that parses the list of transaction data\n",
+    "* Put the contents in a dictionary where the key is the user id and the value is the list of items the user has purchased\n",
+    "* note the transactions are pipe ( | ) delimtied, meaning you'll have to use some string manipulations to get the values from each transaction\n",
+    "* make sure to skip the header somehow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# transactions = [A, item_a], [A, item_b]\n",
+    "# dict = {A:[item_a, item_b]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transactions = [\n",
+    "    [\"user_id|item_id\"],\n",
+    "    [\"A|item_a\"],\n",
+    "    [\"B|item_a\"],\n",
+    "    [\"C|item_a\"],\n",
+    "    [\"C|item_b\"],\n",
+    "    [\"C|item_c\"],\n",
+    "    [\"B|item_c\"],\n",
+    "    [\"D|item_b\"],\n",
+    "    [\"D|item_b\"]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 12\n",
+    "* Make a function that takes 2 lists of equal length and returns a True when the values in a given index are the same in both lists and a false if they are not.\n",
+    "* Use error handling of some kind if the lists are not of equal length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should return [True, True, True, False, False]\n",
+    "a = [1,2,3,4,5]\n",
+    "b = [1,2,3,5,6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question 13\n",
+    "Show three ways of attaching a list to itself (one of then should use the function append) . \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1,2,3]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extra questions - To think about .. And answer..\n",
+    "\n",
+    "* What two datatypes can be declared with { }?\n",
+    "* What two datatypes can be declared with ( )?\n",
+    "* Provide examples of each of the 4 above.\n",
+    "\n",
+    "* Describe what a generator is and how it differs from a list or tuple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assigments/NAME_SURNAME_ASSIGN_2.ipynb b/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb
similarity index 100%
rename from assigments/NAME_SURNAME_ASSIGN_2.ipynb
rename to assigments/NAME_SURNAME_ASSIGN_2_1.ipynb
diff --git a/assigments/NAME_SURNAME_ASSIGN_3.ipynb b/assigments/NAME_SURNAME_ASSIGN_3_1.ipynb
similarity index 100%
rename from assigments/NAME_SURNAME_ASSIGN_3.ipynb
rename to assigments/NAME_SURNAME_ASSIGN_3_1.ipynb
diff --git a/lectures/Lecture 1 - Container, Iteration Tools.ipynb b/lectures/Lecture 1 - Container, Iteration Tools.ipynb
index 672dd99..6873c3f 100644
--- a/lectures/Lecture 1 - Container, Iteration Tools.ipynb	
+++ b/lectures/Lecture 1 - Container, Iteration Tools.ipynb	
@@ -12,6 +12,15 @@
     "* be adventorous so you can learn how powerful the concepts can be"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# I Don't like Rafi Class\n",
+    "## I wont share my screen\n",
+    "###### Just Kidding"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -47,6 +56,46 @@
     "* Atom (https://atom.io/)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 8\n",
+    "\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'b' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-67e500defa1b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'b' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(b)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -63,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,14 +121,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -99,6 +158,23 @@
     "print (b[0] + \" is a letter\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 is a integer\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(str(b[1]) +  \" is a integer\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 94,
@@ -120,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -129,7 +205,7 @@
        "list"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,6 +281,44 @@
     "dir(a)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, '4', '4']\n"
+     ]
+    }
+   ],
+   "source": [
+    "a.append('4')\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 4, 4, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a[6] = int(a[6])\n",
+    "a[5] = int(a[5])\n",
+    "a.sort()\n",
+    "print(a)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -265,14 +379,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4\n"
+      "7\n"
      ]
     }
    ],
@@ -331,16 +445,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['new_item', -3, -3, -3, 5]"
+       "[1, 2, 3, 4, 4, 4, 5]"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -351,22 +465,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['new_item', -3]"
+       "[1, 3, 4]"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a[0:-1:2]"
+    "a[0:5:2]"
    ]
   },
   {
@@ -390,6 +504,26 @@
     "a[4:6]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2, 3, 4, 4, 4, 5]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1:]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -410,22 +544,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1, 3, 5, [2, 4, 6, 8]]\n",
-      "4\n"
+      "[1, 3, 5, [2, 4, 'raf', 8]]\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "list indices must be integers or slices, not tuple",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-33-b47aeedb567a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'raf'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
      ]
     }
    ],
    "source": [
-    "a = [1,3,5,[2,4,6,8]]\n",
+    "a = [1,3,5,[2,4,'raf',8]]\n",
     "print(a)\n",
-    "print (len(a))"
+    "print (a[3][2])"
    ]
   },
   {
@@ -441,37 +585,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3\n",
-      "3\n",
-      "3\n"
+      "<map object at 0x0000017B5CB0D0C8>\n",
+      "[3, 3, 3]\n"
      ]
     }
    ],
    "source": [
     "a = [-3,-3,-3]\n",
-    "list(map(abs, a))\n",
-    "\n",
-    "for k in a:\n",
-    "    print(abs(a[k]))"
+    "b = map(abs, a)\n",
+    "print(b)\n",
+    "print(list(b))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<map object at 0x0000027082FF3608>\n",
+      "<map object at 0x0000017B5CB0DA08>\n",
       "[1, 1, 2, 2]\n"
      ]
     }
@@ -496,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
@@ -504,7 +653,8 @@
      "output_type": "stream",
      "text": [
       "[1, 2, 3, -4]\n",
-      "<filter object at 0x0000027083074AC8>\n"
+      "<filter object at 0x0000017B5CB19CC8>\n",
+      "[1, 2, 3, -4]\n"
      ]
     }
    ],
@@ -515,8 +665,9 @@
     "a = [1,2,3,-4]\n",
     "\n",
     "b = filter(my_func, a)\n",
-    "print (a)\n",
-    "print (b)"
+    "print(a)\n",
+    "print(b)\n",
+    "print(list(b))"
    ]
   },
   {
@@ -531,19 +682,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[27, 40, 18, 1, 22, 29, 8]\n"
+      "['BPF', 'LOG4', 'NV_MAGICCONST', 'RECIP_BPF', 'Random', 'SG_MAGICCONST', 'SystemRandom', 'TWOPI', '_BuiltinMethodType', '_MethodType', '_Sequence', '_Set', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_acos', '_bisect', '_ceil', '_cos', '_e', '_exp', '_inst', '_itertools', '_log', '_os', '_pi', '_random', '_sha512', '_sin', '_sqrt', '_test', '_test_generator', '_urandom', '_warn', 'betavariate', 'choice', 'choices', 'expovariate', 'gammavariate', 'gauss', 'getrandbits', 'getstate', 'lognormvariate', 'normalvariate', 'paretovariate', 'randint', 'random', 'randrange', 'sample', 'seed', 'setstate', 'shuffle', 'triangular', 'uniform', 'vonmisesvariate', 'weibullvariate']\n",
+      "[45, 49, 21, 20, 5, 45, 7]\n"
      ]
     }
    ],
    "source": [
     "import random \n",
+    "a = dir(random)\n",
+    "print(a)\n",
+    "\n",
     "res = [random.randrange(1, 50, 1) for i in range(7)] \n",
     "print (res)"
    ]
@@ -557,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -571,6 +726,7 @@
    ],
    "source": [
     "from math import sqrt\n",
+    "\n",
     "a = [1,2,3,-4]\n",
     "\n",
     "a = list(map(abs,a))\n",
@@ -619,7 +775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -628,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -637,7 +793,7 @@
        "True"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -648,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -657,7 +813,7 @@
        "tuple"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -729,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -738,7 +894,7 @@
        "1"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -749,7 +905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -758,7 +914,7 @@
        "(2, 3)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -769,7 +925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -778,7 +934,7 @@
        "2"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -790,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -799,7 +955,7 @@
        "0"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -825,7 +981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -834,7 +990,7 @@
        "{1, 2, 3, 4, 5}"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -846,7 +1002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -855,7 +1011,7 @@
        "True"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -866,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -875,7 +1031,7 @@
        "set"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -886,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 58,
    "metadata": {
     "scrolled": true
    },
@@ -951,7 +1107,7 @@
        " 'update']"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -962,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -971,7 +1127,7 @@
        "{1, 4}"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1076,7 +1232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1089,7 +1245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1098,7 +1254,7 @@
        "True"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1109,7 +1265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1118,7 +1274,7 @@
        "dict"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 63,
    "metadata": {
     "scrolled": true
    },
@@ -1179,7 +1335,7 @@
        " 'values']"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1197,7 +1353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1206,7 +1362,7 @@
        "dict_keys(['F_name', 'L_name', 'Like_potatos'])"
       ]
      },
-     "execution_count": 149,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1319,22 +1475,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1"
+       "'Rafael'"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "my_dict[\"a\"]"
+    "my_dict[\"F_name\"]"
    ]
   },
   {
@@ -1346,7 +1502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1386,7 +1542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1401,7 +1557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -1410,7 +1566,7 @@
        "{'first_name': 'Jane', 'last_name': 'Doe'}"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1421,7 +1577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -1430,7 +1586,7 @@
        "True"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1441,7 +1597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -1450,13 +1606,13 @@
        "dict"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(my_dict[1])"
+    "type(my_dict[0])"
    ]
   },
   {
@@ -1502,16 +1658,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<enumerate at 0x27082fc9908>"
+       "<enumerate at 0x17b5cb18638>"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1523,23 +1679,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 --> a\n",
-      "1 --> b\n",
-      "2 --> a\n",
-      "<enumerate object at 0x00000270830716D8>\n"
+      "(0, 1) --> 3\n",
+      "(1, 2) --> 3\n",
+      "(2, 3) --> 3\n"
      ]
     }
    ],
    "source": [
-    "for idx, i in enumerate(a):\n",
-    "    print(idx,\"-->\", i)\n",
+    "for idx in enumerate(a):\n",
+    "    print(idx,\"-->\", item)\n",
     "    \n"
    ]
   },
@@ -1555,7 +1710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1565,16 +1720,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<zip at 0x27082fdb588>"
+       "<zip at 0x17b5cb168c8>"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1585,7 +1740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -1594,7 +1749,7 @@
        "[(1, 'a'), (2, 'b'), (3, 'c')]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1605,7 +1760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -1625,7 +1780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -1645,7 +1800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -1705,7 +1860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -1714,7 +1869,7 @@
        "[(1, 'a', 'A'), (2, 'b', 'B'), (3, 'c', 'C')]"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1727,6 +1882,26 @@
     "list(zip(a,b,c))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a\n",
+      "b\n",
+      "c\n"
+     ]
+    }
+   ],
+   "source": [
+    "for item in b:\n",
+    "    print(item)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1739,13 +1914,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
       "0 is not greater then 4\n",
       "1 is not greater then 4\n",
       "2 is not greater then 4\n",
@@ -1757,6 +1933,7 @@
    ],
    "source": [
     "lst = list(range(10))\n",
+    "print(lst)\n",
     "for x in lst:\n",
     "    if x > 4:\n",
     "        print(\"{} is greater then 4 so we will break out loop\".format(x))\n",
@@ -1767,7 +1944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -1775,10 +1952,15 @@
      "output_type": "stream",
      "text": [
       "5 is greater then 4\n",
+      "5\n",
       "6 is greater then 4\n",
+      "6\n",
       "7 is greater then 4\n",
+      "7\n",
       "8 is greater then 4\n",
-      "9 is greater then 4\n"
+      "8\n",
+      "9 is greater then 4\n",
+      "9\n"
      ]
     }
    ],
@@ -1787,13 +1969,12 @@
     "for x in lst:\n",
     "    if x > 4:\n",
     "        print(\"{} is greater then 4\".format(x))\n",
-    "    else:\n",
-    "        pass"
+    "        print(x)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -1819,7 +2000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -1910,7 +2091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1919,19 +2100,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 107,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['a', 'b', 'c'], ['d', 'e', 'f', 'b']]\n"
+     ]
+    }
+   ],
    "source": [
     "a = [\n",
     "    [\"a\", \"b\", \"c\"],\n",
     "    [\"d\", \"e\", \"f\", \"b\"]\n",
-    "]"
+    "]\n",
+    "print(a)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1940,7 +2130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
@@ -1949,7 +2139,7 @@
        "['a', 'b', 'c', 'd', 'e', 'f', 'b']"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1968,7 +2158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1977,7 +2167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 111,
    "metadata": {
     "scrolled": true
    },
@@ -1988,7 +2178,7 @@
        "[('a', 'b'), ('a', 'c'), ('b', 'a'), ('b', 'c'), ('c', 'a'), ('c', 'b')]"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1999,7 +2189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -2013,7 +2203,7 @@
        " ('c', 'b', 'a')]"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2032,7 +2222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2041,7 +2231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -2050,7 +2240,7 @@
        "[('a', 'b'), ('a', 'c'), ('b', 'c')]"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2061,7 +2251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -2070,7 +2260,7 @@
        "[('a', 'b', 'c')]"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2155,7 +2345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2164,7 +2354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2173,7 +2363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -2182,7 +2372,7 @@
        "Counter({'a': 1, 'b': 2, 'c': 1, 'd': 1, 'e': 1, 'f': 1})"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2193,7 +2383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -2202,7 +2392,7 @@
        "collections.Counter"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2213,7 +2403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {

From a520125d5347182994015b3a2bf20dbb58de143b Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 22 Apr 2020 21:03:58 -0500
Subject: [PATCH 32/36] 422

---
 assigments/NAME_SURNAME_ASSIGN_1_1.ipynb      | 100 +--
 assigments/NAME_SURNAME_ASSIGN_1_3.ipynb      |  35 -
 ...bdas, Generators, and Error Handling.ipynb | 655 ++++++++-------
 ...3 - OOO, Datetime and Saving Objects.ipynb | 769 ++++++++++++------
 lectures/Sample Lecture - Objects.ipynb       |   7 -
 5 files changed, 937 insertions(+), 629 deletions(-)

diff --git a/assigments/NAME_SURNAME_ASSIGN_1_1.ipynb b/assigments/NAME_SURNAME_ASSIGN_1_1.ipynb
index 979f53c..b5f71ff 100644
--- a/assigments/NAME_SURNAME_ASSIGN_1_1.ipynb
+++ b/assigments/NAME_SURNAME_ASSIGN_1_1.ipynb
@@ -8,66 +8,6 @@
     "* Topics: containers, udfs, comprehensions, error handling"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Rename this notebook with your NAME_LASTNAME_ASSIGN_1\n",
-    "* Finish the test on this notebook\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF (in my case I would get two files RAFAEL_VESCOVI_ASSIGN_1.pdf and RAFAEL_VESCOVI_ASSIGN_1.ipynb)\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given.\n",
-    "* you should not have to explicitly use  a for loop except for question 12, meaning it is possible to complete the entire assignment without using the keyword \"for\".\n",
-    "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sample Question\n",
-    "* Create a UDF that adds 2 to a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add_func(x):\n",
-    "    return(x+2)\n",
-    "\n",
-    "number = add_func(a)\n",
-    "print(number)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -186,24 +126,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# my_func(1,2,3)\n",
-    "    # do some stuff\n",
-    "    \n",
-    "# return_lst = [1,4,9]"
+    "def my_func(*x):\n",
+    "    return [x[i]*x[i] for i in range(len(x))]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# for this pass in the numberse 1,2,3"
+    "b = my_func(1,2,3,4,5,6,7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6, 7]\n",
+      "[1, 4, 9, 16, 25, 36, 49]\n",
+      "<class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a)\n",
+    "print(b)\n",
+    "print(type(b))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/assigments/NAME_SURNAME_ASSIGN_1_3.ipynb b/assigments/NAME_SURNAME_ASSIGN_1_3.ipynb
index eb2915f..1d3f2b3 100644
--- a/assigments/NAME_SURNAME_ASSIGN_1_3.ipynb
+++ b/assigments/NAME_SURNAME_ASSIGN_1_3.ipynb
@@ -29,41 +29,6 @@
     "* the only third party functions/modules you are allowed to use are permutations and combinations from itertools.  yes, there is a built in median function in numpy, but the idea is to program yourself and solve the problems on your own using basic python techniques"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 10\n",
-    "\n",
-    "* write a function that takes a list, sorts it and finds the middle value.  \n",
-    "* use error handling to check the list is odd in length\n",
-    "* if it's even, return an error message\n",
-    "* feel free to use a try/except or assert\n",
-    "* show the output run on both lists"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a = [4,6,1]\n",
-    "# sort = [1,4,6]\n",
-    "# middle val = 4\n",
-    "# if even number in size, return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [4,6,1,3,12,41,4,1,24,12,17]\n",
-    "b = [4,6,1,3,12,41,4,1,24,12]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/lectures/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling.ipynb b/lectures/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling.ipynb
index 1f49e8c..bed5bf2 100644
--- a/lectures/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling.ipynb	
+++ b/lectures/Lecture 2 - UDFs, Comprehensions, Lambdas, Generators, and Error Handling.ipynb	
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +50,7 @@
        "function"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -61,24 +61,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "<built-in function print>\n",
       "<built-in function print>\n"
      ]
     }
    ],
    "source": [
-    "my_func(print)"
+    "my_func(print)\n",
+    "\n",
+    "print(print)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -102,17 +105,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def my_func(x):\n",
-    "    return x+2 # note you can just add on the fly here"
+    "def my_func1(x):\n",
+    "    print(x+2) # note you can just add on the fly here\n",
+    "    \n",
+    "def my_func2(x):\n",
+    "    return(x+2) # note you can just add on the fly here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_func1(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(my_func2(2))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -120,15 +161,17 @@
      "output_type": "stream",
      "text": [
       "4\n",
+      "None\n",
       "4\n"
      ]
     }
    ],
    "source": [
-    "print(my_func(2))\n",
+    "a = my_func1(2)\n",
+    "b = my_func2(2)\n",
     "\n",
-    "y = my_func(2)\n",
-    "print(y)"
+    "print(a)\n",
+    "print(b)"
    ]
   },
   {
@@ -140,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -175,26 +218,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
     "def my_func(x,y):\n",
-    "\n",
-    "  output = None \n",
-    "  z = x+y\n",
-    "\n",
-    "  if z < 5:\n",
-    "    output = True\n",
+    "  \n",
+    "  output = None\n",
+    "  threshold = 5 \n",
+    "  \n",
+    "  if x+y < threshold:\n",
+    "    output =  True\n",
     "  else:\n",
     "    output = False\n",
-    "\n",
+    "  \n",
     "  return output"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -203,7 +246,7 @@
        "True"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -215,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -224,7 +267,7 @@
        "False"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,43 +299,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<class 'list'>\n",
-      "[0, 1, 3, 5, 7, 9, 11, 13, 15]\n",
-      "0\n",
-      "CoolBeans!\n",
-      "1\n",
-      "CoolBeans!\n",
-      "3\n",
-      "CoolBeans!\n",
-      "5\n",
-      "CoolBeans!\n",
-      "7\n",
-      "CoolBeans!\n",
-      "9\n",
-      "CoolBeans!\n",
-      "11\n",
-      "NotMySauce!\n",
-      "13\n",
-      "NotMySauce!\n",
-      "15\n",
-      "NotMySauce!\n",
-      "The sum of test is 25\n",
-      "raf_list is still 35\n"
+      "<class 'int'>\n",
+      "35\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "'int' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-37-2570de3d723f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[0mtest_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mraf_func_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'The sum of test is '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-37-2570de3d723f>\u001b[0m in \u001b[0;36mraf_func_x\u001b[1;34m(par1)\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[1;31m#test for the ones bellow value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[1;31m#k is a value on raf_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mraf_list\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'int' object is not iterable"
      ]
     }
    ],
    "source": [
     "raf_list = 35\n",
     "\n",
-    "def raf_func_x(raf_list):\n",
-    "    \n",
+    "def raf_func_x(par1):\n",
+    "    global raf_list\n",
     "    #Lets test the types \n",
     "    print(type(raf_list))\n",
     "    print(raf_list)\n",
@@ -323,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -349,7 +384,7 @@
     }
    ],
    "source": [
-    "my_func(\"PoTatTO\",2) # won't work"
+    "my_func() # won't work"
    ]
   },
   {
@@ -379,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -408,29 +443,28 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-45-f73cfd3c8222>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-45-f73cfd3c8222>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    my_func(y = 5, \"str\") # won't work\u001b[0m\n\u001b[1;37m                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "str\n",
+      "7\n"
      ]
     }
    ],
    "source": [
-    "my_func(y = 5, \"str\") # won't work"
+    "my_func(y = 5, x \"str\") # won't work"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3\n",
-      "coolmethod\n"
+      "3\n"
      ]
     }
    ],
@@ -440,7 +474,7 @@
     "    if execute:\n",
     "        print(method) \n",
     "\n",
-    "test(0,1,2, execute=True,method='coolmethod')"
+    "test(0,1,2, execute=False,method='coolmethod')"
    ]
   },
   {
@@ -452,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -463,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -481,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -516,21 +550,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "I am inside the uncle_sam <class 'tuple'>\n",
+      "I am inside the uncle_sam <class 'tuple'>\n",
       "<class 'function'>\n",
       "<class 'list'>\n",
-      "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999]]\n"
+      "[('raf', 0, 'mR Pots', 3, 17, 'hitman'), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999]]\n"
      ]
     }
    ],
    "source": [
     "def uncle_sam(*enlisted):\n",
+    "    print('I am inside the uncle_sam',type(enlisted))\n",
     "    army = []\n",
     "    for soldier in enlisted:\n",
     "        army.append(soldier)\n",
@@ -541,7 +578,8 @@
     "micro_army2 = list(range(0,1000,1))\n",
     "\n",
     "#micro_army3 = micro_army + micro_army2\n",
-    "usaf = uncle_sam(micro_army2)\n",
+    "usaf = uncle_sam(micro_army) + uncle_sam(micro_army2)\n",
+    "\n",
     "print(type(uncle_sam))\n",
     "print(type(usaf))\n",
     "print(usaf)\n",
@@ -567,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -577,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -594,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -621,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -636,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -653,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -663,7 +701,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-73-8751ad374ad8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-69-8751ad374ad8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;31mTypeError\u001b[0m: my_func() takes 2 positional arguments but 3 were given"
      ]
     }
@@ -674,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -701,29 +739,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_func(x,y, **kwargs):\n",
+    "    val = x / y\n",
+    "    if \"third\" in kwargs:\n",
+    "        val+=kwargs[\"third\"]\n",
+    "    return val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.5\n",
-      "None\n",
-      "3.5\n",
-      "None\n"
+      "0\n",
+      "3\n",
+      "()\n",
+      "{'newkey': 'b'}\n"
      ]
     }
    ],
    "source": [
-    "def copied_from_2_cells_above(x,y, **kwargs):\n",
-    "    val = x / y\n",
-    "    if \"third\" in kwargs:\n",
-    "        val+=kwargs[\"third\"]\n",
-    "    print(val)\n",
-    "    \n",
-    "print(copied_from_2_cells_above(1,2))\n",
-    "print(copied_from_2_cells_above(1,2,third=3))\n"
+    "def my_std_func(pos,keyd=\"a\", *args, **kwargs):\n",
+    "    print(pos)\n",
+    "    print(keyd)\n",
+    "    print(args)\n",
+    "    print(kwargs)\n",
+    "    return\n",
+    "\n",
+    "my_std_func(0,keyd=3,newkey=\"b\")"
    ]
   },
   {
@@ -881,26 +932,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "She loves me\n",
+      "yeah\n",
+      "yeah\n",
+      "yeah\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[3, 4, 5]"
+       "[1, 2, 0]"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "my_lambda = lambda x: x + 2\n",
-    "\n",
-    "a = [1,2,3]\n",
+    "def rest3(x):\n",
+    "    print('yeah')\n",
+    "    return x%3\n",
     "\n",
-    "list(map(my_lambda, a))"
+    "a = map(rest3, [1,2,3])\n",
+    "print('She loves me')\n",
+    "list(a)"
    ]
   },
   {
@@ -912,7 +975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -921,37 +984,44 @@
        "[3, 4, 5]"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "a = [1,2,3]\n",
     "list(map(lambda x: x+2, a))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[4, 5]"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False, False, True, True]\n",
+      "[4, 5]\n"
+     ]
     }
    ],
    "source": [
     "a = [2,3,4,5]\n",
     "\n",
-    "list(filter(lambda x: x > 3, a))"
+    "print(list(map(lambda x: x > 3, a)))\n",
+    "print(list(filter(lambda x: x > 3, a)))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -965,7 +1035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -974,7 +1044,7 @@
        "['AEA', 'EEE']"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -997,18 +1067,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n"
+     ]
     }
    ],
    "source": [
@@ -1016,28 +1083,25 @@
     "b = []\n",
     "for i in a:\n",
     "    b.append(i)\n",
-    "b"
+    "print(b)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n"
+     ]
     }
    ],
    "source": [
-    "b = [i for i in a]\n",
-    "b"
+    "b = [value for value in a]\n",
+    "print(b)"
    ]
   },
   {
@@ -1064,61 +1128,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'a'"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{1: 'a', 2: 'b', 3: 'c'}\n"
+     ]
     }
    ],
    "source": [
     "a = [1,2,3]\n",
     "b = [\"a\",\"b\",\"c\"]\n",
     "\n",
-    "my_dict = dict((x[0],x[1]) for x in zip(a,b)}\n",
-    "print()"
+    "my_dict = dict((x[0],x[1]) for x in zip(a,b))\n",
+    "print(my_dict)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[16, 25]"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "b = [i for i in a if i > 3]\n",
+    "a = [1,2,3,4,5]\n",
+    "b = [i*i for i in a if i > 3]\n",
     "b"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[-1, -2, -3]"
+       "[-1, -2, -3, -4, -5]"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1130,7 +1192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -1139,7 +1201,7 @@
        "['a', 'b', 'c']"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1152,7 +1214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -1161,7 +1223,7 @@
        "['a', 'b', 'c']"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1178,29 +1240,30 @@
    "metadata": {},
    "source": [
     "<h3 style=\"color:blue\">write a lambda that</h3>\n",
-    "<p style=\"color:blue\">- use comprehension to apply to a list of numbers</p>\n",
+    "<p style=\"color:blue\">- use comprehension to apply to a list of string integers</p>\n",
     "<p style=\"color:blue\">- the lambda should return the remainder of the number divided by 2</p>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[1, 0, 1, 0, 1, 0]"
+       "[0, 1, 1]"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "test = [1,2,3,4,5,6]\n",
-    "list(map(lambda x:x%2,test))"
+    "test = [\"2\",\"3\",\"5\"]\n",
+    "\n",
+    "list(map(lambda x:int(x)%2,test))"
    ]
   },
   {
@@ -1236,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1246,7 +1309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -1255,7 +1318,7 @@
        "[0, 1, 2, 3, 4]"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1266,27 +1329,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x000001A597BF4848>"
+       "['__class__',\n",
+       " '__del__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__name__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__next__',\n",
+       " '__qualname__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'close',\n",
+       " 'gi_code',\n",
+       " 'gi_frame',\n",
+       " 'gi_running',\n",
+       " 'gi_yieldfrom',\n",
+       " 'send',\n",
+       " 'throw']"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "gen"
+    "dir(gen)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
@@ -1302,8 +1399,8 @@
     }
    ],
    "source": [
-    "for i in gen:\n",
-    "    print(i)"
+    "for value in gen:\n",
+    "    print(value)"
    ]
   },
   {
@@ -1315,7 +1412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1324,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -1333,7 +1430,7 @@
        "0"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1344,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -1353,7 +1450,7 @@
        "1"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1364,7 +1461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -1372,10 +1469,10 @@
      "evalue": "",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m: "
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-138-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mStopIteration\u001b[0m: "
      ]
     }
    ],
@@ -1392,7 +1489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1403,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1412,16 +1509,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<generator object infinite_sequence at 0x000001A597BF46C8>"
+       "<generator object infinite_sequence at 0x000002694289EB48>"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1432,7 +1529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
@@ -1482,7 +1579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1491,7 +1588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
@@ -1500,19 +1597,19 @@
        "81528056"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "nums_squared_lc = [i * 2 for i in range(10000000)]\n",
-    "sys.getsizeof(nums_squared_lc)#/ 1e+9"
+    "sys.getsizeof(nums_squared_lc)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [
     {
@@ -1521,7 +1618,7 @@
        "120"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1541,7 +1638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1577,16 +1674,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x000001A597C350C8>"
+       "<generator object <genexpr> at 0x0000026942DB6948>"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1598,16 +1695,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x000001A597C35148>"
+       "<generator object <genexpr> at 0x0000026942DB69C8>"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1619,7 +1716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 161,
    "metadata": {
     "scrolled": true
    },
@@ -1639,7 +1736,7 @@
        " 'round']"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1650,27 +1747,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['digg',\n",
-       " 'Digg',\n",
-       " '60',\n",
-       " 'web',\n",
-       " 'San Francisco',\n",
-       " 'CA',\n",
-       " '1-Dec-06',\n",
-       " '8500000',\n",
-       " 'USD',\n",
-       " 'b']"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-168-5bd8f1c512c8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_line\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mStopIteration\u001b[0m: "
+     ]
     }
    ],
    "source": [
@@ -1695,15 +1784,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-137-35c08edd0df2>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-169-35c08edd0df2>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-137-35c08edd0df2>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    print((2)))\u001b[0m\n\u001b[1;37m              ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-169-35c08edd0df2>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    print((2)))\u001b[0m\n\u001b[1;37m              ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1721,7 +1810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
@@ -1729,10 +1818,10 @@
      "evalue": "division by zero",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-78-fad870a50e27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-170-fad870a50e27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
    ],
@@ -1742,7 +1831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
@@ -1750,10 +1839,10 @@
      "evalue": "can only concatenate str (not \"int\") to str",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-79-89309f5042e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"string\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-171-89309f5042e3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"string\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
      ]
     }
    ],
@@ -1773,7 +1862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
@@ -1781,10 +1870,10 @@
      "evalue": "The length of x is not greater than 3",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-36-523a2a17546a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The length of x is not greater than 3\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m: The length of x is not greater than 3"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-174-1ac5e093ae2b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The length of x is not greater than 3\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m: The length of x is not greater than 3"
      ]
     }
    ],
@@ -1796,7 +1885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [
     {
@@ -1806,7 +1895,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-138-9bd7d8a13151>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"x is empty\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-175-9bd7d8a13151>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"x is empty\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;31mAssertionError\u001b[0m: x is empty"
      ]
     }
@@ -1827,25 +1916,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 177,
    "metadata": {},
    "outputs": [
     {
      "ename": "Exception",
-     "evalue": "x should not exceed 5.",
+     "evalue": "not cool",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-f2f500004ca1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x should not exceed 5.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mException\u001b[0m: x should not exceed 5."
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-177-c9f4dd786db9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mException\u001b[0m: not cool"
      ]
     }
    ],
    "source": [
     "x = 10\n",
+    "\n",
+    "message = Exception(\"not cool\")\n",
+    "\n",
     "if x > 5:\n",
-    "    raise Exception('x should not exceed 5.')"
+    "    raise message"
    ]
   },
   {
@@ -1858,17 +1950,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 180,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found an error.  Item test isn't a number or float\n",
+      "test\n",
+      "Found an error.  Item String isn't a number or float\n",
+      "String\n"
+     ]
+    }
+   ],
    "source": [
-    "a = [1,2,3,4,\"String\"]\n",
+    "a = [1,'test',3,4,\"String\"]\n",
     "\n",
     "for i in a:\n",
-    "    \n",
     "    try:\n",
     "        i = i+2\n",
-    "    except:"
+    "    except:\n",
+    "        print(\"Found an error.  Item {} isn't a number or float\".format(i))\n",
+    "        print(i)"
    ]
   },
   {
@@ -1884,9 +1988,7 @@
      ]
     }
    ],
-   "source": [
-    "        print(\"Found an error.  Item {} isn't a number or float\".format(i))"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -1900,7 +2002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 181,
    "metadata": {},
    "outputs": [
     {
@@ -1908,10 +2010,10 @@
      "evalue": "list index out of range",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-83-070992024e9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-181-070992024e9e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
      ]
     }
    ],
@@ -1922,7 +2024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 182,
    "metadata": {},
    "outputs": [
     {
@@ -1930,10 +2032,10 @@
      "evalue": "name 'z' is not defined",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-84-c01ac3f50fdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'z' is not defined"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-182-c01ac3f50fdc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'z' is not defined"
      ]
     }
    ],
@@ -1950,14 +2052,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 184,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2\n",
       "An error occurred\n"
      ]
     }
@@ -1965,11 +2066,9 @@
    "source": [
     "a = [1, 2, 3] \n",
     "try:  \n",
-    "    print(a[1]) \n",
-    "  \n",
+    "    print(a[3]) \n",
     "    # Throws error since there are only 3 elements in array \n",
-    "    print(a[3])\n",
-    "    \n",
+    "    print(a[3]) \n",
     "except IndexError: \n",
     "    print(\"An error occurred\")"
    ]
@@ -1983,7 +2082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 185,
    "metadata": {},
    "outputs": [
     {
@@ -1991,10 +2090,10 @@
      "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-97-b6c41dcfb79b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"string\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0;31m# raises the error that was caught\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-185-b6c41dcfb79b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"string\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mraise\u001b[0m \u001b[1;31m# raises the error that was caught\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
      ]
     }
    ],
diff --git a/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb b/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb
index 6f1b674..9bc3e5b 100644
--- a/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb	
+++ b/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb	
@@ -15,73 +15,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "# simple framework for a dog\n",
+    "\n",
     "class dog:\n",
-    "    \n",
-    "    # these are params here, things we set when\n",
-    "    # we initialize our instance of our class\n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
-    "        self.breed = breed\n",
-    "        self.name = name"
+    "    def __init__(self,dog_color,dog_breed,dog_name):\n",
+    "        self.color = dog_color\n",
+    "        self.breed = dog_breed\n",
+    "        self.name = dog_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__']"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(dog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")"
+    "my_dog = dog(dog_color = \"blue\", dog_breed = \"sheperd\", dog_name = \"spot\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'blue'"
+       "__main__.dog"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "my_dog.color"
+    "type(my_dog)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'sheperd'"
+       "'blue'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "my_dog.breed"
+    "my_dog.color"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -118,7 +161,7 @@
        " 'name']"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -129,41 +172,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(my_dog)\n",
+    "\n",
+    "\n",
+    "my_lazy_dog = [(10,'blue',10),('red','blue',10)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "__main__.dog"
+       "'red'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(my_dog)"
+    "my_lazy_dog[0][0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
     "# simple framework for a dog\n",
     "class dog:\n",
     "    \n",
-    "    def __init__(self, color, breed, name):\n",
-    "        self.color = color\n",
+    "    def __init__(self, breed, name,color=None):\n",
+    "        self.__color = color\n",
     "        self.breed = breed\n",
     "        self.name = name\n",
     "        \n",
     "    def speak(self):\n",
-    "        xxx = self.color\n",
-    "        print(\"My color is \" + xxx)\n",
+    "        print(\"My color is \" + self.__color)\n",
     "        \n",
     "    def change_color(self, carrot):\n",
     "        self.color = carrot"
@@ -171,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,27 +234,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<bound method dog.speak of <__main__.dog object at 0x000001F1CE85A9C8>>"
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " '_dog__color',\n",
+       " 'breed',\n",
+       " 'change_color',\n",
+       " 'name',\n",
+       " 'speak']"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "my_dog.speak"
+    "dir(my_dog)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -217,16 +301,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'blue'"
+       "'red'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,47 +321,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'red'"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My color is red\n"
+     ]
     }
    ],
    "source": [
     "my_dog.change_color(\"red\")\n",
-    "my_dog.color"
+    "my_dog.speak()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
     "# simple framework for a dog\n",
     "class dog:\n",
     "    \n",
-    "    def __init__(self, color, breed, name, distance=0):\n",
+    "    def __init__(self, color, breed, name):\n",
     "        self.color = color\n",
     "        self.breed = breed\n",
     "        self.name = name\n",
     "        self.distance = 0\n",
     "        \n",
     "    def walk(self, distance):\n",
-    "        self.distance += distance"
+    "        self.distance += distance\n",
+    "    \n",
+    "    def peemeter(self):\n",
+    "        pee = 1 -  self.distance/50\n",
+    "        print(\"Pee Probability is \", pee)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -286,7 +371,7 @@
        "0"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -298,44 +383,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pee Probability is  0.56\n"
+     ]
     }
    ],
    "source": [
-    "my_dog.walk(5)\n",
-    "my_dog.distance"
+    "my_dog.walk(22)\n",
+    "my_dog.distance\n",
+    "my_dog.peemeter()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "17"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pee Probability is  0.31999999999999995\n"
+     ]
     }
    ],
    "source": [
     "my_dog.walk(12)\n",
-    "my_dog.distance"
+    "my_dog.distance\n",
+    "my_dog.peemeter()"
    ]
   },
   {
@@ -359,6 +440,114 @@
     "my_dog.distance"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a class dog with a variable distance that is private.\n",
+    "\n",
+    "with a function that walks the dog and returns an error if the value is negative\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple framework for a dog\n",
+    "class dog:\n",
+    "    \n",
+    "    def __init__(self, color, breed, name):\n",
+    "        self.color = color\n",
+    "        self.breed = breed\n",
+    "        self.name = name\n",
+    "        self.__distance = 0\n",
+    "        \n",
+    "    def walk(self, distance):\n",
+    "        assert distance > 0, \"Dude.. don't be lazy!!\"\n",
+    "        self.__distance += distance\n",
+    "    \n",
+    "    def get_distance(self):\n",
+    "        print(self.__distance)\n",
+    "    \n",
+    "    def peemeter(self):\n",
+    "        pee = 1 -  self.__distance/50\n",
+    "        print(\"Pee Probability is \", pee)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dog = dog(color = \"blue\", breed = \"sheperd\", name = \"spot\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'dog' object has no attribute 'distance'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-113-9450ab14cff5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_dog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistance\u001b[0m \u001b[1;31m#distance is a private variable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: 'dog' object has no attribute 'distance'"
+     ]
+    }
+   ],
+   "source": [
+    "my_dog.distance #distance is a private variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "10\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "Dude.. don't be lazy!!",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-114-462306e3e0b3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmy_dog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwalk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmy_dog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_distance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mmy_dog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwalk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mmy_dog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_distance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-111-e06d8d5eea94>\u001b[0m in \u001b[0;36mwalk\u001b[1;34m(self, distance)\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwalk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__distance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m         \u001b[1;32massert\u001b[0m \u001b[0mdistance\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Dude.. don't be lazy!!\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__distance\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mdistance\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: Dude.. don't be lazy!!"
+     ]
+    }
+   ],
+   "source": [
+    "my_dog.walk(10)\n",
+    "my_dog.get_distance()\n",
+    "my_dog.walk(10)\n",
+    "my_dog.get_distance()\n",
+    "my_dog.walk(-10)\n",
+    "my_dog.get_distance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -369,28 +558,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class Dog:\n",
+    "# simple framework for a dog\n",
+    "class dog:\n",
     "    \n",
     "    def __init__(self, color, breed, name):\n",
     "        self.color = color\n",
     "        self.breed = breed\n",
     "        self.name = name\n",
-    "        self.distance = 0\n",
+    "        self.__distance = 0\n",
     "        \n",
     "    def walk(self, distance):\n",
-    "        self.distance += distance\n",
+    "        assert distance > 0, \"Dude.. don't be lazy!!\"\n",
+    "        self.__distance += distance\n",
+    "    \n",
+    "    def get_distance(self):\n",
+    "        print(self.__distance)\n",
+    "    \n",
+    "    def peemeter(self):\n",
+    "        pee = 1 -  self.__distance/50\n",
+    "        print(\"Pee Probability is \", pee)\n",
     "        \n",
-    "class Retreiver(Dog):\n",
+    "class Retreiver(dog):\n",
     "    pass"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,95 +597,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 127,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'blue'"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "r.color"
+    "r.walk(10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 128,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__module__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " '__weakref__',\n",
-       " 'breed',\n",
-       " 'color',\n",
-       " 'distance',\n",
-       " 'name',\n",
-       " 'walk']"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
     }
    ],
    "source": [
-    "dir(r)"
+    "r.get_distance()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 286,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class Dog:\n",
+    "# simple framework for a dog\n",
+    "class dog:\n",
     "    \n",
     "    def __init__(self, color, breed, name):\n",
     "        self.color = color\n",
     "        self.breed = breed\n",
     "        self.name = name\n",
-    "        self.distance = 0\n",
+    "        self.__distance = 0\n",
+    "        self.age = 2\n",
     "        \n",
     "    def walk(self, distance):\n",
-    "        self.distance += distance\n",
+    "        assert distance > 0, \"Dude.. don't be lazy!!\"\n",
+    "        self.__distance += distance\n",
+    "    \n",
+    "    def get_distance(self):\n",
+    "        print(self.__distance)\n",
+    "    \n",
+    "    def peemeter(self):\n",
+    "        pee = 1 -  self.__distance/50\n",
+    "        print(\"Pee Probability is \", pee)\n",
+    "\n",
+    "class vac():\n",
+    "    def __init__(self):\n",
+    "        self.vaccins_list=None\n",
     "        \n",
-    "class Retreiver(Dog):\n",
+    "    def add_vaccin(self,name):\n",
+    "        self.vaccins_list.append(name)\n",
+    "    \n",
+    "    def get_vaccin(self):\n",
+    "        print(self.vaccins_list)\n",
     "\n",
+    "\n",
+    "class retreiver(dog):\n",
+    "    def __init__(self, color, name):\n",
+    "        super().__init__(color=color, breed='Retriever',name=name)\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return \"My name is {}\".format(self.name)\n",
+    "        \n",
     "    def speak(self):\n",
     "        print(\"My name is {}\".format(self.name))\n",
     "        \n",
@@ -497,24 +677,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 287,
    "metadata": {},
    "outputs": [
     {
      "ename": "TypeError",
-     "evalue": "__init__() got an unexpected keyword argument 'color'",
+     "evalue": "__init__() got multiple values for argument 'color'",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-62-6f94a433d382>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mRetreiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"blue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"retreiver\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Rex\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblink182\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'color'"
+      "\u001b[1;32m<ipython-input-287-3867047a28de>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mseb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mretreiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'seb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-286-3b305b3e0620>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, color, name)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mretreiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvac\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Retriever'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvac\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: __init__() got multiple values for argument 'color'"
      ]
     }
    ],
    "source": [
-    "r =  Retreiver(color = \"blue\", breed = \"retreiver\", name = \"Rex\")\n",
-    "r.blink182()"
+    "seb = retreiver('black','seb')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'retreiver' object has no attribute 'name'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-285-93f9524fb212>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-283-8cc527b3b14f>\u001b[0m in \u001b[0;36m__repr__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"My name is {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mspeak\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'retreiver' object has no attribute 'name'"
+     ]
+    }
+   ],
+   "source": [
+    "print(seb)"
    ]
   },
   {
@@ -551,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -587,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -597,7 +799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
@@ -615,19 +817,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "30\n"
+      "20\n"
      ]
     }
    ],
    "source": [
-    "rex.class_add()\n",
     "rex.printer()\n",
     "\n"
    ]
@@ -799,14 +1000,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 182,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import datetime\n",
+    "\n",
+    "\n",
     "from datetime import datetime\n",
     "from datetime import date"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['MAXYEAR',\n",
+       " 'MINYEAR',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " 'date',\n",
+       " 'datetime',\n",
+       " 'datetime_CAPI',\n",
+       " 'sys',\n",
+       " 'time',\n",
+       " 'timedelta',\n",
+       " 'timezone',\n",
+       " 'tzinfo']"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(datetime)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -816,19 +1057,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 185,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2020-01-21\n"
+      "2020-04-22 20:08:46.587338\n"
      ]
     }
    ],
    "source": [
-    "today = date.today()\n",
+    "today = datetime.today()\n",
     "print(today)"
    ]
   },
@@ -899,16 +1140,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 186,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1"
+       "4"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 186,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -919,7 +1160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 187,
    "metadata": {},
    "outputs": [
     {
@@ -928,7 +1169,7 @@
        "2020"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -953,16 +1194,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1"
+       "2"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1047,14 +1288,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2020-01-21 19:32:08.672203\n"
+      "2020-04-22 20:10:36.964919\n"
      ]
     }
    ],
@@ -1152,16 +1393,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 201,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8"
+       "36"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1172,16 +1413,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 202,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "32"
+       "10"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1192,16 +1433,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "19"
+       "20"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1212,16 +1453,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 204,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "21"
+       "22"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1232,16 +1473,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1"
+       "4"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1252,7 +1493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 206,
    "metadata": {},
    "outputs": [
     {
@@ -1261,7 +1502,7 @@
        "2020"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 206,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1272,16 +1513,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 207,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1"
+       "2"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1297,51 +1538,60 @@
     "<h3 style=\"color:blue\">make a variable of the current date</h3>\n",
     "<p style=\"color:blue\">- make a list with the weekdays</p>\n",
     "<p style=\"color:blue\">- convert the weekday() integer to the actual date name</p>\n",
-    "<p style=\"color:blue\">- print the below, without hard coding it</p>"
+    "<p style=\"color:blue\">- print the below, without hard coding it</p>\n",
+    "\n",
+    "create a class called converter that receives a date and language and overhauls the print (__repr__) to show the actual date name."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 256,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hoje é Terça\n",
-      "Today is Tuesday\n",
-      "What?\n"
+      "Today is Wednesday\n",
+      "Hoje é Quarta\n",
+      "What?2\n"
      ]
     }
    ],
    "source": [
     "\n",
-    "def day_printer(language='EN'):\n",
-    "    if language=='EN':\n",
-    "        weekdays = {0:\"Monday\",\n",
-    "            1:\"Tuesday\",\n",
-    "            2:\"Wednesday\",\n",
-    "            3:\"Thursday\"}\n",
-    "        pretext=\"Today is \"\n",
-    "    \n",
-    "    elif language=='PT':\n",
-    "        weekdays= {0:\"Segunda\",\n",
-    "            1:\"Terça\",\n",
-    "            2:\"Quarta\",\n",
-    "            3:\"Quinta\"}\n",
-    "        pretext=\"Hoje é \"\n",
-    "    else:\n",
-    "        pretext=\"What?\"\n",
-    "        weekdays=[0,\"\",1,\"\",2,\"\",3,\"\"]\n",
+    "class converter():\n",
+    "    def __init__(self,language):\n",
+    "        self.lan = language\n",
+    "\n",
+    "    def day_printer(self,language):\n",
+    "        if language=='EN':\n",
+    "            weekdays = {0:\"Monday\",\n",
+    "                1:\"Tuesday\",\n",
+    "                2:\"Wednesday\",\n",
+    "                3:\"Thursday\"}\n",
+    "            pretext=\"Today is \"\n",
     "\n",
+    "        elif language=='PT':\n",
+    "            weekdays= {0:\"Segunda\",\n",
+    "                1:\"Terça\",\n",
+    "                2:\"Quarta\",\n",
+    "                3:\"Quinta\"}\n",
+    "            pretext=\"Hoje é \"\n",
+    "        else:\n",
+    "            pretext=\"What?\"\n",
+    "            weekdays=[\"0\",\"1\",\"2\",\"3\"]\n",
     "        \n",
-    "    print(pretext+ weekdays[today.weekday()] )\n",
+    "        return pretext + weekdays[today.weekday()]\n",
     "    \n",
+    "    def __repr__(self):\n",
+    "        x = self.day_printer(self.lan)\n",
+    "        return x\n",
     "    \n",
-    "day_printer('PT')\n",
-    "day_printer('EN')\n",
-    "day_printer('CH')"
+    "\n",
+    "print(converter('EN'))\n",
+    "print(converter('PT'))\n",
+    "print(converter('ES'))"
    ]
   },
   {
@@ -1361,7 +1611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 257,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1370,7 +1620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 258,
    "metadata": {},
    "outputs": [
     {
@@ -1389,15 +1639,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 259,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tuesday\n",
-      "Tue\n"
+      "Wednesday\n",
+      "Wed\n"
      ]
     }
    ],
@@ -1408,15 +1658,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 260,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "January\n",
-      "Jan\n"
+      "April\n",
+      "Apr\n"
      ]
     }
    ],
@@ -1427,15 +1677,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 261,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "01/21/20\n",
-      "21\n"
+      "04/22/20\n",
+      "22\n"
      ]
     }
    ],
@@ -1446,14 +1696,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 262,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tue Jan 21 19:57:30 2020\n"
+      "Wed Apr 22 20:45:33 2020\n"
      ]
     }
    ],
@@ -1521,14 +1771,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 264,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0757\n"
+      "0845\n"
      ]
     }
    ],
@@ -1540,14 +1790,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 263,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "19:57\n"
+      "20:45\n"
      ]
     }
    ],
@@ -1566,7 +1816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 270,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1575,21 +1825,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 275,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2020-01-21 20:00:44.727654\n",
-      "2020-02-01 01:15:44.728642\n"
+      "2020-04-22 20:52:51.292117\n",
+      "2020-05-03 02:07:51.292117\n",
+      "2020-01-31 00:00:00\n"
      ]
     }
    ],
    "source": [
     "print(datetime.now())\n",
-    "print(datetime.now() + timedelta(days=10, hours = 5, minutes = 15))"
+    "b = datetime(year=2020,month=1,day=31)\n",
+    "\n",
+    "print(datetime.now() + timedelta(days=10, hours = 5, minutes = 15))\n",
+    "print(b)"
    ]
   },
   {
@@ -1651,7 +1905,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = datetime.now()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = datetime.now()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.timedelta(seconds=8, microseconds=624430)"
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b-a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
    "metadata": {},
    "outputs": [
     {
@@ -1660,7 +1952,7 @@
        "datetime.datetime"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 271,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1971,13 +2263,6 @@
    "source": [
     "cocumber('Staying ALive!!')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/lectures/Sample Lecture - Objects.ipynb b/lectures/Sample Lecture - Objects.ipynb
index 562c71b..28aacf5 100644
--- a/lectures/Sample Lecture - Objects.ipynb	
+++ b/lectures/Sample Lecture - Objects.ipynb	
@@ -1030,13 +1030,6 @@
     "    * what params we can pass\n",
     "* https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From fc420e28216c618374e7391c570082b3ef6d95c0 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 22 Apr 2020 21:31:46 -0500
Subject: [PATCH 33/36] 422

---
 ...3 - OOO, Datetime and Saving Objects.ipynb | 94 +++++++++++++------
 1 file changed, 66 insertions(+), 28 deletions(-)

diff --git a/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb b/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb
index 9bc3e5b..b6f476f 100644
--- a/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb	
+++ b/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb	
@@ -625,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
+   "execution_count": 312,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -651,8 +651,13 @@
     "        print(\"Pee Probability is \", pee)\n",
     "\n",
     "class vac():\n",
+    "    \n",
+    "    @classmethod\n",
+    "    def printme(self):\n",
+    "        print(self.vaccins_list)\n",
+    "    \n",
     "    def __init__(self):\n",
-    "        self.vaccins_list=None\n",
+    "        self.vaccins_list=[]\n",
     "        \n",
     "    def add_vaccin(self,name):\n",
     "        self.vaccins_list.append(name)\n",
@@ -661,10 +666,11 @@
     "        print(self.vaccins_list)\n",
     "\n",
     "\n",
-    "class retreiver(dog):\n",
+    "class retreiver(dog,vac):\n",
     "    def __init__(self, color, name):\n",
-    "        super().__init__(color=color, breed='Retriever',name=name)\n",
-    "    \n",
+    "        dog.__init__(self,color=color, breed='Retriever',name=name)\n",
+    "        vac.__init__(self)\n",
+    "        \n",
     "    def __repr__(self):\n",
     "        return \"My name is {}\".format(self.name)\n",
     "        \n",
@@ -677,46 +683,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 287,
+   "execution_count": 313,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "__init__() got multiple values for argument 'color'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-287-3867047a28de>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mseb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mretreiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'seb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m<ipython-input-286-3b305b3e0620>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, color, name)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mretreiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvac\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Retriever'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvac\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: __init__() got multiple values for argument 'color'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['BCG']\n"
      ]
     }
    ],
    "source": [
-    "seb = retreiver('black','seb')"
+    "seb = retreiver('black','seb')\n",
+    "seb.add_vaccin('BCG')\n",
+    "seb.get_vaccin()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
+   "execution_count": 304,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'retreiver' object has no attribute 'name'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-285-93f9524fb212>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m<ipython-input-283-8cc527b3b14f>\u001b[0m in \u001b[0;36m__repr__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"My name is {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mspeak\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'retreiver' object has no attribute 'name'"
-     ]
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " '_dog__distance',\n",
+       " 'add_vaccin',\n",
+       " 'age',\n",
+       " 'blink182',\n",
+       " 'breed',\n",
+       " 'color',\n",
+       " 'get_distance',\n",
+       " 'get_vaccin',\n",
+       " 'name',\n",
+       " 'peemeter',\n",
+       " 'speak',\n",
+       " 'walk']"
+      ]
+     },
+     "execution_count": 304,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(seb)"
+    "dir(seb)"
    ]
   },
   {

From cda0735cc547f7ccdafecde78831598219894810 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Sun, 3 May 2020 23:07:32 -0500
Subject: [PATCH 34/36] Update

---
 assigments/NAME_SURNAME_ASSIGN_2_1.ipynb      |   149 +-
 assigments/NAME_SURNAME_ASSIGN_2_2.ipynb      |   102 +
 .../NAME_SURNAME_ASSIGN_3_1.ipynb             |     0
 ...ynb => Lecture 3 - OOO and Datetime.ipynb} |   212 -
 lectures/Lecture 4 -  Numpy.ipynb             | 97672 ++++++++++++++++
 ... Pandas.ipynb => Lecture 5 - Pandas.ipynb} |  2337 -
 6 files changed, 97778 insertions(+), 2694 deletions(-)
 create mode 100644 assigments/NAME_SURNAME_ASSIGN_2_2.ipynb
 rename {assigments => future}/NAME_SURNAME_ASSIGN_3_1.ipynb (100%)
 rename lectures/{Lecture 3 - OOO, Datetime and Saving Objects.ipynb => Lecture 3 - OOO and Datetime.ipynb} (87%)
 create mode 100644 lectures/Lecture 4 -  Numpy.ipynb
 rename lectures/{Lecture 4 and 5 - Numpy and Pandas.ipynb => Lecture 5 - Pandas.ipynb} (75%)

diff --git a/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb b/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb
index 9a76633..6e7b13f 100644
--- a/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb
+++ b/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb
@@ -4,29 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Assignment 2\n",
-    "* Due: Tuesday 2/25/2020 by  9 PM \n",
+    "## Assignment 2 - Part 1\n",
     "* Topics: pandas and numpy"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instructions\n",
-    "* Download your notebook as a PDF\n",
-    "* Turn in both the notebook and PDF\n",
-    "* Show the output of your code working on the sample data, like below.  \n",
-    "* If techniques are specified, use them.  Otherwise, feel free to solve the problem how you want.  \n",
-    "* Try to keep your code clean, concise and avoid loops if necessary.  \n",
-    "* You have to show your work (code).\n",
-    "* You will be graded:\n",
-    "    * The functionality of your code (Does it do what it was meant to)\n",
-    "    * Showing the output on the sample data provided\n",
-    "    * How concise it is (did you use a for loop when you could have used a comprehension for instance).  Simply put, try and write clean, concise and readable code (don't use 10 lines for what could have been done in 4).\n",
-    "    * Even if the answer isn't perfect, make an honest attempt as partial credit will be given."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -99,138 +80,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Question 6\n",
+    "## Extra questions  - To think about.\n",
     "* Describe a situation the functionality from question 4 and 5 could be of use?"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 7\n",
-    "* sort the feature values for each row and replace the indices with the feature names\n",
-    "    * so each row will have 4 columns, the first column being the feature name that is the highest value, the second column being the feature name that is the second highest value, etc.\n",
-    "* note, watch out how argsort in numpy works.  you will need to reverse the order somehow.  the sorted(reverse = True) functionality might be of help.\n",
-    "* make sure to replace the index values with the feature name, as we did above\n",
-    "* put the resulting 2-d array into a pandas dataframe and print the first 5 rows.\n",
-    "* hint, look at the apply_along_axis() method for numpy arrays"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 8\n",
-    "* apply z-score normalization to each column in the iris data (note you do not need the target/label column)\n",
-    "* column wise meaning, treat each column as an array, and find the standard deviations and means of that column\n",
-    "* note, you can use zscore from scipy.stats\n",
-    "* results should be a pandas dataframe, printing out 5 rows and running the describe() method on the dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 9\n",
-    "* make a function that takes in a 2d numpy array X, a 1d numpy array Y and the size the training data, test data and valiation datasets.  \n",
-    "* return 6 items\n",
-    "    * train_x, train_y, test_x, test_y, val_x, val_y\n",
-    "* do not use any modules.  this can be solved using bracket notation to subset.  Note // will take care of decimals in doing division. you could also use int() to convert the float to a whole number\n",
-    "* make the params for the training, test and val size be decimals, that repsent percentages of the data.  For instance .8, .1, .1 means an 80% training, 10% test and 10% validation split\n",
-    "* use an assert to make sure the numbers add to 1\n",
-    "* print out the dimensions of all 6 elements, using iris as a test case with an 80-10-10 split"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 10\n",
-    "* using pandas, find the sum of each feature by species type (label)\n",
-    "* do the same, but find the min, max and median as well"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 11\n",
-    "* mean center each column of the iris dataframe (excluding the label column of course)\n",
-    "* this means, for each column, the mean should be zero.  to accomplish this we can subtract the column mean from each element of the column\n",
-    "* note, broadcasting, which if we have say a 150 row and 4 column dataframe, can  take a row vector of size 4 and apply it to each element\n",
-    "*  thus, we can answer this questions doing something like df - df_col_means\n",
-    "* run the describe() method at the end to show the data has been mean center"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quesiton 12\n",
-    "* Explain what the axis mean in regards to a pandas dataframe and numpy array\n",
-    "* How would you groupby two columns in pandas?\n",
-    "* What functions are used to read in csvs and excel files in pandas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 13\n",
-    "* using Pandas, subset the dataset for only species setosa and petal length > 1.3\n",
-    "* sum the feature columns and display the results for each (excluding the label column)\n",
-    "* do the same, but change the and to an or, and repeat the same calculation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 14\n",
-    "* write a lambda that subtracts 2 from all elements of a pandas dataframe\n",
-    "* display the top 5 rows\n",
-    "* use the same lambda,  but applying it to the sepal length  column only\n",
-    "* so the output should be the top 5 rows of a dataframe where all cells have had 2 subtracted and then another dataframe where only sepal length has 2 subtracted from it"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 15\n",
-    "* normalize each column of pandas dataframe to 0-1 scale.\n",
-    "* hint\n",
-    "    * 0-1 is done by using (X - xmin)/(xmax - xmin)\n",
-    "    * the approach should be similar to when we meean centered the dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Question 16\n",
-    "* assume the below 2 matrices, the first is observations and the second is cluster centers\n",
-    "* using cdist from scipy, create a matrix where the rows represent our 3 observations and the columns represent our 2 clusters and the cells values are the euclidean distances between a given observation and cluster\n",
-    "* when would this be of use?  "
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "observations = np.array([\n",
-    "    [1,2,3],\n",
-    "    [4,3,1],\n",
-    "    [2,3,4]\n",
-    "])\n",
-    "\n",
-    "cluster_centers = np.array([\n",
-    "    [2,3,1],\n",
-    "    [2,1,3]\n",
-    "])"
-   ]
+   "source": []
   }
  ],
  "metadata": {
diff --git a/assigments/NAME_SURNAME_ASSIGN_2_2.ipynb b/assigments/NAME_SURNAME_ASSIGN_2_2.ipynb
new file mode 100644
index 0000000..0a532f1
--- /dev/null
+++ b/assigments/NAME_SURNAME_ASSIGN_2_2.ipynb
@@ -0,0 +1,102 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 2 - Part 2\n",
+    "* Topics: pandas and numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 6\n",
+    "* sort the feature values for each row and replace the indices with the feature names\n",
+    "    * so each row will have 4 columns, the first column being the feature name that is the highest value, the second column being the feature name that is the second highest value, etc.\n",
+    "* note, watch out how argsort in numpy works.  you will need to reverse the order somehow.  the sorted(reverse = True) functionality might be of help.\n",
+    "* make sure to replace the index values with the feature name, as we did above\n",
+    "* put the resulting 2-d array into a pandas dataframe and print the first 5 rows.\n",
+    "* hint, look at the apply_along_axis() method for numpy arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 7\n",
+    "* apply z-score normalization to each column in the iris data (note you do not need the target/label column)\n",
+    "* column wise meaning, treat each column as an array, and find the standard deviations and means of that column\n",
+    "* note, you can use zscore from scipy.stats\n",
+    "* results should be a pandas dataframe, printing out 5 rows and running the describe() method on the dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 8\n",
+    "* make a function that takes in a 2d numpy array X, a 1d numpy array Y and the size the training data, test data and valiation datasets.  \n",
+    "* return 6 items\n",
+    "    * train_x, train_y, test_x, test_y, val_x, val_y\n",
+    "* do not use any modules.  this can be solved using bracket notation to subset.  Note // will take care of decimals in doing division. you could also use int() to convert the float to a whole number\n",
+    "* make the params for the training, test and val size be decimals, that repsent percentages of the data.  For instance .8, .1, .1 means an 80% training, 10% test and 10% validation split\n",
+    "* use an assert to make sure the numbers add to 1\n",
+    "* print out the dimensions of all 6 elements, using iris as a test case with an 80-10-10 split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 9\n",
+    "* using pandas, find the sum of each feature by species type (label)\n",
+    "* do the same, but find the min, max and median as well"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Question 10\n",
+    "* mean center each column of the iris dataframe (excluding the label column of course)\n",
+    "* this means, for each column, the mean should be zero.  to accomplish this we can subtract the column mean from each element of the column\n",
+    "* note, broadcasting, which if we have say a 150 row and 4 column dataframe, can  take a row vector of size 4 and apply it to each element\n",
+    "*  thus, we can answer this questions doing something like df - df_col_means\n",
+    "* run the describe() method at the end to show the data has been mean center"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extra questions.. to think and answer..\n",
+    "* Explain what the axis mean in regards to a pandas dataframe and numpy array\n",
+    "* How would you groupby two columns in pandas?\n",
+    "* What functions are used to read in csvs and excel files in pandas"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assigments/NAME_SURNAME_ASSIGN_3_1.ipynb b/future/NAME_SURNAME_ASSIGN_3_1.ipynb
similarity index 100%
rename from assigments/NAME_SURNAME_ASSIGN_3_1.ipynb
rename to future/NAME_SURNAME_ASSIGN_3_1.ipynb
diff --git a/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb b/lectures/Lecture 3 - OOO and Datetime.ipynb
similarity index 87%
rename from lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb
rename to lectures/Lecture 3 - OOO and Datetime.ipynb
index b6f476f..40c5aa7 100644
--- a/lectures/Lecture 3 - OOO, Datetime and Saving Objects.ipynb	
+++ b/lectures/Lecture 3 - OOO and Datetime.ipynb	
@@ -2089,218 +2089,6 @@
     "<p style=\"color:blue\">- add/subtract a year</p>\n",
     "<p style=\"color:blue\">- add subtract a year and 2 weeks</p>"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Saving Objects in Python\n",
-    "* serialization, marshaling, and flattening. \n",
-    "* same—to save an object to a file for later retrieval\n",
-    "* accomplishes this by writing the object as one long stream of bytes. \n",
-    "* pickling also used to save models in sklearn\n",
-    "* https://docs.python.org/3/library/pickle.html\n",
-    "* https://scikit-learn.org/stable/modules/model_persistence.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = [\"a\", \"b\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file = open('test_pickle.pkl', 'wb') \n",
-    "pickle.dump(test, file)\n",
-    "file.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'test' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-161-a07cb10eeb0f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# delete from memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m: name 'test' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "# delete from memory\n",
-    "del test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file = open('test_pickle.pkl', 'rb') \n",
-    "test = pickle.load(file)\n",
-    "file.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b']"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### using with"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "item = \"some string I made\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"test_pickle.pkl\", \"wb\") as file:\n",
-    "    pickle.dump(item, file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del item\n",
-    "#item"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"test_pickle.pkl\", \"rb\") as file:\n",
-    "    item = pickle.load(file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'some string I made'"
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "item"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">make a python object</h3>\n",
-    "<p style=\"color:blue\">- save the object</p>\n",
-    "<p style=\"color:blue\">- delete the object from memory</p>\n",
-    "<p style=\"color:blue\">- reload it</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import shutil\n",
-    "import os\n",
-    "class cocumber:\n",
-    "    def __init__(self,test):\n",
-    "        with open('potato.pkl','wb') as f :\n",
-    "            pickle.dump(test, f)\n",
-    "        del test\n",
-    "        os.rename('potato.pkl', 'mashme3.txt')\n",
-    "        with open('meshme3.txt','rb') as f :    \n",
-    "            test = pickle.load(f)\n",
-    "        print(test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'meshme3.txt'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-190-24b9bf6d3f76>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcocumber\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Staying ALive!!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m<ipython-input-189-df4c0cfbf515>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'potato.pkl'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mashme3.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'meshme3.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m             \u001b[0mtest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'meshme3.txt'"
-     ]
-    }
-   ],
-   "source": [
-    "cocumber('Staying ALive!!')"
-   ]
   }
  ],
  "metadata": {
diff --git a/lectures/Lecture 4 -  Numpy.ipynb b/lectures/Lecture 4 -  Numpy.ipynb
new file mode 100644
index 0000000..a92ca4b
--- /dev/null
+++ b/lectures/Lecture 4 -  Numpy.ipynb	
@@ -0,0 +1,97672 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numpy Arrays\n",
+    "* multi dimensional arrays or data\n",
+    "* can be 1d, 2d, 3d\n",
+    "* very computationally efficient to perform linear algebra operations over\n",
+    "* elementwise operations\n",
+    "* row and column wise calculations\n",
+    "* https://docs.scipy.org/doc/numpy-1.11.0/numpy-user-1.11.0.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Arrays vs. Matrix Class\n",
+    "* \"Short Answer: Use Arrays.\"\n",
+    "* They are the standard vector/matrix/tensor type of numpy. Many numpy functions return arrays, not matrices.\n",
+    "* The array class is intended to be a general-purposen-dimensional array for many kinds of numerical computing, whilematrixis intended to facilitate linear algebracomputations specifically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1-D arrays\n",
+    "* note his only has one demension\n",
+    "* we could use a method called reshape to change this (we will get into this more later on)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1,2,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5,)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = np.reshape(a, (5,1))\n",
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = b[1]\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### indexing and slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 3])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k_element in a:\n",
+    "    print(k_element)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### looping\n",
+    "* numpy loops through the left most axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,4]\n",
+    "])\n",
+    "\n",
+    "array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "[2 3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# since we have 2 items in the first axis we will end up with 2 items in our iteration\n",
+    "for i in array:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3,4],\n",
+    "    [2,3,4,4],\n",
+    "    [2,3,4,4]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,7,4],\n",
+    "    [4,6,9,4],\n",
+    "    [2,3,4,4]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3 4]\n",
+      " [2 3 4 4]\n",
+      " [2 3 4 4]] (3, 4) <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# depth 2\n",
+    "# rows 3\n",
+    "# columns 4\n",
+    "tensor = np.array([array, array1])\n",
+    "tensor.shape\n",
+    "image1 = tensor[0]\n",
+    "print(image1,image1.shape,type(image1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* remember numpy will iterate left most index\n",
+    "* in this 3-d cube, it's iterating the dpeth\n",
+    "* so we end up printing our 2 initial matrices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3 4]\n",
+      " [2 3 4 4]\n",
+      " [2 3 4 4]]\n",
+      "[[5 6 7 4]\n",
+      " [4 6 9 4]\n",
+      " [2 3 4 4]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tensor:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### performing matrix operations\n",
+    "* axis 0 is the y axis\n",
+    "* the up and down axis\n",
+    "* the number of rows\n",
+    "* so any operation done on axis 0 is going to be columns\n",
+    "* done up and down\n",
+    "* axis 1 is the length of our array\n",
+    "* any summing or anything is done across our dataframe\n",
+    "* doing some operation to our rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3,4],\n",
+    "    [2,3,4,4],\n",
+    "    [2,3,4,4]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 4)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['T',\n",
+       " '__abs__',\n",
+       " '__add__',\n",
+       " '__and__',\n",
+       " '__array__',\n",
+       " '__array_finalize__',\n",
+       " '__array_function__',\n",
+       " '__array_interface__',\n",
+       " '__array_prepare__',\n",
+       " '__array_priority__',\n",
+       " '__array_struct__',\n",
+       " '__array_ufunc__',\n",
+       " '__array_wrap__',\n",
+       " '__bool__',\n",
+       " '__class__',\n",
+       " '__complex__',\n",
+       " '__contains__',\n",
+       " '__copy__',\n",
+       " '__deepcopy__',\n",
+       " '__delattr__',\n",
+       " '__delitem__',\n",
+       " '__dir__',\n",
+       " '__divmod__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__float__',\n",
+       " '__floordiv__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__iadd__',\n",
+       " '__iand__',\n",
+       " '__ifloordiv__',\n",
+       " '__ilshift__',\n",
+       " '__imatmul__',\n",
+       " '__imod__',\n",
+       " '__imul__',\n",
+       " '__index__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__int__',\n",
+       " '__invert__',\n",
+       " '__ior__',\n",
+       " '__ipow__',\n",
+       " '__irshift__',\n",
+       " '__isub__',\n",
+       " '__iter__',\n",
+       " '__itruediv__',\n",
+       " '__ixor__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lshift__',\n",
+       " '__lt__',\n",
+       " '__matmul__',\n",
+       " '__mod__',\n",
+       " '__mul__',\n",
+       " '__ne__',\n",
+       " '__neg__',\n",
+       " '__new__',\n",
+       " '__or__',\n",
+       " '__pos__',\n",
+       " '__pow__',\n",
+       " '__radd__',\n",
+       " '__rand__',\n",
+       " '__rdivmod__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rfloordiv__',\n",
+       " '__rlshift__',\n",
+       " '__rmatmul__',\n",
+       " '__rmod__',\n",
+       " '__rmul__',\n",
+       " '__ror__',\n",
+       " '__rpow__',\n",
+       " '__rrshift__',\n",
+       " '__rshift__',\n",
+       " '__rsub__',\n",
+       " '__rtruediv__',\n",
+       " '__rxor__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__setstate__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " '__truediv__',\n",
+       " '__xor__',\n",
+       " 'all',\n",
+       " 'any',\n",
+       " 'argmax',\n",
+       " 'argmin',\n",
+       " 'argpartition',\n",
+       " 'argsort',\n",
+       " 'astype',\n",
+       " 'base',\n",
+       " 'byteswap',\n",
+       " 'choose',\n",
+       " 'clip',\n",
+       " 'compress',\n",
+       " 'conj',\n",
+       " 'conjugate',\n",
+       " 'copy',\n",
+       " 'ctypes',\n",
+       " 'cumprod',\n",
+       " 'cumsum',\n",
+       " 'data',\n",
+       " 'diagonal',\n",
+       " 'dot',\n",
+       " 'dtype',\n",
+       " 'dump',\n",
+       " 'dumps',\n",
+       " 'fill',\n",
+       " 'flags',\n",
+       " 'flat',\n",
+       " 'flatten',\n",
+       " 'getfield',\n",
+       " 'imag',\n",
+       " 'item',\n",
+       " 'itemset',\n",
+       " 'itemsize',\n",
+       " 'max',\n",
+       " 'mean',\n",
+       " 'min',\n",
+       " 'nbytes',\n",
+       " 'ndim',\n",
+       " 'newbyteorder',\n",
+       " 'nonzero',\n",
+       " 'partition',\n",
+       " 'prod',\n",
+       " 'ptp',\n",
+       " 'put',\n",
+       " 'ravel',\n",
+       " 'real',\n",
+       " 'repeat',\n",
+       " 'reshape',\n",
+       " 'resize',\n",
+       " 'round',\n",
+       " 'searchsorted',\n",
+       " 'setfield',\n",
+       " 'setflags',\n",
+       " 'shape',\n",
+       " 'size',\n",
+       " 'sort',\n",
+       " 'squeeze',\n",
+       " 'std',\n",
+       " 'strides',\n",
+       " 'sum',\n",
+       " 'swapaxes',\n",
+       " 'take',\n",
+       " 'tobytes',\n",
+       " 'tofile',\n",
+       " 'tolist',\n",
+       " 'tostring',\n",
+       " 'trace',\n",
+       " 'transpose',\n",
+       " 'var',\n",
+       " 'view']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-2. -1.  0.  1.]\n",
+      " [-1.  0.  1.  1.]\n",
+      " [-1.  0.  1.  1.]]\n",
+      "float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = (array-array.mean())/array.std()\n",
+    "print(c)\n",
+    "print(c.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(array, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2., 3., 4., 4.])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.median(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* the other option would be to loop which is far more costly of an operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "13\n",
+      "13\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in array:\n",
+    "    print(sum(i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n",
+      "8\n",
+      "11\n",
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in array.T:\n",
+    "    print(sum(i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3, 4],\n",
+       "       [2, 3, 4, 4],\n",
+       "       [2, 3, 4, 4]])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### tranpose, turn rows into columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 2],\n",
+       "       [2, 3, 3],\n",
+       "       [3, 4, 4],\n",
+       "       [4, 4, 4]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">Make a 2-d array in numpy:</h3>\n",
+    "<p style=\"color:blue\">- find the row wise mean</p>\n",
+    "<p style=\"color:blue\">- find the column wise mean</p>\n",
+    "<p style=\"color:blue\">- do the same for the transpose of the array you've made</p>\n",
+    "<p style=\"color:blue\">- check if matrix square</p>\n",
+    "<p style=\"color:blue\">- multiple the matrix by transpose</p>\n",
+    "<p style=\"color:blue\">- find the z score for each value</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 2) float64\n",
+      "[1.80651136 1.7476596 ]\n",
+      "[0.19506737 1.55174101 1.80736258]\n",
+      "[0.19506737 1.55174101 1.80736258]\n",
+      "[1.80651136 1.7476596 ]\n",
+      "Not a square!\n",
+      "[0.34453012 2.75760428 3.21566298]\n",
+      "[0.34453012 2.75760428 3.21566298]\n",
+      "Square as me!\n",
+      "[[-1.05426807 -0.8533037  -0.82187756]\n",
+      " [-0.8533037   0.77785179  1.08527525]\n",
+      " [-0.82187756  1.08527525  1.45622829]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.random.random_sample((3,2))\n",
+    "print(a.shape,a.dtype)\n",
+    "print(a.sum(0))\n",
+    "print(a.sum(1))\n",
+    "\n",
+    "b = a.T\n",
+    "\n",
+    "print(b.sum(0))\n",
+    "print(b.sum(1))\n",
+    "\n",
+    "\n",
+    "if(a.shape[0]!=a.shape[1]):\n",
+    "    print(\"Not a square!\")\n",
+    "\n",
+    "\n",
+    "c = np.dot(a,b)\n",
+    "\n",
+    "print(c.sum(0))\n",
+    "print(c.sum(1))\n",
+    "\n",
+    "\n",
+    "if(c.shape[0]!=c.shape[1]):\n",
+    "    print(\"Not a square!\")\n",
+    "else:\n",
+    "    print(\"Square as me!\")\n",
+    "\n",
+    "def zmatrix(matrix):\n",
+    "    return (matrix-matrix.mean())/matrix.std()\n",
+    "print(zmatrix(c))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### other useful metrics\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.quantile.html\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1101, 2020, 3212],\n",
+       "       [2212, 3112, 4121]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([555.5, 546. , 454.5])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.std(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1656.5, 2566. , 3666.5])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1101, 2020, 3212])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.min(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2212, 3112, 4121])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(array, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1656.5, 2566. , 3666.5])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.quantile(array,.5, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1106.555, 2025.46 , 3216.545])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.percentile(array,.5, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### reshape\n",
+    "* change the shape of your data\n",
+    "* adding indices\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this is indexed by one element\n",
+    "array = np.array([1,2,3,4])\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4,)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 1)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.reshape(array, (4,1))\n",
+    "array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array[0][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we've now added two dimension using reshape\n",
+    "array1 = np.reshape(array,(2,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2],\n",
+       "       [3, 4]])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([1,2,3,4,5,6,7,8])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5, 6, 7, 8])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the next 8 numbers have 2x2x2 dimmensions\n",
+    "\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array1 = np.reshape(array, (2,2,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[1, 2],\n",
+       "        [3, 4]],\n",
+       "\n",
+       "       [[5, 6],\n",
+       "        [7, 8]]])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 2, 2)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2]\n",
+      " [3 4]] \n",
+      "\n",
+      "[[5 6]\n",
+      " [7 8]] \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in array1:\n",
+    "    print(i, \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### norm of a vector\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.norm.html\n",
+    "* defaults to the l2 norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.7416573867739413"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([1,2,3])\n",
+    "np.linalg.norm(array,None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.7416573867739413"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### matrix multiplication/dot product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2],\n",
+    "    [2,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6],\n",
+    "    [4,6]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[13, 18],\n",
+       "       [22, 30]])"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# shortcut\n",
+    "array@array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[13, 18],\n",
+       "       [22, 30]])"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.matmul(array, array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[13, 18],\n",
+       "       [22, 30]])"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dot(array, array1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### remember the dimension constraints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,2],\n",
+    "    [4,6,1]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3) (2, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(array.shape, array1.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "The dimensions aren't aligned",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-57-fe46837ad8c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0marray1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0marray1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
+     ]
+    }
+   ],
+   "source": [
+    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
+    "\n",
+    "array@array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3) (3, 6)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[19, 27, 10, 17, 14, 21],\n",
+       "       [28, 39, 13, 25, 21, 28]])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5],\n",
+    "    [2,3,2,2,1,3]\n",
+    "])\n",
+    "\n",
+    "print(array.shape, array1.shape)\n",
+    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
+    "\n",
+    "array@array1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
+    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
+    "<p style=\"color:blue\">- tests to make sure they can be multipled using assert</p>\n",
+    "<p style=\"color:blue\">- return the dot product of the two matrices</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[19 27 10 17 14 21]\n",
+      " [28 39 13 25 21 28]]\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "The dimensions aren't aligned",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0marray2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36mrafdot\u001b[1;34m(m1, m2)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0mm2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m array = np.array([\n",
+      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
+     ]
+    }
+   ],
+   "source": [
+    "def rafdot(m1, m2):\n",
+    "    assert m1.shape[1] == m2.shape[0], \"The dimensions aren't aligned\"\n",
+    "    return m1@m2\n",
+    "\n",
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5],\n",
+    "    [2,3,2,2,1,3]\n",
+    "])\n",
+    "\n",
+    "array2 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5]])\n",
+    "\n",
+    "c = rafdot(array,array1)\n",
+    "print(c)\n",
+    "\n",
+    "c = rafdot(array,array2)\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### broadcasting\n",
+    "* expand the dimension to add to a matrix\n",
+    "* https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3],\n",
+    "    [2,3,3]\n",
+    "])\n",
+    "\n",
+    "array1 = np.array([1,1])\n",
+    "array1 = np.reshape(array1, (2,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [2, 3, 3]])"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1],\n",
+       "       [1]])"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3, 4],\n",
+       "       [3, 4, 4]])"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.add(array, array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [1, 2, 2]])"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.subtract(array, array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3, 4],\n",
+       "       [3, 4, 4]])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array + array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2],\n",
+       "       [1, 2, 2]])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array - array1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### elementwise squaring\n",
+    "* this is the elementwise operations arrays that make arrays so powerful"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 4, 9],\n",
+       "       [4, 9, 9]], dtype=int32)"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1,  32, 243],\n",
+       "       [ 32, 243, 243]])"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.power(array, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
+    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
+    "<p style=\"color:blue\">- iterates the rows and columns and squares each element</p>\n",
+    "<p style=\"color:blue\">- puts the squared items in a list</p>\n",
+    "<p style=\"color:blue\">- returns the list</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[1, 4, 9],\n",
+      "       [4, 9, 9]], dtype=int32), array([[1],\n",
+      "       [1]], dtype=int32)]\n",
+      "<class 'list'>\n",
+      "[array([[1, 4, 9],\n",
+      "       [4, 9, 9]], dtype=int32), array([[1],\n",
+      "       [1]], dtype=int32)]\n",
+      "<class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "def raf_square(m1,m2):\n",
+    "    flist = []\n",
+    "    for k in m1, m2:\n",
+    "        c = k**2\n",
+    "        flist.append(c)\n",
+    "    return flist\n",
+    "\n",
+    "def andy_func(a,b):\n",
+    "    new_list = []\n",
+    "    d, c = a**2, b**2\n",
+    "    new_list.append(d)\n",
+    "    new_list.append(c)\n",
+    "    return new_list\n",
+    "\n",
+    "\n",
+    "\n",
+    "a = raf_square(array,array1)\n",
+    "print(a)\n",
+    "print(type(a))\n",
+    "b = andy_func(array,array1)\n",
+    "print(b)\n",
+    "print(type(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### argmax and min\n",
+    "* returns the index of the max/min value\n",
+    "* In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.\n",
+    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([[1,2,3,4,5],[10,2,3,4,5]])\n",
+    "np.argmax(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmin(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,6,3],\n",
+    "    [3,10,5],\n",
+    "    [15,8,9]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2], dtype=int64)"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(array, axis = 0) # column level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(array, axis = 1) # row level"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### let's say these are distances\n",
+    "* each row is an item\n",
+    "* and each column is the distance to a cluster\n",
+    "array = np.array(["
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,6,3],\n",
+    "    [3,10,5],\n",
+    "    [15,8,9]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this could then give us the index of the min distance\n",
+    "np.argmin(array, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "items = [\"item_A\", \"item_B\", \"item_C\"]\n",
+    "clusters = [\"A\", \"B\", \"C\"]\n",
+    "closest_cluster = np.argmin(array, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['A', 'A', 'B']"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "closest_cluster_labels = [clusters[idx] for idx in closest_cluster]\n",
+    "closest_cluster_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('item_A', 'A'), ('item_B', 'A'), ('item_C', 'B')]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(zip(items, closest_cluster_labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['item_A', 'A'],\n",
+       "       ['item_B', 'A'],\n",
+       "       ['item_C', 'B']], dtype='<U6')"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([\n",
+    "    items,\n",
+    "    closest_cluster_labels\n",
+    "]).T\n",
+    "\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['item_A', 'B'],\n",
+       "       ['item_B', 'B'],\n",
+       "       ['item_C', 'B']], dtype='<U6')"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array = np.array([\n",
+    "    items,\n",
+    "    [clusters[idx] for idx in np.argmin(array, axis = 1)]\n",
+    "]).T\n",
+    "\n",
+    "array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(np.array([1,1,2,2,5,5,5]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### argsort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([1,3,6,0,51,12])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 0, 1, 2, 5, 4], dtype=int64)"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_args = np.argsort(array)\n",
+    "sorted_args"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 3, 6, 12, 51]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[array[idx] for idx in sorted_args]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[12  0  8  9]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 0], dtype=int64)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "array = np.array([12,0,8,9])\n",
+    "\n",
+    "print(array)\n",
+    "np.argsort(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def myfunc(x):\n",
+    "    pos = np.argsort(x)\n",
+    "    items = [\"item_A\", \"item_B\", \"item_C\"]\n",
+    "    return items[pos]\n",
+    "\n",
+    "vectorized_func = myfunc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "only integer scalar arrays can be converted to a scalar index",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-132-86bed837003f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvectorized_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-131-98e43f64e76f>\u001b[0m in \u001b[0;36mmyfunc\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mpos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"item_A\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_B\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_C\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mvectorized_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmyfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index"
+     ]
+    }
+   ],
+   "source": [
+    "vectorized_func(array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distance Measures\n",
+    "* used in many ML algorithms\n",
+    "* rec systems, kmeans, knn, decision trees\n",
+    "* https://docs.scipy.org/doc/scipy/reference/spatial.distance.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### euclidean distance\n",
+    "* magnitude makes a difference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1,2,3])\n",
+    "b = np.array([4,2,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.605551275463989"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(sum((a-b)**2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### cosine similarity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6415330278717848"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dot(a, b)/(np.linalg.norm(a)*np.linalg.norm(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### jaccard coefficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [\"the\", \"old\", \"man\", \"by\", \"the\", \"sea\"]\n",
+    "b = [\"the\", \"old\", \"man\", \"by\", \"the\", \"chair\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def jaccard_similarity(a, b):\n",
+    "    return len(set(a).intersection(set(b))) / len(set(a).union(set(b)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.42857142857142855"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jaccard_similarity(a,b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### distance matrix\n",
+    "* pdist returns a reduced distance matrix, since they are symetrical\n",
+    "* squareform makes this a dense form"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.spatial.distance import pdist, squareform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.array([\n",
+    "    [1,2,3,4],\n",
+    "    [2,4,2,1],\n",
+    "    [2,3,4,5]\n",
+    "    \n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.        , 3.87298335, 2.        ],\n",
+       "       [3.87298335, 0.        , 4.58257569],\n",
+       "       [2.        , 4.58257569, 0.        ]])"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "squareform(pdist(array, \"euclidean\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2,)\n",
+      "(2,)\n",
+      "[0. 1.]\n",
+      "[[0. 1.]\n",
+      " [1. 1.]]\n",
+      "(array([0.8, 0.1]), array([], dtype=float64), 2, array([1.61803399, 0.61803399]))\n",
+      "y = 0.09999999999999992x + 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "xdata = np.array([0.0,1.0])\n",
+    "ydata = np.array([0.1,0.9])\n",
+    "\n",
+    "\n",
+    "print(ydata.shape)\n",
+    "print(xdata.shape)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "data_pred = xdata.T * xdata.T\n",
+    "print(data_pred)\n",
+    "\n",
+    "# the model has the following face y = bx + a*1  \n",
+    "\n",
+    "A = np.vstack([xdata,np.ones(len(xdata))]).T\n",
+    "\n",
+    "\n",
+    "print(A)\n",
+    "\n",
+    "\n",
+    "print (np.linalg.lstsq(A, ydata))\n",
+    "\n",
+    "\n",
+    "b, a = np.linalg.lstsq(A, ydata)[0]\n",
+    "print(\"y = \"+str(a)+'x + '+str(b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-194-db0e3b2aad31>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0misprime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipykernel\\iostream.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, string)\u001b[0m\n\u001b[0;32m    400\u001b[0m             \u001b[0mis_child\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_master_process\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m             \u001b[1;31m# only touch the buffer in the IO thread to avoid races\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_child\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m                 \u001b[1;31m# newlines imply flush in subprocesses\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipykernel\\iostream.py\u001b[0m in \u001b[0;36mschedule\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m    203\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_events\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m             \u001b[1;31m# wake event thread (message content is ignored)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 205\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_event_pipe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    206\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\zmq\\sugar\\socket.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data, flags, copy, track, routing_id, group)\u001b[0m\n\u001b[0;32m    398\u001b[0m                                  copy_threshold=self.copy_threshold)\n\u001b[0;32m    399\u001b[0m             \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 400\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSocket\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    401\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    402\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msend_multipart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg_parts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._send_copy\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\zmq\\backend\\cython\\checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "#find if a number is prime\n",
+    "#find if it is a prime of goldbach and the primes that build it\n",
+    "\n",
+    "\n",
+    "def isprime(number):\n",
+    "    if(number%2==0):\n",
+    "        if(number!=2):\n",
+    "            return 0\n",
+    "        else:\n",
+    "            return 1\n",
+    "    dividers = np.arange(3,number,2)\n",
+    "    #print(dividers)\n",
+    "    for k in dividers:\n",
+    "        if number%k==0:\n",
+    "            return 0\n",
+    "    return 1\n",
+    "\n",
+    "for k in np.arange(0,1000*1000,1):\n",
+    "    print(k,isprime(k))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb b/lectures/Lecture 5 - Pandas.ipynb
similarity index 75%
rename from lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb
rename to lectures/Lecture 5 - Pandas.ipynb
index 621a347..eaa6b33 100644
--- a/lectures/Lecture 4 and 5 - Numpy and Pandas.ipynb	
+++ b/lectures/Lecture 5 - Pandas.ipynb	
@@ -1,2342 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Numpy Arrays\n",
-    "* multi dimensional arrays or data\n",
-    "* can be 1d, 2d, 3d\n",
-    "* very computationally efficient to perform linear algebra operations over\n",
-    "* elementwise operations\n",
-    "* row and column wise calculations\n",
-    "* https://docs.scipy.org/doc/numpy-1.11.0/numpy-user-1.11.0.pdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Arrays vs. Matrix Class\n",
-    "* \"Short Answer: Use Arrays.\"\n",
-    "* They are the standard vector/matrix/tensor type of numpy. Many numpy functions return arrays, not matrices.\n",
-    "* The array class is intended to be a general-purposen-dimensional array for many kinds of numerical computing, whilematrixis intended to facilitate linear algebracomputations specifically."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1-D arrays\n",
-    "* note his only has one demension\n",
-    "* we could use a method called reshape to change this (we will get into this more later on)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3,4,5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5,)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5, 1)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = np.reshape(a, (5,1))\n",
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b[1,0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### indexing and slicing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[0:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 3])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in a:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### looping\n",
-    "* numpy loops through the left most axis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,4]\n",
-    "])\n",
-    "\n",
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 2 3]\n",
-      "[2 3 4]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# since we have 2 items in the first axis we will end up with 2 items in our iteration\n",
-    "for i in array:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,3,4,4],\n",
-    "    [2,3,4,4]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,7,4],\n",
-    "    [4,6,9,4],\n",
-    "    [2,3,4,4]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3, 4)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# depth 2\n",
-    "# rows 3\n",
-    "# columns 4\n",
-    "tensor = np.array([array, array1])\n",
-    "tensor.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* remember numpy will iterate left most index\n",
-    "* in this 3-d cube, it's iterating the dpeth\n",
-    "* so we end up printing our 2 initial matrices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3 4]\n",
-      " [2 3 4 4]\n",
-      " [2 3 4 4]]\n",
-      "[[5 6 7 4]\n",
-      " [4 6 9 4]\n",
-      " [2 3 4 4]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in tensor:\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### performing matrix operations\n",
-    "* axis 0 is the y axis\n",
-    "* the up and down axis\n",
-    "* the number of rows\n",
-    "* so any operation done on axis 0 is going to be columns\n",
-    "* done up and down\n",
-    "* axis 1 is the length of our array\n",
-    "* any summing or anything is done across our dataframe\n",
-    "* doing some operation to our rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,3,4,4],\n",
-    "    [2,3,4,4]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 4)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3, 4],\n",
-       "       [2, 3, 4, 4],\n",
-       "       [2, 3, 4, 4]])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 5,  8, 11, 12])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10, 13, 13])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('int32')"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2., 3., 4., 4.])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.median(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* the other option would be to loop which is far more costly of an operation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "13\n",
-      "13\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array:\n",
-    "    print(sum(i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "8\n",
-      "11\n",
-      "12\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array.T:\n",
-    "    print(sum(i))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3, 4],\n",
-       "       [2, 3, 4, 4],\n",
-       "       [2, 3, 4, 4]])"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### tranpose, turn rows into columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 2],\n",
-       "       [2, 3, 3],\n",
-       "       [3, 4, 4],\n",
-       "       [4, 4, 4]])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make a 2-d array in numpy:</h3>\n",
-    "<p style=\"color:blue\">- find the row wise mean</p>\n",
-    "<p style=\"color:blue\">- find the column wise mean</p>\n",
-    "<p style=\"color:blue\">- do the same for the transpose of the array you've made</p>\n",
-    "<p style=\"color:blue\">- check if matrix square</p>\n",
-    "<p style=\"color:blue\">- multiple the matrix by transpose</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[3. 3. 3. 3.]\n",
-      "[4. 4. 4.]\n",
-      "[4. 4. 4.]\n",
-      "[3. 3. 3. 3.]\n",
-      "Not a square!\n",
-      "[12. 12. 12.]\n",
-      "[12. 12. 12.]\n",
-      "Square as me!\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = np.ones([3,4])\n",
-    "\n",
-    "\n",
-    "print(a.sum(0))\n",
-    "print(a.sum(1))\n",
-    "\n",
-    "b = a.T\n",
-    "\n",
-    "print(b.sum(0))\n",
-    "print(b.sum(1))\n",
-    "\n",
-    "\n",
-    "if(a.shape[0]!=a.shape[1]):\n",
-    "    print(\"Not a square!\")\n",
-    "\n",
-    "\n",
-    "c = np.dot(a,b)\n",
-    "\n",
-    "print(c.sum(0))\n",
-    "print(c.sum(1))\n",
-    "\n",
-    "\n",
-    "if(c.shape[0]!=c.shape[1]):\n",
-    "    print(\"Not a square!\")\n",
-    "else:\n",
-    "    print(\"Square as me!\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### other useful metrics\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.quantile.html\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1101, 2020, 3212],\n",
-       "       [2212, 3112, 4121]])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([555.5, 546. , 454.5])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.std(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1656.5, 2566. , 3666.5])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1101, 2020, 3212])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2212, 3112, 4121])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.max(array, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1656.5, 2566. , 3666.5])"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.quantile(array,.5, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1106.555, 2025.46 , 3216.545])"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.percentile(array,.5, axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### reshape\n",
-    "* change the shape of your data\n",
-    "* adding indices\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4])"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# this is indexed by one element\n",
-    "array = np.array([1,2,3,4])\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4,)"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4, 1)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.reshape(array, (4,1))\n",
-    "array.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array[0][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we've now added two dimension using reshape\n",
-    "array1 = np.reshape(array,(2,2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2],\n",
-       "       [3, 4]])"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([1,2,3,4,5,6,7,8])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4, 5, 6, 7, 8])"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array1 = np.reshape(array, (2,2,2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[1, 2],\n",
-       "        [3, 4]],\n",
-       "\n",
-       "       [[5, 6],\n",
-       "        [7, 8]]])"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2, 2)"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2]\n",
-      " [3 4]] \n",
-      "\n",
-      "[[5 6]\n",
-      " [7 8]] \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in array1:\n",
-    "    print(i, \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### norm of a vector\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.norm.html\n",
-    "* defaults to the l2 norm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.7416573867739413"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([1,2,3])\n",
-    "np.linalg.norm(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.7416573867739413"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sqrt(14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### matrix multiplication/dot product"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2],\n",
-    "    [2,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6],\n",
-    "    [4,6]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# shortcut\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.matmul(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[13, 18],\n",
-       "       [22, 30]])"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.dot(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### remember the dimension constraints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,2],\n",
-    "    [4,6,1]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3) (2, 3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(array.shape, array1.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "The dimensions aren't aligned",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-57-fe46837ad8c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0marray1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0marray1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
-     ]
-    }
-   ],
-   "source": [
-    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
-    "\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 3) (3, 6)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[19, 27, 10, 17, 14, 21],\n",
-       "       [28, 39, 13, 25, 21, 28]])"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5],\n",
-    "    [2,3,2,2,1,3]\n",
-    "])\n",
-    "\n",
-    "print(array.shape, array1.shape)\n",
-    "assert array.shape[1] == array1.shape[0], \"The dimensions aren't aligned\"\n",
-    "\n",
-    "array@array1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
-    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
-    "<p style=\"color:blue\">- tests to make sure they can be multipled using assert</p>\n",
-    "<p style=\"color:blue\">- return the dot product of the two matrices</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[19 27 10 17 14 21]\n",
-      " [28 39 13 25 21 28]]\n"
-     ]
-    },
-    {
-     "ename": "AssertionError",
-     "evalue": "The dimensions aren't aligned",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0marray2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-60-53aa43397e46>\u001b[0m in \u001b[0;36mrafdot\u001b[1;34m(m1, m2)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrafdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32massert\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"The dimensions aren't aligned\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mm1\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0mm2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m array = np.array([\n",
-      "\u001b[1;31mAssertionError\u001b[0m: The dimensions aren't aligned"
-     ]
-    }
-   ],
-   "source": [
-    "def rafdot(m1, m2):\n",
-    "    assert m1.shape[1] == m2.shape[0], \"The dimensions aren't aligned\"\n",
-    "    return m1@m2\n",
-    "\n",
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5],\n",
-    "    [2,3,2,2,1,3]\n",
-    "])\n",
-    "\n",
-    "array2 = np.array([\n",
-    "    [5,6,2,5,3,2],\n",
-    "    [4,6,1,3,4,5]])\n",
-    "\n",
-    "c = rafdot(array,array1)\n",
-    "print(c)\n",
-    "\n",
-    "c = rafdot(array,array2)\n",
-    "print(c)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### broadcasting\n",
-    "* expand the dimension to add to a matrix\n",
-    "* https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3],\n",
-    "    [2,3,3]\n",
-    "])\n",
-    "\n",
-    "array1 = np.array([1,1])\n",
-    "array1 = np.reshape(array1, (2,1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [2, 3, 3]])"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1],\n",
-       "       [1]])"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 3, 4],\n",
-       "       [3, 4, 4]])"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.add(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [1, 2, 2]])"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.subtract(array, array1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 3, 4],\n",
-       "       [3, 4, 4]])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array + array1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [1, 2, 2]])"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array - array1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### elementwise squaring\n",
-    "* this is the elementwise operations arrays that make arrays so powerful"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1,  32, 243],\n",
-       "       [ 32, 243, 243]])"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array**5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  1,  32, 243],\n",
-       "       [ 32, 243, 243]])"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.power(array, 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">Make udf that:</h3>\n",
-    "<p style=\"color:blue\">- takes 2 2-d arrays as params</p>\n",
-    "<p style=\"color:blue\">- iterates the rows and columns and squares each element</p>\n",
-    "<p style=\"color:blue\">- puts the squared items in a list</p>\n",
-    "<p style=\"color:blue\">- returns the list</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array([[1, 4, 9],\n",
-       "        [4, 9, 9]]), array([[25, 36,  4, 25,  9,  4],\n",
-       "        [16, 36,  1,  9, 16, 25],\n",
-       "        [ 4,  9,  4,  4,  1,  9]])]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def raf_square(m1,m2):\n",
-    "    flist = []\n",
-    "    for k in m1, m2:\n",
-    "        c = k*k\n",
-    "        flist.append(c)\n",
-    "    return flist\n",
-    "\n",
-    "raf_square(array,array1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### argmax and min\n",
-    "* returns the index of the max/min value\n",
-    "* In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.\n",
-    "* https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([1,2,3,4,5])\n",
-    "np.argmax(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmin(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3],\n",
-    "    [3,10,5],\n",
-    "    [15,8,9]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 1, 2])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(array, axis = 0) # column level"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 1, 0])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(array, axis = 1) # row level"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### let's say these are distances\n",
-    "* each row is an item\n",
-    "* and each column is the distance to a cluster\n",
-    "array = np.array(["
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3],\n",
-    "    [3,10,5],\n",
-    "    [15,8,9]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 1], dtype=int64)"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# this could then give us the index of the min distance\n",
-    "np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "items = [\"item_A\", \"item_B\", \"item_C\"]\n",
-    "clusters = [\"A\", \"B\", \"C\"]\n",
-    "closest_cluster = np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['A', 'A', 'B']"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "closest_cluster_labels = [clusters[idx] for idx in closest_cluster]\n",
-    "closest_cluster_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('item_A', 'A'), ('item_B', 'A'), ('item_C', 'B')]"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(zip(items, closest_cluster_labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['item_A', 'A'],\n",
-       "       ['item_B', 'A'],\n",
-       "       ['item_C', 'B']], dtype='<U6')"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    items,\n",
-    "    closest_cluster_labels\n",
-    "]).T\n",
-    "\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['item_A', 'B'],\n",
-       "       ['item_B', 'B'],\n",
-       "       ['item_C', 'B']], dtype='<U6')"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array = np.array([\n",
-    "    items,\n",
-    "    [clusters[idx] for idx in np.argmin(array, axis = 1)]\n",
-    "]).T\n",
-    "\n",
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argmax(np.array([1,1,2,2,5,5,5]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### argsort"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([1,3,6,0,51,12])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sorted_args = np.argsort(array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 3, 6, 12, 51]"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[array[idx] for idx in sorted_args]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,6,3],\n",
-    "    [3,10,5],\n",
-    "    [15,8,9]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "items = [\"item_A\", \"item_B\", \"item_C\"]\n",
-    "clusters = [\"A\", \"B\", \"C\"]\n",
-    "closest_cluster = np.argmin(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1,  6,  3],\n",
-       "       [ 3, 10,  5],\n",
-       "       [15,  8,  9]])"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 2, 1],\n",
-       "       [0, 2, 1],\n",
-       "       [1, 2, 0]], dtype=int64)"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.argsort(array, axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def myfunc(x):\n",
-    "    items = [\"item_A\", \"item_B\", \"item_C\"]\n",
-    "    return items[x]\n",
-    "\n",
-    "vectorized_func = np.vectorize(myfunc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-84-86bed837003f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvectorized_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2089\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2091\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2092\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2093\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[1;34m(self, func, args)\u001b[0m\n\u001b[0;32m   2165\u001b[0m                       for a in args]\n\u001b[0;32m   2166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2167\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2169\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-83-df07eceacbb6>\u001b[0m in \u001b[0;36mmyfunc\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmyfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"item_A\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_B\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"item_C\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mvectorized_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvectorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmyfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-   "source": [
-    "vectorized_func(array)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distance Measures\n",
-    "* used in many ML algorithms\n",
-    "* rec systems, kmeans, knn, decision trees\n",
-    "* https://docs.scipy.org/doc/scipy/reference/spatial.distance.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### euclidean distance\n",
-    "* magnitude makes a difference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3])\n",
-    "b = np.array([4,2,1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.605551275463989"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sqrt(sum((a-b)**2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### cosine similarity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6415330278717848"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.dot(a, b)/(np.linalg.norm(a)*np.linalg.norm(b))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### jaccard coefficient"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [\"the\", \"old\", \"man\", \"by\", \"the\", \"sea\"]\n",
-    "b = [\"the\", \"old\", \"man\", \"by\", \"the\", \"chair\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def jaccard_similarity(a, b):\n",
-    "    return len(set(a).intersection(set(b))) / len(set(a).union(set(b)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6666666666666666"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "jaccard_similarity(a,b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### distance matrix\n",
-    "* pdist returns a reduced distance matrix, since they are symetrical\n",
-    "* squareform makes this a dense form"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.spatial.distance import pdist, squareform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "array = np.array([\n",
-    "    [1,2,3,4],\n",
-    "    [2,4,2,1],\n",
-    "    [2,3,4,5]\n",
-    "    \n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.        , 3.87298335, 2.        ],\n",
-       "       [3.87298335, 0.        , 4.58257569],\n",
-       "       [2.        , 4.58257569, 0.        ]])"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squareform(pdist(array, \"euclidean\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.1 0.9]\n",
-      "[0. 1.]\n",
-      "[[0. 1.]\n",
-      " [1. 1.]]\n",
-      "(array([0.8, 0.1]), array([], dtype=float64), 2, array([1.61803399, 0.61803399]))\n",
-      "y = 0.09999999999999992x + 0.8\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
-      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
-      "  \n",
-      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
-      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "xdata = np.array([0.0,1.0])\n",
-    "ydata = np.array([0.1,0.9])\n",
-    "\n",
-    "data_pred = xdata.T * xdata.T\n",
-    "\n",
-    "A = np.vstack([xdata,np.ones(len(xdata))]).T\n",
-    "\n",
-    "\n",
-    "print(ydata)\n",
-    "print(xdata)\n",
-    "print(A)\n",
-    "\n",
-    "\n",
-    "print (np.linalg.lstsq(A, ydata))\n",
-    "\n",
-    "# the model has the following face y = a + bx\n",
-    "b, a = np.linalg.lstsq(A, ydata)[0]\n",
-    "print(\"y = \"+str(a)+'x + '+str(b))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
-     ]
-    }
-   ],
-   "source": [
-    "#find if a number is prime\n",
-    "#find if it is a prime of goldbach and the primes that build it\n",
-    "\n",
-    "\n",
-    "def isprime(number):\n",
-    "    if(number%2):\n",
-    "        if(number!=2):\n",
-    "            return 0\n",
-    "        else:\n",
-    "            return 1\n",
-    "    dividers = np.arange(number,1,2)\n",
-    "    print(dividers)\n",
-    "    for k in dividers:\n",
-    "        if number%divider:\n",
-    "            return 0\n",
-    "    return 1 \n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 38371c9b70997c31f77e4db46b5ae31eb6f6cfae Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Wed, 13 May 2020 17:56:01 -0500
Subject: [PATCH 35/36] Class 6

---
 Lecture 6 - Saving Object.ipynb               |  277 +++
 assigments/NAME_SURNAME_ASSIGN_2_1.ipynb      |  114 +
 data/class.json                               |    1 +
 lectures/Lecture 5 - Pandas.ipynb             | 1971 ++++++++++-------
 .../Lecture 6 - Matplotlib, Seaborn.ipynb     |  278 +--
 lectures/Lecture 6 - Saving objects.ipynb     |  268 +++
 6 files changed, 1952 insertions(+), 957 deletions(-)
 create mode 100644 Lecture 6 - Saving Object.ipynb
 create mode 100644 data/class.json
 create mode 100644 lectures/Lecture 6 - Saving objects.ipynb

diff --git a/Lecture 6 - Saving Object.ipynb b/Lecture 6 - Saving Object.ipynb
new file mode 100644
index 0000000..f32c9bb
--- /dev/null
+++ b/Lecture 6 - Saving Object.ipynb	
@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving Objects in Python\n",
+    "* serialization, marshaling, and flattening. \n",
+    "* same—to save an object to a file for later retrieval\n",
+    "* accomplishes this by writing the object as one long stream of bytes. \n",
+    "* pickling also used to save models in sklearn\n",
+    "* https://docs.python.org/3/library/pickle.html\n",
+    "* https://scikit-learn.org/stable/modules/model_persistence.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    def __init__(self, name):\n",
+    "        self.name = name\n",
+    "        \n",
+    "d = Dog(\"Brian\")\n",
+    "\n",
+    "assert hasattr(d, \"name\"), \"this object doesn't have attribuet namee\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = [\"a\", \"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open('test_pickle', 'wb') \n",
+    "pickle.dump(test, file)\n",
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b']"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delete from memory\n",
+    "del test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'test' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-143-4e1243bd22c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'test' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"/Users/conagrabrands/Desktop\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open('test_pickle', 'rb') \n",
+    "test = pickle.load(file)\n",
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b']"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### using with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "item = \"some string I made\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test_pickle.pkl\", \"wb\") as file:\n",
+    "    pickle.dump(item, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'item' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-154-8cd64e89cea0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'item' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "del item\n",
+    "item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test_pickle.pkl\", \"rb\") as file:\n",
+    "    item = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'some string I made'"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "item"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a python object</h3>\n",
+    "<p style=\"color:blue\">- save the object</p>\n",
+    "<p style=\"color:blue\">- delete the object from memory</p>\n",
+    "<p style=\"color:blue\">- reload it</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb b/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb
index 6e7b13f..a745a5c 100644
--- a/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb
+++ b/assigments/NAME_SURNAME_ASSIGN_2_1.ipynb
@@ -32,6 +32,120 @@
     "iris = load_iris()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setosa\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>Variety</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "1                4.9               3.0                1.4               0.2   \n",
+       "2                4.7               3.2                1.3               0.2   \n",
+       "3                4.6               3.1                1.5               0.2   \n",
+       "4                5.0               3.6                1.4               0.2   \n",
+       "\n",
+       "   Variety  \n",
+       "0        0  \n",
+       "1        0  \n",
+       "2        0  \n",
+       "3        0  \n",
+       "4        0  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=pd.DataFrame(iris.data, columns=iris.feature_names)\n",
+    "df['Variety'] = iris.target\n",
+    "print(iris['target_names'][0])\n",
+    "#df['Variety'] = df['Variety'].apply(lambda x: iris['target_names'][x])\n",
+    "df.head(5)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/data/class.json b/data/class.json
new file mode 100644
index 0000000..e2c3ddf
--- /dev/null
+++ b/data/class.json
@@ -0,0 +1 @@
+{"age":{"0":31,"1":28,"2":28},"job":{"0":"data scientist","1":"data scientist","2":null},"city":{"0":"chicago","1":"new york","2":null}}
\ No newline at end of file
diff --git a/lectures/Lecture 5 - Pandas.ipynb b/lectures/Lecture 5 - Pandas.ipynb
index eaa6b33..70f6e2d 100644
--- a/lectures/Lecture 5 - Pandas.ipynb	
+++ b/lectures/Lecture 5 - Pandas.ipynb	
@@ -15,11 +15,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import numpy as np"
    ]
   },
   {
@@ -33,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,24 +46,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "a    1\n",
-       "b    2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a    1\n",
+      "b    2\n",
+      "c    3\n",
+      "d    4\n",
+      "e    5\n",
+      "dtype: int64\n",
+      "a    1\n",
+      "b    2\n",
+      "dtype: int64\n"
+     ]
     }
    ],
    "source": [
-    "my_series[[\"a\", \"b\"]]"
+    "print(my_series)\n",
+    "\n",
+    "print(my_series[[\"a\", \"b\"]])"
    ]
   },
   {
@@ -78,29 +84,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 6)\n"
+     ]
+    }
+   ],
    "source": [
     "array1 = np.array([\n",
     "    [5,6,2,5,3,2],\n",
     "    [4,6,1,3,4,5],\n",
     "    [2,3,2,2,1,3]\n",
-    "])"
+    "])\n",
+    "print(array1.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 6)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df = pd.DataFrame(array1)"
+    "array1 = np.array([\n",
+    "    [5,6,2,5,3,2],\n",
+    "    [4,6,1,3,4,5],\n",
+    "    [2,3,2,2,1,3]\n",
+    "])\n",
+    "df = pd.DataFrame(array1)\n",
+    "df.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -553,7 +585,7 @@
        " 'xs']"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -564,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -615,6 +647,15 @@
        "      <td>4</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
@@ -622,21 +663,22 @@
       "text/plain": [
        "   0  1  2  3  4  5\n",
        "0  5  6  2  5  3  2\n",
-       "1  4  6  1  3  4  5"
+       "1  4  6  1  3  4  5\n",
+       "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head(2)"
+    "df.head(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -707,7 +749,7 @@
        "2  2  3  2  2  1  3"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -718,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -727,7 +769,7 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -738,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -747,7 +789,7 @@
        "RangeIndex(start=0, stop=6, step=1)"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -765,16 +807,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.columns = [\"R\", \"b\", \"c\", \"d\", \"e\", \"f\"]"
+    "df.columns = [\"R\", \"A\", \"F\", \"4\", \"Echo\", \"Lima\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -799,11 +841,11 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>e</th>\n",
-       "      <th>f</th>\n",
+       "      <th>A</th>\n",
+       "      <th>F</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>Lima</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -839,13 +881,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   R  b  c  d  e  f\n",
-       "0  5  6  2  5  3  2\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
+       "   R  A  F  4  Echo  Lima\n",
+       "0  5  6  2  5     3     2\n",
+       "1  4  6  1  3     4     5\n",
+       "2  2  3  2  2     1     3"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -856,22 +898,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R\n",
+      "A\n",
+      "F\n",
+      "4\n",
+      "Echo\n",
+      "Lima\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k in range(len(df.columns)):\n",
+    "    print(df.columns[k])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "R    int32\n",
-       "b    int32\n",
-       "c    int32\n",
-       "d    int32\n",
-       "e    int32\n",
-       "f    int32\n",
+       "R       int32\n",
+       "A       int32\n",
+       "F       int32\n",
+       "4       int32\n",
+       "Echo    int32\n",
+       "Lima    int32\n",
        "dtype: object"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -891,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -903,7 +968,7 @@
        "Name: R, dtype: int32"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -914,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -923,7 +988,7 @@
        "pandas.core.series.Series"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -934,16 +999,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[5, 4, 2]"
+       "list"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -954,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -963,7 +1028,7 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -974,7 +1039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -998,44 +1063,44 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>c</th>\n",
-       "      <th>b</th>\n",
+       "      <th>R</th>\n",
+       "      <th>Echo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   c  b\n",
-       "0  2  6\n",
-       "1  1  6\n",
-       "2  2  3"
+       "   R  Echo\n",
+       "0  5     3\n",
+       "1  4     4\n",
+       "2  2     1"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[[\"c\", \"b\"]]"
+    "df[[\"R\", \"Echo\"]]"
    ]
   },
   {
@@ -1054,22 +1119,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "R    5\n",
-       "b    6\n",
-       "c    2\n",
-       "d    5\n",
-       "e    3\n",
-       "f    2\n",
+       "R       5\n",
+       "A       6\n",
+       "F       2\n",
+       "4       5\n",
+       "Echo    3\n",
+       "Lima    2\n",
        "Name: 0, dtype: int32"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1080,31 +1145,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "R    4\n",
-       "b    6\n",
-       "c    1\n",
-       "Name: 1, dtype: int32"
+       "R    5\n",
+       "A    6\n",
+       "F    2\n",
+       "Name: 0, dtype: int32"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# need integer based subsetting\n",
-    "df.iloc[1,[0,1,2]]"
+    "df.iloc[0,[0,1,2]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1129,22 +1194,22 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>e</th>\n",
-       "      <th>f</th>\n",
+       "      <th>A</th>\n",
+       "      <th>F</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>Lima</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
+       "      <td>2</td>\n",
        "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
@@ -1160,23 +1225,23 @@
        "</div>"
       ],
       "text/plain": [
-       "   R  b  c  d  e  f\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
+       "   R  A  F  4  Echo  Lima\n",
+       "0  5  6  2  5     3     2\n",
+       "2  2  3  2  2     1     3"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.iloc[[1,2]]"
+    "df.iloc[[0,2]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1201,31 +1266,31 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>e</th>\n",
-       "      <th>f</th>\n",
+       "      <th>A</th>\n",
+       "      <th>F</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>Lima</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
        "      <td>2</td>\n",
-       "      <td>5</td>\n",
+       "      <td>2</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>5</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
@@ -1241,24 +1306,24 @@
        "</div>"
       ],
       "text/plain": [
-       "   R  b  c  d  e  f\n",
-       "0  5  6  2  5  3  2\n",
-       "2  2  3  2  2  1  3\n",
-       "1  4  6  1  3  4  5"
+       "   R  A  F  4  Echo  Lima\n",
+       "2  2  3  2  2     1     3\n",
+       "0  5  6  2  5     3     2\n",
+       "1  4  6  1  3     4     5"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.iloc[[0,2,1]]"
+    "df.iloc[[2,0,1]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1283,11 +1348,11 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>e</th>\n",
-       "      <th>f</th>\n",
+       "      <th>A</th>\n",
+       "      <th>F</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>Lima</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1323,13 +1388,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   R  b  c  d  e  f\n",
-       "0  5  6  2  5  3  2\n",
-       "1  4  6  1  3  4  5\n",
-       "2  2  3  2  2  1  3"
+       "   R  A  F  4  Echo  Lima\n",
+       "0  5  6  2  5     3     2\n",
+       "1  4  6  1  3     4     5\n",
+       "2  2  3  2  2     1     3"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1340,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1391,7 +1456,7 @@
        "2  2"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1402,7 +1467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1427,7 +1492,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>b</th>\n",
+       "      <th>A</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1441,11 +1506,11 @@
        "</div>"
       ],
       "text/plain": [
-       "   R  b\n",
+       "   R  A\n",
        "1  4  6"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1464,7 +1529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1473,7 +1538,7 @@
        "RangeIndex(start=0, stop=3, step=1)"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1484,7 +1549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1493,7 +1558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1518,11 +1583,11 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>e</th>\n",
-       "      <th>f</th>\n",
+       "      <th>A</th>\n",
+       "      <th>F</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>Lima</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1558,13 +1623,13 @@
        "</div>"
       ],
       "text/plain": [
-       "    R  b  c  d  e  f\n",
-       "aa  5  6  2  5  3  2\n",
-       "bb  4  6  1  3  4  5\n",
-       "cc  2  3  2  2  1  3"
+       "    R  A  F  4  Echo  Lima\n",
+       "aa  5  6  2  5     3     2\n",
+       "bb  4  6  1  3     4     5\n",
+       "cc  2  3  2  2     1     3"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1575,7 +1640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1590,7 +1655,7 @@
        "Name: aa, dtype: int32"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1601,7 +1666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1626,11 +1691,11 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
-       "      <th>e</th>\n",
-       "      <th>f</th>\n",
+       "      <th>A</th>\n",
+       "      <th>F</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>Lima</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1657,12 +1722,12 @@
        "</div>"
       ],
       "text/plain": [
-       "    R  b  c  d  e  f\n",
-       "aa  5  6  2  5  3  2\n",
-       "bb  4  6  1  3  4  5"
+       "    R  A  F  4  Echo  Lima\n",
+       "aa  5  6  2  5     3     2\n",
+       "bb  4  6  1  3     4     5"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1673,7 +1738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1698,31 +1763,31 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>b</th>\n",
+       "      <th>Echo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>aa</td>\n",
        "      <td>5</td>\n",
-       "      <td>6</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    R  b\n",
-       "aa  5  6"
+       "    R  Echo\n",
+       "aa  5     3"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.loc[[\"aa\"], [\"R\",\"b\"]]"
+    "df.loc[[\"aa\"], [\"R\",\"Echo\"]]"
    ]
   },
   {
@@ -1736,7 +1801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1761,38 +1826,38 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>R</th>\n",
-       "      <th>c</th>\n",
+       "      <th>Echo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>aa</td>\n",
        "      <td>5</td>\n",
-       "      <td>2</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>bb</td>\n",
        "      <td>4</td>\n",
-       "      <td>1</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    R  c\n",
-       "aa  5  2\n",
-       "bb  4  1"
+       "    R  Echo\n",
+       "aa  5     3\n",
+       "bb  4     4"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "rows = [\"aa\", \"bb\"]\n",
-    "cols = [\"R\", \"c\"]\n",
+    "cols = [\"R\", \"Echo\"]\n",
     "\n",
     "df.loc[rows,cols]"
    ]
@@ -1806,16 +1871,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df1 = df[[ \"b\", \"c\", \"d\"]]"
+    "df1 = df[[ \"R\", \"4\", \"Echo\"]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1839,42 +1904,42 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
+       "      <th>R</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>aa</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
        "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>bb</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
+       "      <td>4</td>\n",
        "      <td>3</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>cc</td>\n",
-       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    b  c  d\n",
-       "aa  6  2  5\n",
-       "bb  6  1  3\n",
-       "cc  3  2  2"
+       "    R  4  Echo\n",
+       "aa  5  5     3\n",
+       "bb  4  3     4\n",
+       "cc  2  2     1"
       ]
      },
-     "execution_count": 185,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1892,7 +1957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1916,9 +1981,12 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
+       "      <th>R</th>\n",
+       "      <th>A</th>\n",
+       "      <th>F</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>Lima</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1927,72 +1995,96 @@
        "      <td>3.000000</td>\n",
        "      <td>3.000000</td>\n",
        "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>mean</td>\n",
+       "      <td>3.666667</td>\n",
        "      <td>5.000000</td>\n",
        "      <td>1.666667</td>\n",
        "      <td>3.333333</td>\n",
+       "      <td>2.666667</td>\n",
+       "      <td>3.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>std</td>\n",
+       "      <td>1.527525</td>\n",
        "      <td>1.732051</td>\n",
        "      <td>0.577350</td>\n",
        "      <td>1.527525</td>\n",
+       "      <td>1.527525</td>\n",
+       "      <td>1.527525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>min</td>\n",
+       "      <td>2.000000</td>\n",
        "      <td>3.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>25%</td>\n",
+       "      <td>3.000000</td>\n",
        "      <td>4.500000</td>\n",
        "      <td>1.500000</td>\n",
        "      <td>2.500000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>50%</td>\n",
+       "      <td>4.000000</td>\n",
        "      <td>6.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>75%</td>\n",
+       "      <td>4.500000</td>\n",
        "      <td>6.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>4.000000</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>4.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>max</td>\n",
+       "      <td>5.000000</td>\n",
        "      <td>6.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              b         c         d\n",
-       "count  3.000000  3.000000  3.000000\n",
-       "mean   5.000000  1.666667  3.333333\n",
-       "std    1.732051  0.577350  1.527525\n",
-       "min    3.000000  1.000000  2.000000\n",
-       "25%    4.500000  1.500000  2.500000\n",
-       "50%    6.000000  2.000000  3.000000\n",
-       "75%    6.000000  2.000000  4.000000\n",
-       "max    6.000000  2.000000  5.000000"
+       "              R         A         F         4      Echo      Lima\n",
+       "count  3.000000  3.000000  3.000000  3.000000  3.000000  3.000000\n",
+       "mean   3.666667  5.000000  1.666667  3.333333  2.666667  3.333333\n",
+       "std    1.527525  1.732051  0.577350  1.527525  1.527525  1.527525\n",
+       "min    2.000000  3.000000  1.000000  2.000000  1.000000  2.000000\n",
+       "25%    3.000000  4.500000  1.500000  2.500000  2.000000  2.500000\n",
+       "50%    4.000000  6.000000  2.000000  3.000000  3.000000  3.000000\n",
+       "75%    4.500000  6.000000  2.000000  4.000000  3.500000  4.000000\n",
+       "max    5.000000  6.000000  2.000000  5.000000  4.000000  5.000000"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df1.describe()"
+    "df.describe()"
    ]
   },
   {
@@ -2004,7 +2096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -2028,28 +2120,28 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
+       "      <th>R</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>b</td>\n",
+       "      <td>R</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>0.755929</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.785714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>c</td>\n",
-       "      <td>-0.500000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.928571</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.188982</td>\n",
+       "      <td>0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>d</td>\n",
-       "      <td>0.755929</td>\n",
-       "      <td>0.188982</td>\n",
+       "      <td>Echo</td>\n",
+       "      <td>0.785714</td>\n",
+       "      <td>0.500000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2057,13 +2149,13 @@
        "</div>"
       ],
       "text/plain": [
-       "          b         c         d\n",
-       "b  1.000000 -0.500000  0.755929\n",
-       "c -0.500000  1.000000  0.188982\n",
-       "d  0.755929  0.188982  1.000000"
+       "             R         4      Echo\n",
+       "R     1.000000  0.928571  0.785714\n",
+       "4     0.928571  1.000000  0.500000\n",
+       "Echo  0.785714  0.500000  1.000000"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2081,19 +2173,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "b    15\n",
-       "c     5\n",
-       "d    10\n",
+       "R       11\n",
+       "4       10\n",
+       "Echo     8\n",
        "dtype: int64"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2104,7 +2196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -2129,19 +2221,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "b    6.0\n",
-       "c    2.0\n",
-       "d    3.0\n",
+       "R       4.0\n",
+       "4       3.0\n",
+       "Echo    3.0\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2159,7 +2251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -2176,12 +2268,12 @@
     }
    ],
    "source": [
-    "df1[\"class\"] = [\"AA\", \"AA\", \"BB\"]"
+    "df1[\"new_class\"] = [\"AA\", \"AA\",\"\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -2205,46 +2297,50 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
+       "      <th>R</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
        "      <th>class</th>\n",
+       "      <th>new_class</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>aa</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
        "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>AA</td>\n",
        "      <td>AA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>bb</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
+       "      <td>4</td>\n",
        "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AA</td>\n",
        "      <td>AA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>cc</td>\n",
-       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "      <td>BB</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    b  c  d class\n",
-       "aa  6  2  5    AA\n",
-       "bb  6  1  3    AA\n",
-       "cc  3  2  2    BB"
+       "    R  4  Echo class new_class\n",
+       "aa  5  5     3    AA        AA\n",
+       "bb  4  3     4    AA        AA\n",
+       "cc  2  2     1    BB          "
       ]
      },
-     "execution_count": 198,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2255,7 +2351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -2279,9 +2375,9 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
+       "      <th>R</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>class</th>\n",
@@ -2293,28 +2389,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>AA</td>\n",
-       "      <td>12</td>\n",
-       "      <td>3</td>\n",
+       "      <td>9</td>\n",
        "      <td>8</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>BB</td>\n",
-       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "        b  c  d\n",
-       "class          \n",
-       "AA     12  3  8\n",
-       "BB      3  2  2"
+       "       R  4  Echo\n",
+       "class            \n",
+       "AA     9  8     7\n",
+       "BB     2  2     1"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2325,7 +2421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -2349,15 +2445,17 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
+       "      <th>R</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>new_class</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>class</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2366,25 +2464,27 @@
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>BB</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       b  c  d\n",
-       "class         \n",
-       "AA     2  2  2\n",
-       "BB     1  1  1"
+       "       R  4  Echo  new_class\n",
+       "class                       \n",
+       "AA     2  2     2          2\n",
+       "BB     1  1     1          1"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2395,7 +2495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -2419,10 +2519,11 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
+       "      <th>R</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
        "      <th>class</th>\n",
+       "      <th>new_class</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>class</th>\n",
@@ -2430,15 +2531,17 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>AA</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>BB</td>\n",
@@ -2446,19 +2549,20 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       b  c  d  class\n",
-       "class                \n",
-       "AA     1  2  2      1\n",
-       "BB     1  1  1      1"
+       "       R  4  Echo  class  new_class\n",
+       "class                              \n",
+       "AA     2  2     2      1          1\n",
+       "BB     1  1     1      1          1"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2477,7 +2581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -2486,7 +2590,7 @@
        "Index(['AA', 'BB'], dtype='object', name='class')"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2497,7 +2601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
@@ -2522,10 +2626,10 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>class</th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>c</th>\n",
-       "      <th>d</th>\n",
+       "      <th>R</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Echo</th>\n",
+       "      <th>new_class</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2550,12 +2654,12 @@
        "</div>"
       ],
       "text/plain": [
-       "  class  a  b  c  d\n",
-       "0    AA  2  2  2  2\n",
-       "1    BB  1  1  1  1"
+       "  class  R  4  Echo  new_class\n",
+       "0    AA  2  2     2          2\n",
+       "1    BB  1  1     1          1"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2566,7 +2670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
@@ -2575,7 +2679,7 @@
        "RangeIndex(start=0, stop=2, step=1)"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2593,7 +2697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2603,7 +2707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -2688,26 +2792,247 @@
        "4           5.0          3.6           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3 style=\"color:blue\">read in the iris csv:</h3>\n",
-    "<p style=\"color:blue\">- find the descriptive statistics</p>\n",
-    "<p style=\"color:blue\">- create a correlation matrix </p>"
+    "df.head(5)\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal.length    float64\n",
+       "sepal.width     float64\n",
+       "petal.length    float64\n",
+       "petal.width     float64\n",
+       "variety          object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">read in the iris csv:</h3>\n",
+    "<p style=\"color:blue\">- find the descriptive statistics</p>\n",
+    "<p style=\"color:blue\">- create a correlation matrix </p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>count</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>mean</td>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>3.057333</td>\n",
+       "      <td>3.758000</td>\n",
+       "      <td>1.199333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>std</td>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>0.435866</td>\n",
+       "      <td>1.765298</td>\n",
+       "      <td>0.762238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>min</td>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25%</td>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>50%</td>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.350000</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>75%</td>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>max</td>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       sepal.length  sepal.width  petal.length  petal.width\n",
+       "count    150.000000   150.000000    150.000000   150.000000\n",
+       "mean       5.843333     3.057333      3.758000     1.199333\n",
+       "std        0.828066     0.435866      1.765298     0.762238\n",
+       "min        4.300000     2.000000      1.000000     0.100000\n",
+       "25%        5.100000     2.800000      1.600000     0.300000\n",
+       "50%        5.800000     3.000000      4.350000     1.300000\n",
+       "75%        6.400000     3.300000      5.100000     1.800000\n",
+       "max        7.900000     4.400000      6.900000     2.500000"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>sepal.length</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.117570</td>\n",
+       "      <td>0.871754</td>\n",
+       "      <td>0.817941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>sepal.width</td>\n",
+       "      <td>-0.117570</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.428440</td>\n",
+       "      <td>-0.366126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>petal.length</td>\n",
+       "      <td>0.871754</td>\n",
+       "      <td>-0.428440</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.962865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>petal.width</td>\n",
+       "      <td>0.817941</td>\n",
+       "      <td>-0.366126</td>\n",
+       "      <td>0.962865</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              sepal.length  sepal.width  petal.length  petal.width\n",
+       "sepal.length      1.000000    -0.117570      0.871754     0.817941\n",
+       "sepal.width      -0.117570     1.000000     -0.428440    -0.366126\n",
+       "petal.length      0.871754    -0.428440      1.000000     0.962865\n",
+       "petal.width       0.817941    -0.366126      0.962865     1.000000"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### subset on rules"
@@ -2715,49 +3040,200 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "      <th>variety</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>145</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>146</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>147</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>148</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>149</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "(118, 5)"
+       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
+       "145           6.7          3.0           5.2          2.3  Virginica\n",
+       "146           6.3          2.5           5.0          1.9  Virginica\n",
+       "147           6.5          3.0           5.2          2.0  Virginica\n",
+       "148           6.2          3.4           5.4          2.3  Virginica\n",
+       "149           5.9          3.0           5.1          1.8  Virginica"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df1 = df[df[\"sepal.length\"] > 5]\n",
-    "df1.shape"
+    "df1 = df[df[\"sepal.length\"] > 5]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "      <th>variety</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>140</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>141</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>143</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>144</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>148</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "(23, 5)"
+       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
+       "140           6.7          3.1           5.6          2.4  Virginica\n",
+       "141           6.9          3.1           5.1          2.3  Virginica\n",
+       "143           6.8          3.2           5.9          2.3  Virginica\n",
+       "144           6.7          3.3           5.7          2.5  Virginica\n",
+       "148           6.2          3.4           5.4          2.3  Virginica"
       ]
      },
-     "execution_count": 218,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "df1 = df[(df[\"sepal.length\"] > 6) & (df[\"sepal.width\"] > 3)]\n",
-    "df1.shape"
+    "df1.tail()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -2766,7 +3242,7 @@
        "(50, 5)"
       ]
      },
-     "execution_count": 219,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2785,7 +3261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -2794,7 +3270,7 @@
        "array(['Setosa', 'Versicolor', 'Virginica'], dtype=object)"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2805,7 +3281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -2814,7 +3290,7 @@
        "(100, 5)"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2835,7 +3311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -2844,7 +3320,7 @@
        "(50, 5)"
       ]
      },
-     "execution_count": 227,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2863,7 +3339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2873,7 +3349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
@@ -2922,7 +3398,7 @@
        "0           5.1          3.5           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2933,7 +3409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2942,7 +3418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
@@ -2976,7 +3452,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>-5.1</td>\n",
+       "      <td>5.1</td>\n",
        "      <td>3.5</td>\n",
        "      <td>1.4</td>\n",
        "      <td>0.2</td>\n",
@@ -2984,7 +3460,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>-4.9</td>\n",
+       "      <td>4.9</td>\n",
        "      <td>3.0</td>\n",
        "      <td>1.4</td>\n",
        "      <td>0.2</td>\n",
@@ -2992,7 +3468,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>-4.7</td>\n",
+       "      <td>4.7</td>\n",
        "      <td>3.2</td>\n",
        "      <td>1.3</td>\n",
        "      <td>0.2</td>\n",
@@ -3000,7 +3476,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>-4.6</td>\n",
+       "      <td>4.6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>1.5</td>\n",
        "      <td>0.2</td>\n",
@@ -3008,7 +3484,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
-       "      <td>-5.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>3.6</td>\n",
        "      <td>1.4</td>\n",
        "      <td>0.2</td>\n",
@@ -3020,14 +3496,14 @@
       ],
       "text/plain": [
        "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0          -5.1          3.5           1.4          0.2  Setosa\n",
-       "1          -4.9          3.0           1.4          0.2  Setosa\n",
-       "2          -4.7          3.2           1.3          0.2  Setosa\n",
-       "3          -4.6          3.1           1.5          0.2  Setosa\n",
-       "4          -5.0          3.6           1.4          0.2  Setosa"
+       "0           5.1          3.5           1.4          0.2  Setosa\n",
+       "1           4.9          3.0           1.4          0.2  Setosa\n",
+       "2           4.7          3.2           1.3          0.2  Setosa\n",
+       "3           4.6          3.1           1.5          0.2  Setosa\n",
+       "4           5.0          3.6           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3038,7 +3514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -3049,7 +3525,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3060,7 +3536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3069,7 +3545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3079,7 +3555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
@@ -3113,7 +3589,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>5.1</td>\n",
+       "      <td>-5.1</td>\n",
        "      <td>-3.5</td>\n",
        "      <td>-1.4</td>\n",
        "      <td>-0.2</td>\n",
@@ -3121,7 +3597,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>4.9</td>\n",
+       "      <td>-4.9</td>\n",
        "      <td>-3.0</td>\n",
        "      <td>-1.4</td>\n",
        "      <td>-0.2</td>\n",
@@ -3129,7 +3605,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>4.7</td>\n",
+       "      <td>-4.7</td>\n",
        "      <td>-3.2</td>\n",
        "      <td>-1.3</td>\n",
        "      <td>-0.2</td>\n",
@@ -3137,7 +3613,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>4.6</td>\n",
+       "      <td>-4.6</td>\n",
        "      <td>-3.1</td>\n",
        "      <td>-1.5</td>\n",
        "      <td>-0.2</td>\n",
@@ -3145,7 +3621,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>-5.0</td>\n",
        "      <td>-3.6</td>\n",
        "      <td>-1.4</td>\n",
        "      <td>-0.2</td>\n",
@@ -3157,14 +3633,14 @@
       ],
       "text/plain": [
        "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1         -3.5          -1.4         -0.2  Setosa\n",
-       "1           4.9         -3.0          -1.4         -0.2  Setosa\n",
-       "2           4.7         -3.2          -1.3         -0.2  Setosa\n",
-       "3           4.6         -3.1          -1.5         -0.2  Setosa\n",
-       "4           5.0         -3.6          -1.4         -0.2  Setosa"
+       "0          -5.1         -3.5          -1.4         -0.2  Setosa\n",
+       "1          -4.9         -3.0          -1.4         -0.2  Setosa\n",
+       "2          -4.7         -3.2          -1.3         -0.2  Setosa\n",
+       "3          -4.6         -3.1          -1.5         -0.2  Setosa\n",
+       "4          -5.0         -3.6          -1.4         -0.2  Setosa"
       ]
      },
-     "execution_count": 236,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3175,7 +3651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3184,7 +3660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -3218,7 +3694,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>-5.1</td>\n",
+       "      <td>5.1</td>\n",
        "      <td>-3.5</td>\n",
        "      <td>-1.4</td>\n",
        "      <td>-0.2</td>\n",
@@ -3226,7 +3702,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>-4.9</td>\n",
+       "      <td>4.9</td>\n",
        "      <td>-3.0</td>\n",
        "      <td>-1.4</td>\n",
        "      <td>-0.2</td>\n",
@@ -3234,7 +3710,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>-4.7</td>\n",
+       "      <td>4.7</td>\n",
        "      <td>-3.2</td>\n",
        "      <td>-1.3</td>\n",
        "      <td>-0.2</td>\n",
@@ -3242,7 +3718,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>-4.6</td>\n",
+       "      <td>4.6</td>\n",
        "      <td>-3.1</td>\n",
        "      <td>-1.5</td>\n",
        "      <td>-0.2</td>\n",
@@ -3250,7 +3726,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
-       "      <td>-5.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>-3.6</td>\n",
        "      <td>-1.4</td>\n",
        "      <td>-0.2</td>\n",
@@ -3262,14 +3738,14 @@
       ],
       "text/plain": [
        "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0          -5.1         -3.5          -1.4         -0.2  Setosa\n",
-       "1          -4.9         -3.0          -1.4         -0.2  Setosa\n",
-       "2          -4.7         -3.2          -1.3         -0.2  Setosa\n",
-       "3          -4.6         -3.1          -1.5         -0.2  Setosa\n",
-       "4          -5.0         -3.6          -1.4         -0.2  Setosa"
+       "0           5.1         -3.5          -1.4         -0.2  Setosa\n",
+       "1           4.9         -3.0          -1.4         -0.2  Setosa\n",
+       "2           4.7         -3.2          -1.3         -0.2  Setosa\n",
+       "3           4.6         -3.1          -1.5         -0.2  Setosa\n",
+       "4           5.0         -3.6          -1.4         -0.2  Setosa"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3287,7 +3763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -3301,7 +3777,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 239,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3312,22 +3788,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(str)"
+    "df[\"sepal.length\"] = df[\"sepal.length\"].apply(float)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "sepal.length     object\n",
+       "sepal.length    float64\n",
        "sepal.width     float64\n",
        "petal.length    float64\n",
        "petal.width     float64\n",
@@ -3335,7 +3811,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 241,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3363,7 +3839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3372,7 +3848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
@@ -3396,24 +3872,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
        "      <th>sepal.width</th>\n",
        "      <th>petal.length</th>\n",
        "      <th>petal.width</th>\n",
-       "      <th>sepal.length_-4.3</th>\n",
-       "      <th>sepal.length_-4.4</th>\n",
-       "      <th>sepal.length_-4.5</th>\n",
-       "      <th>sepal.length_-4.6</th>\n",
-       "      <th>sepal.length_-4.7</th>\n",
-       "      <th>sepal.length_-4.8</th>\n",
-       "      <th>sepal.length_-4.9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sepal.length_-7.1</th>\n",
-       "      <th>sepal.length_-7.2</th>\n",
-       "      <th>sepal.length_-7.3</th>\n",
-       "      <th>sepal.length_-7.4</th>\n",
-       "      <th>sepal.length_-7.6</th>\n",
-       "      <th>sepal.length_-7.7</th>\n",
-       "      <th>sepal.length_-7.9</th>\n",
        "      <th>variety_Setosa</th>\n",
        "      <th>variety_Versicolor</th>\n",
        "      <th>variety_Virginica</th>\n",
@@ -3421,188 +3883,87 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>-3.5</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <td>145</td>\n",
+       "      <td>6.7</td>\n",
        "      <td>-3.0</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>-1.3</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>-5.2</td>\n",
+       "      <td>-2.3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>-3.1</td>\n",
-       "      <td>-1.5</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>-3.6</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
+       "    <tr>\n",
+       "      <td>146</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>-2.5</td>\n",
+       "      <td>-5.0</td>\n",
+       "      <td>-1.9</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>147</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>-5.2</td>\n",
+       "      <td>-2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>148</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>-3.4</td>\n",
+       "      <td>-5.4</td>\n",
+       "      <td>-2.3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>149</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>-5.1</td>\n",
+       "      <td>-1.8</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 41 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   sepal.width  petal.length  petal.width  sepal.length_-4.3  \\\n",
-       "0         -3.5          -1.4         -0.2                  0   \n",
-       "1         -3.0          -1.4         -0.2                  0   \n",
-       "2         -3.2          -1.3         -0.2                  0   \n",
-       "3         -3.1          -1.5         -0.2                  0   \n",
-       "4         -3.6          -1.4         -0.2                  0   \n",
-       "\n",
-       "   sepal.length_-4.4  sepal.length_-4.5  sepal.length_-4.6  sepal.length_-4.7  \\\n",
-       "0                  0                  0                  0                  0   \n",
-       "1                  0                  0                  0                  0   \n",
-       "2                  0                  0                  0                  1   \n",
-       "3                  0                  0                  1                  0   \n",
-       "4                  0                  0                  0                  0   \n",
-       "\n",
-       "   sepal.length_-4.8  sepal.length_-4.9  ...  sepal.length_-7.1  \\\n",
-       "0                  0                  0  ...                  0   \n",
-       "1                  0                  1  ...                  0   \n",
-       "2                  0                  0  ...                  0   \n",
-       "3                  0                  0  ...                  0   \n",
-       "4                  0                  0  ...                  0   \n",
+       "     sepal.length  sepal.width  petal.length  petal.width  variety_Setosa  \\\n",
+       "145           6.7         -3.0          -5.2         -2.3               0   \n",
+       "146           6.3         -2.5          -5.0         -1.9               0   \n",
+       "147           6.5         -3.0          -5.2         -2.0               0   \n",
+       "148           6.2         -3.4          -5.4         -2.3               0   \n",
+       "149           5.9         -3.0          -5.1         -1.8               0   \n",
        "\n",
-       "   sepal.length_-7.2  sepal.length_-7.3  sepal.length_-7.4  sepal.length_-7.6  \\\n",
-       "0                  0                  0                  0                  0   \n",
-       "1                  0                  0                  0                  0   \n",
-       "2                  0                  0                  0                  0   \n",
-       "3                  0                  0                  0                  0   \n",
-       "4                  0                  0                  0                  0   \n",
-       "\n",
-       "   sepal.length_-7.7  sepal.length_-7.9  variety_Setosa  variety_Versicolor  \\\n",
-       "0                  0                  0               1                   0   \n",
-       "1                  0                  0               1                   0   \n",
-       "2                  0                  0               1                   0   \n",
-       "3                  0                  0               1                   0   \n",
-       "4                  0                  0               1                   0   \n",
-       "\n",
-       "   variety_Virginica  \n",
-       "0                  0  \n",
-       "1                  0  \n",
-       "2                  0  \n",
-       "3                  0  \n",
-       "4                  0  \n",
-       "\n",
-       "[5 rows x 41 columns]"
+       "     variety_Versicolor  variety_Virginica  \n",
+       "145                   0                  1  \n",
+       "146                   0                  1  \n",
+       "147                   0                  1  \n",
+       "148                   0                  1  \n",
+       "149                   0                  1  "
       ]
      },
-     "execution_count": 243,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head()"
+    "df.tail()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
@@ -3657,7 +4018,7 @@
        "2     C    CC"
       ]
      },
-     "execution_count": 244,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3681,6 +4042,80 @@
     "<h3 style=\"color:blue\">perform get dummies on col_1 only?</h3>"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col_2</th>\n",
+       "      <th>col_1_A</th>\n",
+       "      <th>col_1_B</th>\n",
+       "      <th>col_1_C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>AA</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>BB</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>CC</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col_2  col_1_A  col_1_B  col_1_C\n",
+       "0    AA        1        0        0\n",
+       "1    BB        0        1        0\n",
+       "2    CC        0        0        1"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(df,columns=['col_1'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3690,7 +4125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 162,
    "metadata": {
     "scrolled": true
    },
@@ -3718,7 +4153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 249,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [
     {
@@ -3738,7 +4173,7 @@
     }
    ],
    "source": [
-    "for idx, r in df.head(5).iterrows():\n",
+    "for idx, r in df.tail(5).iterrows():\n",
     "    print(r)"
    ]
   },
@@ -3751,18 +4186,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 252,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['A', 'B', 'C']"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['A', 'B', 'C']\n",
+      "[0    A\n",
+      "1    B\n",
+      "2    C\n",
+      "Name: col_1, dtype: object]\n"
+     ]
     }
    ],
    "source": [
@@ -3779,7 +4215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 253,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
@@ -3846,7 +4282,7 @@
        "4     E          1"
       ]
      },
-     "execution_count": 253,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3869,7 +4305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -3924,7 +4360,7 @@
        "2     C          1"
       ]
      },
-     "execution_count": 254,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3943,7 +4379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [
     {
@@ -4051,7 +4487,7 @@
        "2     C        NaN        1.0"
       ]
      },
-     "execution_count": 255,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4080,7 +4516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -4139,7 +4575,7 @@
        "2     C          1          1"
       ]
      },
-     "execution_count": 257,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4159,7 +4595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 258,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -4232,7 +4668,7 @@
        "4     E          1        NaN"
       ]
      },
-     "execution_count": 258,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4256,7 +4692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4267,7 +4703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
@@ -4343,7 +4779,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 260,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4354,7 +4790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [
     {
@@ -4430,7 +4866,7 @@
        "7  False"
       ]
      },
-     "execution_count": 261,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4441,7 +4877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [
     {
@@ -4517,7 +4953,7 @@
        "7   True"
       ]
      },
-     "execution_count": 262,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4528,7 +4964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
+   "execution_count": 177,
    "metadata": {},
    "outputs": [
     {
@@ -4604,7 +5040,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 263,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4615,7 +5051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -4691,7 +5127,7 @@
        "7    1.0"
       ]
      },
-     "execution_count": 264,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4702,7 +5138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 266,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -4773,7 +5209,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 266,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4784,7 +5220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 267,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -4845,7 +5281,7 @@
        "Index: [0, 1, 2, 3, 4, 5, 6, 7]"
       ]
      },
-     "execution_count": 267,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4856,7 +5292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -4932,7 +5368,7 @@
        "7  1.000000"
       ]
      },
-     "execution_count": 268,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4943,8 +5379,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {},
+   "execution_count": 95,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -5019,7 +5457,7 @@
        "7  1.0"
       ]
      },
-     "execution_count": 269,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5030,7 +5468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -5115,7 +5553,7 @@
        "7      7  1.000000"
       ]
      },
-     "execution_count": 270,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5133,15 +5571,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 301,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\raves\\Documents\\GitHub\\python_for_analytics\\lectures\n",
-      "['../data\\\\ca.json']\n"
+     "ename": "NameError",
+     "evalue": "name 'os' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-97-39d28f2a4ce3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'os' is not defined"
      ]
     }
    ],
@@ -5157,120 +5598,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "with open(path, \"r\") as file:\n",
-    "    line = file.readlines()\n",
-    "    for l in line[0:50]:\n",
-    "        print(l)"
-   ]
-  },
-  {
-   "cell_type": "raw",
+   "execution_count": 179,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "{\n",
-    "    \"brian\" {\n",
-    "        \"age\":31,\n",
-    "        \"job\":data scientist,\n",
-    "        \"city\"chicago\n",
-    "        },\n",
-    "        \n",
-    "   \"james\":{\n",
-    "        \"age\":28,\n",
-    "        \"job\":data scientist,\n",
-    "        \"city\"new york\n",
-    "        }\n",
-    "        \n",
-    "     \"james\":{\n",
-    "        \"age\":28,\n",
-    "        }\n",
-    "    \n",
-    "    \n",
-    "}"
+    "\n",
+    "columns = [\"age\", \"job\", \"city\"]\n",
+    "data = [\n",
+    "    [31, \"data scientist\", \"chicago\"],\n",
+    "    [28, \"data scientist\", \"new york\"],\n",
+    "    [28,None,None]\n",
+    "]\n",
+    "\n",
+    "path = '../data/class.json'\n",
+    "\n",
+    "df = pd.DataFrame(data, columns = columns)\n",
+    "df.to_json(path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 297,
+   "execution_count": 180,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>job</th>\n",
-       "      <th>city</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>31</td>\n",
-       "      <td>data scientist</td>\n",
-       "      <td>chicago</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>28</td>\n",
-       "      <td>data scientist</td>\n",
-       "      <td>new york</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>28</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age             job      city\n",
-       "0   31  data scientist   chicago\n",
-       "1   28  data scientist  new york\n",
-       "2   28            None      None"
-      ]
-     },
-     "execution_count": 297,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"age\":{\"0\":31,\"1\":28,\"2\":28},\"job\":{\"0\":\"data scientist\",\"1\":\"data scientist\",\"2\":null},\"city\":{\"0\":\"chicago\",\"1\":\"new york\",\"2\":null}}\n"
+     ]
     }
    ],
    "source": [
-    "\n",
-    "columns = [\"age\", \"job\", \"city\"]\n",
-    "data = [\n",
-    "    [31, \"data scientist\", \"chicago\"],\n",
-    "    [28, \"data scientist\", \"new york\"],\n",
-    "    [28,None,None]\n",
-    "]\n",
-    "\n",
-    "df = pd.DataFrame(data, columns = columns)\n",
-    "df"
+    "with open(path, \"r\") as file:\n",
+    "    line = file.readlines()\n",
+    "    for l in line[0:50]:\n",
+    "        print(l)"
    ]
   },
   {
@@ -5282,7 +5645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 303,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -5292,7 +5655,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-303-fa996717fc03>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../data/ca.json'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-102-fa996717fc03>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../data/ca.json'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    593\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mshould_close\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    715\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 717\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    718\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[1;34m(self, json)\u001b[0m\n\u001b[0;32m    737\u001b[0m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"frame\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 739\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    741\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"series\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
@@ -5308,7 +5671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 304,
+   "execution_count": 181,
    "metadata": {},
    "outputs": [
     {
@@ -5367,7 +5730,7 @@
        "2   28            None      None"
       ]
      },
-     "execution_count": 304,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5395,7 +5758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 305,
+   "execution_count": 182,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5413,7 +5776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 306,
+   "execution_count": 183,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5422,7 +5785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 308,
+   "execution_count": 184,
    "metadata": {},
    "outputs": [
     {
@@ -5485,7 +5848,7 @@
        "2  2.0  Faye Raker             130              60"
       ]
      },
-     "execution_count": 308,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5496,7 +5859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 309,
+   "execution_count": 187,
    "metadata": {},
    "outputs": [
     {
@@ -5555,7 +5918,7 @@
        "2  2.0  Faye Raker  {'height': 130, 'weight': 60}"
       ]
      },
-     "execution_count": 309,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5566,7 +5929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 311,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5586,7 +5949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 312,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [
     {
@@ -5612,8 +5975,9 @@
        "      <th></th>\n",
        "      <th>state</th>\n",
        "      <th>shortname</th>\n",
-       "      <th>info</th>\n",
        "      <th>counties</th>\n",
+       "      <th>info.governor</th>\n",
+       "      <th>info.gender</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -5621,37 +5985,39 @@
        "      <td>0</td>\n",
        "      <td>Florida</td>\n",
        "      <td>FL</td>\n",
-       "      <td>{'governor': 'Rick Scott', 'gender': 'm'}</td>\n",
        "      <td>[{'name': 'Dade', 'population': 12345}, {'name...</td>\n",
+       "      <td>Rick Scott</td>\n",
+       "      <td>m</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
        "      <td>Ohio</td>\n",
        "      <td>OH</td>\n",
-       "      <td>{'governor': 'John Kasich', 'gender': 'm'}</td>\n",
        "      <td>[{'name': 'Summit', 'population': 1234}, {'nam...</td>\n",
+       "      <td>John Kasich</td>\n",
+       "      <td>m</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     state shortname                                        info  \\\n",
-       "0  Florida        FL   {'governor': 'Rick Scott', 'gender': 'm'}   \n",
-       "1     Ohio        OH  {'governor': 'John Kasich', 'gender': 'm'}   \n",
+       "     state shortname                                           counties  \\\n",
+       "0  Florida        FL  [{'name': 'Dade', 'population': 12345}, {'name...   \n",
+       "1     Ohio        OH  [{'name': 'Summit', 'population': 1234}, {'nam...   \n",
        "\n",
-       "                                            counties  \n",
-       "0  [{'name': 'Dade', 'population': 12345}, {'name...  \n",
-       "1  [{'name': 'Summit', 'population': 1234}, {'nam...  "
+       "  info.governor info.gender  \n",
+       "0    Rick Scott           m  \n",
+       "1   John Kasich           m  "
       ]
      },
-     "execution_count": 312,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "json_normalize(data, max_level = 0)"
+    "json_normalize(data)"
    ]
   },
   {
@@ -5802,7 +6168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 314,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [
     {
@@ -5881,7 +6247,7 @@
        "4    Cuyahoga        1337   John Kasich           m"
       ]
      },
-     "execution_count": 314,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5901,7 +6267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 317,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5910,16 +6276,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 321,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [],
    "source": [
-    "chunk_size = 20"
+    "chunk_size = 15"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 322,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5928,53 +6294,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-211-cea6666cdc75>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-211-cea6666cdc75>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    for k in blocks\u001b[0m\n\u001b[1;37m                   ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k in blocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(20, 5)\n",
-      "(20, 5)\n",
-      "(20, 5)\n",
-      "(20, 5)\n",
-      "(20, 5)\n",
-      "(20, 5)\n",
-      "(20, 5)\n",
-      "(10, 5)\n"
+      "    5.8    4  1.2   .2  Setosa\n",
+      "0   5.7  4.4  1.5  0.4  Setosa\n",
+      "1   5.4  3.9  1.3  0.4  Setosa\n",
+      "2   5.1  3.5  1.4  0.3  Setosa\n",
+      "3   5.7  3.8  1.7  0.3  Setosa\n",
+      "4   5.1  3.8  1.5  0.3  Setosa\n",
+      "5   5.4  3.4  1.7  0.2  Setosa\n",
+      "6   5.1  3.7  1.5  0.4  Setosa\n",
+      "7   4.6  3.6  1.0  0.2  Setosa\n",
+      "8   5.1  3.3  1.7  0.5  Setosa\n",
+      "9   4.8  3.4  1.9  0.2  Setosa\n",
+      "10  5.0  3.0  1.6  0.2  Setosa\n",
+      "11  5.0  3.4  1.6  0.4  Setosa\n",
+      "12  5.2  3.5  1.5  0.2  Setosa\n",
+      "13  5.2  3.4  1.4  0.2  Setosa\n",
+      "14  4.7  3.2  1.6  0.2  Setosa\n",
+      "    4.7  3.2  1.6   .2  Setosa\n",
+      "0   4.8  3.1  1.6  0.2  Setosa\n",
+      "1   5.4  3.4  1.5  0.4  Setosa\n",
+      "2   5.2  4.1  1.5  0.1  Setosa\n",
+      "3   5.5  4.2  1.4  0.2  Setosa\n",
+      "4   4.9  3.1  1.5  0.2  Setosa\n",
+      "5   5.0  3.2  1.2  0.2  Setosa\n",
+      "6   5.5  3.5  1.3  0.2  Setosa\n",
+      "7   4.9  3.6  1.4  0.1  Setosa\n",
+      "8   4.4  3.0  1.3  0.2  Setosa\n",
+      "9   5.1  3.4  1.5  0.2  Setosa\n",
+      "10  5.0  3.5  1.3  0.3  Setosa\n",
+      "11  4.5  2.3  1.3  0.3  Setosa\n",
+      "12  4.4  3.2  1.3  0.2  Setosa\n",
+      "13  5.0  3.5  1.6  0.6  Setosa\n",
+      "14  5.1  3.8  1.9  0.4  Setosa\n",
+      "    5.1  3.8  1.9   .4      Setosa\n",
+      "0   4.8  3.0  1.4  0.3      Setosa\n",
+      "1   5.1  3.8  1.6  0.2      Setosa\n",
+      "2   4.6  3.2  1.4  0.2      Setosa\n",
+      "3   5.3  3.7  1.5  0.2      Setosa\n",
+      "4   5.0  3.3  1.4  0.2      Setosa\n",
+      "5   7.0  3.2  4.7  1.4  Versicolor\n",
+      "6   6.4  3.2  4.5  1.5  Versicolor\n",
+      "7   6.9  3.1  4.9  1.5  Versicolor\n",
+      "8   5.5  2.3  4.0  1.3  Versicolor\n",
+      "9   6.5  2.8  4.6  1.5  Versicolor\n",
+      "10  5.7  2.8  4.5  1.3  Versicolor\n",
+      "11  6.3  3.3  4.7  1.6  Versicolor\n",
+      "12  4.9  2.4  3.3  1.0  Versicolor\n",
+      "13  6.6  2.9  4.6  1.3  Versicolor\n",
+      "14  5.2  2.7  3.9  1.4  Versicolor\n",
+      "    5.2  2.7  3.9  1.4  Versicolor\n",
+      "0   5.0  2.0  3.5  1.0  Versicolor\n",
+      "1   5.9  3.0  4.2  1.5  Versicolor\n",
+      "2   6.0  2.2  4.0  1.0  Versicolor\n",
+      "3   6.1  2.9  4.7  1.4  Versicolor\n",
+      "4   5.6  2.9  3.6  1.3  Versicolor\n",
+      "5   6.7  3.1  4.4  1.4  Versicolor\n",
+      "6   5.6  3.0  4.5  1.5  Versicolor\n",
+      "7   5.8  2.7  4.1  1.0  Versicolor\n",
+      "8   6.2  2.2  4.5  1.5  Versicolor\n",
+      "9   5.6  2.5  3.9  1.1  Versicolor\n",
+      "10  5.9  3.2  4.8  1.8  Versicolor\n",
+      "11  6.1  2.8  4.0  1.3  Versicolor\n",
+      "12  6.3  2.5  4.9  1.5  Versicolor\n",
+      "13  6.1  2.8  4.7  1.2  Versicolor\n",
+      "14  6.4  2.9  4.3  1.3  Versicolor\n"
      ]
     }
    ],
    "source": [
-    "for c in pd.read_csv(path, chunksize = chunk_size):\n",
-    "    print(c.shape)"
+    "for k in [1,2,3,4]:\n",
+    "    a = pd.read_csv(path, skiprows=0+k*chunk_size, nrows = chunk_size)\n",
+    "    print(a)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 324,
+   "execution_count": 210,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(1, 4)\n",
-      "(1, 4)\n",
-      "(2, 4)\n",
-      "(1, 4)\n",
-      "(1, 4)\n",
-      "(1, 4)\n",
-      "(1, 4)\n",
-      "(1, 4)\n"
+      "      5  3.3  1.4   .2      Setosa\n",
+      "0   7.0  3.2  4.7  1.4  Versicolor\n",
+      "1   6.4  3.2  4.5  1.5  Versicolor\n",
+      "2   6.9  3.1  4.9  1.5  Versicolor\n",
+      "3   5.5  2.3  4.0  1.3  Versicolor\n",
+      "4   6.5  2.8  4.6  1.5  Versicolor\n",
+      "5   5.7  2.8  4.5  1.3  Versicolor\n",
+      "6   6.3  3.3  4.7  1.6  Versicolor\n",
+      "7   4.9  2.4  3.3  1.0  Versicolor\n",
+      "8   6.6  2.9  4.6  1.3  Versicolor\n",
+      "9   5.2  2.7  3.9  1.4  Versicolor\n",
+      "10  5.0  2.0  3.5  1.0  Versicolor\n",
+      "11  5.9  3.0  4.2  1.5  Versicolor\n",
+      "12  6.0  2.2  4.0  1.0  Versicolor\n",
+      "13  6.1  2.9  4.7  1.4  Versicolor\n",
+      "14  5.6  2.9  3.6  1.3  Versicolor\n"
      ]
     }
    ],
    "source": [
-    "for c in pd.read_csv(path, chunksize = chunk_size):\n",
-    "    df = c.groupby(\"variety\").sum()\n",
-    "    print(df.shape)"
+    "print(a)"
    ]
   },
   {
diff --git a/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb b/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb
index 65d263e..f9f01e0 100644
--- a/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb	
+++ b/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb	
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -82,16 +82,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9bca048>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223928c148>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -121,16 +121,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x14ef9cb06c8>"
+       "<matplotlib.collections.PathCollection at 0x2223a38f3c8>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -154,16 +154,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9cf5c88>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x222380d9408>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,24 +202,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([13., 15., 10., 10.,  9.,  8., 11., 11.,  7.,  6.]),\n",
+       "(array([ 9., 12., 14.,  9.,  9.,  7.,  7., 14., 14.,  5.]),\n",
        " array([0. , 0.9, 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, 9. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -234,22 +234,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef89d9148>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a4ca088>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -264,22 +264,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9a80208>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a565888>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -294,22 +294,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9ac2bc8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a565b48>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -324,22 +324,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9ecf2c8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a62a188>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -361,22 +361,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14ef9fcf608>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a6c0188>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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OblGJWKMSMejlvsZCqEUKX1yWoigcqTnGX85uZ5djH37Fj1lvxmpJpGdMKrHmGCIMZkx6IwrQ5G+i0eehzltPnaeO6sZaztadA8CoM5AUbSMlOonu0cnqrpgQGiSFLy7paM0JNh19l2O1J7EYo/hZj58yyNqfirpq9Dpdq8fx+rxUNFzgXL2d8vrzgTeAHY6vsUba6BXbA4sp6mqthhDin6TwxQ84PXWsO7SJryv20NUcy7SBtzM8eRhmgxmXUseF+po2jWcymEiNSSE1JgVFUXB66iivP8+Fxkp2Ofayy7EXm8VK79ge9IxNxWwwX6U1E0LbpPBFM7sc+3jz4AbcTW4m9hnLLdeMJiKEBazT6egSEUuXiFiG9prMZ4e/4uTF05y4eJov7V9TVrGbHjHd6dP1GpItSW36S0IIcXlS+AIAn9/HpqNb+Pj0X+kZm0rBdT+ne8zVP84ea44hvdt1DEpMo8pdzfGLpzh58QynnGeIMkTSq0tPru3ai64RXa56FiE6u1YVfklJCStWrKCpqYmZM2cyY8aMFp+3aNEisrOzmTJlCgDl5eUsXLiQyspK+vTpw7PPPkt0dHTo0ouQcHldrNq3hoPVh7mpxyhu7zex3c+m0el0JEYlkBiVwA3WDMrrz3O89iTfVB/hYPVhEiLj6NOlF7269CDCENGu2YToLIKeKG232ykqKmLt2rVs2rSJdevWceTIkR88Z/bs2bz//vvNHn/iiSeYPn06paWlpKen89JLL4U2vbhitY0X+cPOFRyuOcaMtDuZOmCy6qdOGvQGesamktNjJLf1Hc8N1kz8isKOit1sOrKFv579G2fqyvErflVzCtHRBC38bdu2kZ2dTVxcHBaLhdzcXEpLS5s9p6SkhFtuuYXx48cHHvN6vXz11Vfk5uYCMGXKlB8sJ9RV2VDNsp0rqHJX89CQ+xjZ/Ua1I/1ApDGStIR+jO99C7f2upn+8X1xNFTy17N/Y9PR9/jq/NccqToh5S9EKwQ9pFNRUYHVag1M22w29uzZ0+w5s2bNAmDHjh2Bx6qrq4mJicFo/PZXWK1W7HZ7SEKLK1flrqZo5wrcvkbmDSmkT9deakcKKj4yjvjIOIZY0zlXb+f4xVMcv3iKI7uPYzaY6RnTnR6x3UmKsqr+V4oQ4Sho4fv9fnTfO1NCUZRm05fS0vNas9z3JSbGXHa+1RrbpvHaSzjm+n6mWvdFXvpqFY3+RpbePJ8+8T1bPY6j3kNsbGTIcv3Ysbp26UNaSh+afE00+X18cbqMUzVnOFp7AoPeQPcYGz27dqdH1xS6RsS2adsL1fpZos1Yo0O3LYT7dhVOwjFXOGQKWvjJycmUlZUFph0OBzabLejACQkJOJ1OfD4fBoOh1ct9X2VlHX5/y1/dt1pjcTicbRqvPYRjru9namhq4I87X+aCq5qHhxQS0xTXprwuxYPT6Q5NsARCMlZG9wF43QrDEodgdzk4V2/nXL2d0xd3wGmwGKOwRnWjW1QC3aISiYvocunr/IQoE4Ar1oPDdfnX1q/4cXrqqHLXUN1YQ01jLS6vC1dTA/VeFy5vAx6/B5PJSJPXF1jObDATbbIQY4omITKeblEJJFlsdItKaLdrGIXjtg7hmau9Mun1usvuKAct/JEjR/LCCy9QVVVFVFQUW7du5Te/+U3QX2wymcjKymLLli3k5eWxadMmcnJy2pZehJTP72PVvjWcrT/P7Mxf0jeut9qRQurbPfvkwOmkTk8d5+vt2F0OKlwOTjpPA2DUGYmP7ErXiC50NXcJ/Iw0hv7sn4YmN9X/LPNvf9ZS7a6hyl0dmPYpvmbL6NARZYzEYrJgMUYRYTDz3d8nCgqKAhc9Ts7X27noqcPr9waWjTCY6RGTSv+4PvSLv5Z+XftgMphCvl6iYwpa+ElJScyfP5+CggK8Xi9Tp04lMzOTwsJC5s2bR0ZGxiWXXbJkCYsXL2bFihWkpKSwbNmykIYXbfP24RIOVB1iRtqdDEocqHacqy7WHEOsOYb+8X1RFIX6JhcXGqq40FBJtbuGkxfPNCtLo87Ih6c/Q4+eKGMUUcZITHojRr0Rg87wz596FEXBp/hR8ONXFHyKj0afB4/PE/jZ0OSm+Oi7uJsam2XS6/R0NXchPjKO3l2v4YaIOBL++dlEfEQccRFdW7zS6KX2EBVFoc5bz4WGSs7V2zlTd44TF0+x9dSnlJ78GLPBzHUJAxhiTWewNT2kX6ITHY9OUZSWj5mEATmkExpWayxvf/0+6w9t4pZrcpjSb9KPHsul1LGzfF9Icg3tdR07Tx644nEyug9gb/mhNi+nKAoNTW5qPRepbXTianIRYTZRXmunoclNQ1MD/ktcDbQlZr0Zs8FEhMFMpDGS3l17YI20Eh/5z1KPiKOLOfZHfaDc1u3K3dTI0drj7L1wgL0X9lPTWEuEwcwNtkxyUkfQq0vrP7cJVab2Eo65OswhHdHxHXQc4e3D75CeeB239Z2gdpywodPpsJiisJiiSIlOApq/CSmKgl/x06T4aPI34VN8+Pw+9Do9Op0OPXr0Oh0GvQGT3vyDy0AM7Z6ORXf5Ew+ulkhjBIMS0xiUmMa/DsjnaM1x/n5+JzsqdvO3c2X06XINt1wzmsHWQXLfAg2Rwu/kahudFO14lcTIeO4ZNE3+524DnU6HQWfAgKFDHwrR6/T0j+9L//i+3NF/En87t4NPz3zBq/teJyU6ifG9b+EGW6ZsGxoghd+J+fw+Vv9jDfVeFwuGPUSUUS5BrHVRxihu6jmKnNQRfF2xh/dOfsz/+cdarjn1Gfl9J5CW0F/tiOIqksLvxEpPfMThmmM8+JOZpMakqB1HhBGD3kBW8g0MTRrMV+e/puTY+7ywayU3WDOYOmAycRFd1Y4orgIp/E7qWO0J3jvxET9JHsroPtlh9yGWCA96nZ7hKcMYasvko9N/ofTERxz42yHy+t5KTuoIOczTych/zU6oocnNa/94k4TIeP51wG1qxxEdgMlg4tbet/DYT/6NPl178dahzTxbtpzTzrNqRxMhJIXfCa0/tIkqdw33DJpGlDF0l0EQnZ/VksiDg+/jl9ffRVVjNc+UvUDpiY/k4nSdhBzS6WTK7Lv48vxOJvQew7Vde6sdR3RAOp2OrOQbuD5xIH/+ppiSY+9zoOoQ91x/F/GRcWrHE1dA9vA7kSp3NX/+ZiN9ulzDrb1vUTuO6OAsJgu/HDSdgut+zmnnWZ76soidFXuCLyjClhR+J+FX/Pz3/j/jV/zMvP4uuTywCAmdTsfwlGEsvvFX2KK6sWrfG6w9+DZenzf4wiLsSOF3Ep+e+YIjNce5s38+Vkui2nFEJ2OzdOPfhs1lXK+b+KL8S4p2/okLriq1Y4k2ksLvBCpcDt45Wkp6YhrZKVlqxxGdlEFvIL/veO7PKMDuquA/tj7N4epjascSbSCF38H5FT9vHHgLo97AXWl3tPkmM0K01WBrOguzHiLabOH5Xa/wyenPCeNrMIrvkcLv4D47s42jtSeY2l++HSnaT3J0Ev819t8ZlJjG24ff4Y0Db9Hkb1I7lghCCr8Dq3BdYPPR90hPTGN48jC14wiNsZiiuD+jgAm9x/C382Us37UKl9eldixxGVL4HZQcyhHhQK/TM/Haccy8fhrHak/w7I7lXGioVDuWuAQp/A7q20M5x7lDDuWIMPCT5KE8fMP91Hnq+d9lL3Ks9oTakUQLpPA7oO8O5QxKTCNbDuWIMNEvrg8Lsh4kyhjJH79+Rb6kFYak8DsYv+JnzcFvD+VMl0M5IszYLFYWZD3ENbE9+D/71vCXM9vVjiS+Rwq/g/nLme0cqZFDOSJ8xZiieXhIIend0lh3qJh3j22V0zbDhBR+B+JwVbL56BY5lCPCntlgojC9gBEpN7LlxIf8+VCxXHEzDMjVMjsIv+Ln9QPrMOgN3DVwihzKEWHPoDcwI20qseYYtp78hDpPHfdcfxcmg0ntaJole/gdxCenP+do7Qnu7J8vl6gVHYZOpyO/73ju6J/HLsc+lu9eRUNTg9qxNKtVhV9SUsKECRMYN24ca9as+cH8AwcOMGXKFHJzc3nsscdoavr2G3fFxcWMGjWK/Px88vPzKSoqCm16jThfb+edY6VkdhvET5KHqh1HiDa7uee/cM/1d3G09gTP7XyZ2ka55aYagha+3W6nqKiItWvXsmnTJtatW8eRI0eaPWfhwoX8+te/5v3330dRFNavXw/Avn37WLx4MZs3b2bz5s3Mnz//6qxFJ+bz+/i/+9cTYTBzV5ocyhEd143JNzAn85dUNFxgmXxBSxVBC3/btm1kZ2cTFxeHxWIhNzeX0tLSwPyzZ8/idrsZMmQIAFOmTAnM37t3L8XFxeTl5bFgwQJqa2uv0mp0Xh+c+oyTztP8fMDtdDHHqh1HiCtyfeJAHrnhfhqa3Pxhx0ucrTundiRNCVr4FRUVWK3WwLTNZsNut19yvtVqDcy3Wq3MnTuXd955h5SUFJ588slQZu/0zjjL2XL8A4bZBjMsabDacYQIid5drmH+sDnodXqKdq7gSM1xtSNpRtCzdPx+f7PDCIqiNJu+3Pzly5cHHp81axZjx45tU7jExJjLzrdaw3OPNxS5vD4vz+x8m5iIaOaOvJvYiMu/Fu2RCcBR7yE2NnQ3Rg/FWCaTIewyAViizVijQ7eNhuP2/mMzWa2xPGVdyG8/e57lu1/lf40sZGj3DNVzXU3hkClo4ScnJ1NWVhaYdjgc2Gy2ZvMdDkdg+sKFC9hsNpxOJxs2bOCee+4Bvn0jMBjadtu9yso6/P6Wv7BhtcbicITfBz+hyvX2oXc4WXOG2Zn34L6o4ObHjxnK18qleHA63SEZiwRCMpY31hd2mQBcsR4crtC87uG4vV95JjOPDJ7NS7tX8cznf+IX1/1rSE5K6JyvVevo9brL7igHPaQzcuRItm/fTlVVFQ0NDWzdupWcnJzA/NTUVCIiItixYwcAmzdvJicnB4vFwquvvsru3bsBeOONN9q8h69Vey/s55MznzO6x0/J6Ha92nGEuGpizTHMu+EB+nXtw3/v/zOfnP5c7UidWtA9/KSkJObPn09BQQFer5epU6eSmZlJYWEh8+bNIyMjg2effZbHH3+curo6Bg0aREFBAQaDgeeee46lS5fidrvp3bs3zzzzTHusU4dW01jL6wfWkxqTwu19J6gdR4irLsoYydzB97J6/5u8ffgd6r31TOwzTs5Iuwpa9U3bvLw88vLymj22cuXKwL/T0tJ4++23f7BcVlYWxcXFVxhRO/yKn//+x5/x+rzcN2iGfCNRaIbJYOK+QTP48zcbee/ERzi99fx8wG3odfLd0FCSSyuEka0nP+FQzVHuTruTpGhb8AWE6EQMegPT06YSbYrmg1Of4vK6mHn9NIx6qalQkVcyTByrPcG7/zwFMzslS+04QqhCp9NxW78JxJijKT7yLg1Nbmal/4JIY4Ta0ToF+XspDDg9dazat4b4iDj5Nq0QwJhrRnN32p0crDrMC7tWUuetVztSpyCFrzKf38eqfW9Q761nVsbdRBmj1I4kRFgY0f1GCjN+wZm6cpbteAmHSy7FcKWk8FVWfORdDtccY3raVK6J7aF2HCHCymBrOg8PKaTOU8+zO17kWO1JtSN1aFL4KtpW/iWfnPmcm3qOkqtgCnEJ/eL68G+Be+W+LPfKvQJS+Co5WHWYN7/ZyHUJA7i970S14wgR1pIsVhYMe4hrYlNZte8Ntp78RG6b+CNI4augvO48r+57nWSLjfvS78agb9slJ4TQohhzNPOG3M8w22A2H32P1w+sx+Pzqh2rQ5HTMttZZUMVy3evwqQ3MWfwL4kyhu6iX0J0diaDiXsG3UVStI0txz+gvP48hekFJEbFqx2tQ5A9/HZU23iR53etxOPz8NCQWSREykYqRFvpdXom9hnL7Mx7cLgqeabseb6pOhJ8QSGF316cnjpe3PUqFz1O5g6+l9SYFLUjCdGhZXS7nkU3PkyMKZoXdq3kw1OfyXH9IKTw20FNYy1FO/+Eo6GS2Rn30KdrL7UjCdEpJFmsLMx6iMHWdIqPvMuKPaupcV9UO1bYksK/yiobqija+SdqGmt4aMgsBib0UzuSEDqr75QAAAwQSURBVJ1KpDGSWel3c2f/fA5VH2FB6W/Ye2G/2rHCkhT+VXTy4mn+944Xqfe6eHhIIf3i+qgdSYhOSafT8bOeP2VR1jwSouL4057XePPgBhp9HrWjhRUp/Ktkl2MfRTv/hFlvYsGwuXIYR4h20D0mmafGLGLsNT/ji/Iv+d2XRRyoPKR2rLAhhR9iPr+PTUe2sHLv/6V7TDILsh4iOTpJ7VhCaIbJYOK2fhOYd8P96NDx4u5XWbn3dardNWpHU52chx9ClQ3VvPjpKxxwHGFUajZT++XJTUyEUMmA+L48Ovx/8dGpv1B64iP2Vx5kfJ8x3NzzXzR7jX1trnWIKYrC5+V/p/jI/4dOp2Pm9dPk2jhChAGT3sitvW/mxqQhbDhcwuaj77Gt/Esm9RnH0KTBmrujlhT+FTrtPMtbh97haO1xBsb34+Gf3oPOZVY7lhDiexKjErg/cyb/qDzI5qPvsXr/m7x/8hNye9/MDdYMzVzeRAr/R6psqOK9Ex/xt3NlRJssTB94ByO7/wRbdBccLqfa8YQQLRiUmMZ1CQPYWbGHLcc/ZPU/1lISmcBNPf+F4SlDO/39KKTw26i87jwfn/4rfz+/Az06buo5ivG9x2Axde4NRYjOQq/Tk5U0hKG2TPZe2M8HJz/lrcOb2XzsPbJsQ7gxeQj94q7tlId7pPBbwd3UyJ4L/+CL8r9zpOY4Jr2RnNQRjO31M+IiuqodTwjxI+h1egZb0xlsTefkxdP85cx2yuxfs+3cl3Qxx3KDLZNhtsH06XpNpyl/KfxLuOhxsr/yG/ZVHmTfhQN4/V4SIxO4re8ERqTcSIw5Wu2IQogQ6dWlJ7+4vif/OvA29l04wM6K3XxR/nc+O/MFsaYY+sdfy4D4vvSP60uSxdph7zvdqsIvKSlhxYoVNDU1MXPmTGbMmNFs/oEDB3jssceor68nKyuLJ554AqPRSHl5OQsXLqSyspI+ffrw7LPPEh0dfkXpbnJz3lXBuTo7xy+e4ljtCc7V2wGINccwIiWLYUlDuLZrr07zTi+E+KEIg5lhSYMZljSYhiY3ey/sZ3/lIQ7XHA3caauLOZZeXXqSGp1M95gUUmOSsUZ16xAf/AYtfLvdTlFRERs3bsRsNjNt2jSGDx9Ov37/75owCxcu5Le//S1Dhgzh0UcfZf369UyfPp0nnniC6dOnM3HiRJYvX85LL73EwoULr+oKfZ/P78Pta8Td1IirqYGLnovUNjoDPy80VHKu3k514//7QkaUMZI+XXqRlXQD1ycOoEdMdyl5ITQoyhjJT5KH8pPkoSiKgqOhksPVRzlUc5QzznL+UXkQv+IHwKgzkBAZT3xkXOBnfERXok3RRJssNJqtuBsVooyRmPRG1f5CCFr427ZtIzs7m7i4OAByc3MpLS3loYceAuDs2bO43W6GDBkCwJQpU3j++ee58847+eqrr1i+fHng8bvvvrtNha/XX/5FaWl+ZUM1/73/TZzeenz+pksuG2mMJC6iK0OTM7BGdcNm6YY1qhsJkXFXXPDBcqshVJkMigGLOTQ3bTHoQzOWMUTjQOgyfTeWPoT/Y3fm7SrUQp9LR3KMleQYK//SMxuAJn8TjoZK7PUOKhoc1DTWUtt4EXtDBUdrj6HwvUs1H2w+mlFvxKQ3YtSb0Ot06NAFfo9Bp2fStbn0jevd5pTB1jto4VdUVGC1WgPTNpuNPXv2XHK+1WrFbrdTXV1NTEwMRqOx2eNtER9/+cM/iYkxP3yMGJ7qsahNvyfUWsqlttBliqFHN1uIxoIeXUNzX4De3XqEZBwIXaZQ69zbVWi1V64k4kinb7v8rlAIuivr9/ub/fmhKEqz6UvN/5/PAzrsBx1CCNEZBC385ORkHA5HYNrhcGCz2S45/8KFC9hsNhISEnA6nfh8vhaXE0II0b6CFv7IkSPZvn07VVVVNDQ0sHXrVnJycgLzU1NTiYiIYMeOHQBs3ryZnJwcTCYTWVlZbNmyBYBNmzY1W04IIUT70imtuAlkSUkJL7/8Ml6vl6lTp1JYWEhhYSHz5s0jIyODgwcP8vjjj1NXV8egQYP43e9+h9ls5uzZsyxevJjKykpSUlJYtmwZXbvKF5WEEEINrSp8IYQQHZ+cYC6EEBohhS+EEBohhS+EEBohhS+EEBrR4Qt///79pKenqx0jYMeOHUydOpX8/HxmzpzJ2bNnVc1TUlLChAkTGDduHGvWrFE1y3defPFFJk6cyMSJE3nmmWfUjtPM73//exYvXqx2jICPP/6YKVOmMH78eH7729+qHSdg8+bNgf+Gv//971XNUldXx6RJkzhz5gzw7eVg8vLyGDduHEVFRWGRad26dUyaNIm8vDz+4z/+A4/Ho0oulA7M5XIp06ZNUwYMGKB2lICbbrpJOXDggKIoivLWW28ps2fPVi3L+fPnlZtuukmprq5W6uvrlby8POXw4cOq5VEURfniiy+Un//850pjY6Pi8XiUgoICZevWrapm+s62bduU4cOHK//+7/+udhRFURTl1KlTyqhRo5Rz584pHo9Hueuuu5RPP/1U7ViKy+VSbrzxRqWyslLxer3K1KlTlS+++EKVLLt27VImTZqkDBo0SDl9+rTS0NCgjB49Wjl16pTi9XqVe++9t91fs/+Z6dixY8rYsWMVp9Op+P1+ZdGiRcrq1avbNdN3OvQe/tNPP83MmTPVjhHg8Xh45JFHSEtLA2DgwIGcO3dOtTzfv/CdxWIJXPhOTVarlcWLF2M2mzGZTPTt25fy8nJVMwHU1NRQVFTE7Nmz1Y4S8MEHHzBhwgSSk5MxmUwUFRUxePBgtWPh8/nw+/00NDTQ1NREU1MTERERqmRZv349S5YsCXyLf8+ePfTq1YuePXtiNBrJy8tr923+f2Yym80sWbKEmJgYdDodAwYMUG2b77A3QPnoo49wu93ceuutakcJMJvN5OfnA99eY+jFF19kzJgxquUJduE7NfTv3z/w7xMnTvDee+/x5ptvqpjoW7/+9a+ZP3++qm/Q/9PJkycxmUzMnj2bc+fO8bOf/Yxf/epXasciJiaGRx55hPHjxxMVFcWNN97I0KFDVcny1FNPNZtuaZtv60UbQ50pNTWV1NRUAKqqqlizZg2/+93v2jXTd8K+8N97770fvDjXXnstdXV1vPbaa+qE4tK5XnvtNTweD4sXL6apqYkHHnhApYTBL3ynpsOHD/PAAw+waNEievfurWqWt956i5SUFEaMGMHGjRtVzfJ9Pp+PsrIyXn/9dSwWC3PmzKG4uJgpU6aomuvgwYNs2LCBTz75hNjYWBYsWMCqVauYNWuWqrkgvLd5u93OrFmzuOOOOxg+fLgqGcK+8MePH8/48eObPfbWW2/x8ssvN7vzVn5+PmvWrCEmpn0ui9pSLoD6+nrmzJlDXFwcK1aswGQytUueliQnJ1NWVhaYDpcL2O3YsYN58+bx6KOPMnHiRLXjsGXLFhwOB/n5+dTW1uJyufiv//ovHn30UVVzdevWjREjRpCQkADAmDFj2LNnj+qF//nnnzNixAgSExOBb+91sXbt2rAo/GAXe1TL0aNHmTVrFr/4xS+499571QuiyicHIRZOH9rOmTNHefzxxxWfz6d2lMCHtpWVlYrL5VImT56s7N69W9VM5eXlyvDhw5Vt27apmuNSNmzYEDYf2u7atUvJzc1VamtrlaamJuWBBx5Q1q9fr3Ys5a9//asyefJkpb6+XvH7/cp//ud/Ks8//7yqmW666Sbl9OnTitvtVnJycpQTJ04oTU1Nyn333ads2bJF1UxOp1MZPXq0UlxcrEqO7wv7PfyOZP/+/Xz00Uf069eP22+/Hfj2GOLKlStVyZOUlMT8+fMpKCgIXPguMzNTlSzfWbVqFY2NjTz99NOBx6ZNm8Zdd92lYqrwNHjwYGbNmsX06dPxer389Kc/5Y477lA7FqNGjWL//v1MmTIFk8lERkYG999/v9qxAIiIiODpp5/m4YcfprGxkdGjR6v+Od/bb7/NhQsXWL16NatXrwbg5ptv5pFHHmn3LHLxNCGE0IgOfVqmEEKI1pPCF0IIjZDCF0IIjZDCF0IIjZDCF0IIjZDCF0IIjZDCF0IIjZDCF0IIjfj/AQXr/BDbiMAAAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -392,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -411,7 +411,7 @@
        "<BarContainer object of 4 artists>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -432,16 +432,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14efa00b608>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a6eb688>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -469,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -575,7 +575,7 @@
        "4       24.59  3.61  Female     No  Sun  Dinner     4"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -586,22 +586,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x14efa0b8088>"
+       "<seaborn.axisgrid.FacetGrid at 0x22238a69ec8>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -616,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -625,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -639,10 +639,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14efa1d96c8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a760fc8>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -670,16 +670,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x14efa222848>"
+       "<seaborn.axisgrid.FacetGrid at 0x2223a800148>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -707,16 +707,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x14efb428708>"
+       "<seaborn.axisgrid.FacetGrid at 0x2223a928ac8>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -737,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -757,16 +757,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x14efb4c5088>"
+       "<seaborn.axisgrid.FacetGrid at 0x2223aa00c88>"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -795,12 +795,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -823,12 +823,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x2223babe388>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x432 with 3 Axes>"
       ]
@@ -843,16 +853,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14efba704c8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a9bfa08>"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -873,16 +883,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x14efbb11348>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223bbf5848>"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -910,7 +920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1474,128 +1484,6 @@
     "               row_colors=network_colors, col_colors=network_colors,\n",
     "               linewidths=.75, figsize=(13, 13))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import msca"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = msca.base.raf_sum(1,2)\n",
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "from msca.base import raf_sum\n",
-    "print(raf_sum(1,2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "function"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(raf_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__builtins__',\n",
-       " '__cached__',\n",
-       " '__doc__',\n",
-       " '__file__',\n",
-       " '__loader__',\n",
-       " '__name__',\n",
-       " '__package__',\n",
-       " '__spec__',\n",
-       " 'potato',\n",
-       " 'raf_sum']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(msca.base)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No cool\n"
-     ]
-    }
-   ],
-   "source": [
-    "msca.base.potato(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "git clone xxx\n",
-    "cd xxx\n",
-    "pip install ."
-   ]
   }
  ],
  "metadata": {
diff --git a/lectures/Lecture 6 - Saving objects.ipynb b/lectures/Lecture 6 - Saving objects.ipynb
new file mode 100644
index 0000000..e5dc188
--- /dev/null
+++ b/lectures/Lecture 6 - Saving objects.ipynb	
@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving Objects in Python\n",
+    "* serialization, marshaling, and flattening. \n",
+    "* same—to save an object to a file for later retrieval\n",
+    "* accomplishes this by writing the object as one long stream of bytes. \n",
+    "* pickling also used to save models in sklearn\n",
+    "* https://docs.python.org/3/library/pickle.html\n",
+    "* https://scikit-learn.org/stable/modules/model_persistence.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Dog:\n",
+    "    \n",
+    "    def __init__(self, name):\n",
+    "        self.name = name\n",
+    "        \n",
+    "d = Dog(\"Rex\")\n",
+    "\n",
+    "assert hasattr(d, \"name\"), \"this object doesn't have attribuet namee\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = [\"a\", \"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open('test_pickle', 'wb') \n",
+    "pickle.dump(test, file)\n",
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b']"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delete from memory\n",
+    "del test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'test' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-143-4e1243bd22c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'test' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open('test_pickle', 'rb') \n",
+    "test = pickle.load(file)\n",
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b']"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### using with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "item = \"some string I made\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test_pickle.pkl\", \"wb\") as file:\n",
+    "    pickle.dump(item, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'item' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-154-8cd64e89cea0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'item' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "del item\n",
+    "item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test_pickle.pkl\", \"rb\") as file:\n",
+    "    item = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'some string I made'"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "item"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3 style=\"color:blue\">make a python object</h3>\n",
+    "<p style=\"color:blue\">- save the object</p>\n",
+    "<p style=\"color:blue\">- delete the object from memory</p>\n",
+    "<p style=\"color:blue\">- reload it</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From d10f870884d8bbe73ed45ce4b07c9cd12e861a10 Mon Sep 17 00:00:00 2001
From: Rafael Vescovi <ravescovi@gmail.com>
Date: Tue, 2 Jun 2020 18:02:30 -0500
Subject: [PATCH 36/36] Ch4

---
 .../Challenge 2 - Face Recognition.ipynb      |   89 +-
 challenges/Challenge 4 - Panorama.ipynb       |  430 +++++++
 challenges/Challenge 4 - Panorama.py          |  196 +++
 data/Photos/IMG_20200602_164740.jpg           |  Bin 0 -> 4373398 bytes
 data/Photos/IMG_20200602_164742.jpg           |  Bin 0 -> 4428120 bytes
 data/Photos/IMG_20200602_164745.jpg           |  Bin 0 -> 4116233 bytes
 data/Photos/IMG_20200602_164747.jpg           |  Bin 0 -> 4432603 bytes
 data/Photos/IMG_20200602_164749.jpg           |  Bin 0 -> 4201397 bytes
 lectures/Lecture 10 - RegExp.ipynb            |   78 +-
 .../Lecture 6 - Matplotlib, Seaborn.ipynb     | 1091 ++++++++++++++---
 .../Lecture 6 - Saving Object.ipynb           |    0
 lectures/Lecture 6 - Saving objects.ipynb     |  180 ++-
 ...eature Extraction and Transformation.ipynb |  936 ++++++++------
 ...n Tree, Regression, Model Evaluation.ipynb |  706 +++++++----
 ...Lecture 9 - Multiprocess and Threads.ipynb |   48 +
 15 files changed, 2851 insertions(+), 903 deletions(-)
 create mode 100644 challenges/Challenge 4 - Panorama.ipynb
 create mode 100644 challenges/Challenge 4 - Panorama.py
 create mode 100644 data/Photos/IMG_20200602_164740.jpg
 create mode 100644 data/Photos/IMG_20200602_164742.jpg
 create mode 100644 data/Photos/IMG_20200602_164745.jpg
 create mode 100644 data/Photos/IMG_20200602_164747.jpg
 create mode 100644 data/Photos/IMG_20200602_164749.jpg
 rename Lecture 6 - Saving Object.ipynb => lectures/Lecture 6 - Saving Object.ipynb (100%)
 create mode 100644 lectures/Lecture 9 - Multiprocess and Threads.ipynb

diff --git a/challenges/Challenge 2 - Face Recognition.ipynb b/challenges/Challenge 2 - Face Recognition.ipynb
index 3fff9e8..1a1d50a 100644
--- a/challenges/Challenge 2 - Face Recognition.ipynb	
+++ b/challenges/Challenge 2 - Face Recognition.ipynb	
@@ -22,17 +22,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import msca\n",
     "import cv2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +47,6 @@
     "        minSize=(30, 30)\n",
     "    )\n",
     "\n",
-    "\n",
     "    return faces\n",
     "\n",
     "def draw_squares(image, faces):\n",
@@ -59,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +85,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 77,  66,  63, ...,  36,  18,  18],\n",
+       "       [ 82,  71,  63, ...,  36,  18,  18],\n",
+       "       [ 84,  71,  60, ...,  36,  17,  25],\n",
+       "       ...,\n",
+       "       [ 45,  22,  19, ..., 225, 110,   3],\n",
+       "       [ 47,  60,  62, ..., 232, 157,   3],\n",
+       "       [ 58,  53,  68, ..., 229, 150,   4]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abba_img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -96,13 +120,13 @@
        "(426, 500)"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -114,13 +138,13 @@
     }
    ],
    "source": [
-    "plt.imshow(abba_img,cmap='gray')\n",
+    "plt.imshow(abba_img,cmap='viridis')\n",
     "abba_img.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +165,7 @@
        "       [354,  78,  74,  74]], dtype=int32)"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -152,25 +176,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "new_img = draw_squares(img,faces)"
+    "new_img = draw_squares(abba_img,faces)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1c38bcfaf48>"
+       "<matplotlib.image.AxesImage at 0x1c711b8d648>"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -193,22 +217,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1c38854e788>"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "TypeError",
+     "evalue": "Image data of dtype object cannot be converted to float",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-13-7a028c132309>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mme\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'../../../../Downloads/wow.png'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mme_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mme\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mme_img\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mfaces\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mface_detect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mme_img\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcass\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mnew_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdraw_squares\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mme_img\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfaces\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, data, **kwargs)\u001b[0m\n\u001b[0;32m   2681\u001b[0m         \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2682\u001b[0m         resample=resample, url=url, **({\"data\": data} if data is not\n\u001b[1;32m-> 2683\u001b[1;33m         None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2684\u001b[0m     \u001b[0msci\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2685\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1599\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1600\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1601\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1603\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    367\u001b[0m                 \u001b[1;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    368\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[1;32m--> 369\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    370\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    371\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    367\u001b[0m                 \u001b[1;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    368\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[1;32m--> 369\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    370\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    371\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   5669\u001b[0m                               resample=resample, **kwargs)\n\u001b[0;32m   5670\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5671\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5672\u001b[0m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5673\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36mset_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    683\u001b[0m                 not np.can_cast(self._A.dtype, float, \"same_kind\")):\n\u001b[0;32m    684\u001b[0m             raise TypeError(\"Image data of dtype {} cannot be converted to \"\n\u001b[1;32m--> 685\u001b[1;33m                             \"float\".format(self._A.dtype))\n\u001b[0m\u001b[0;32m    686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    687\u001b[0m         if not (self._A.ndim == 2\n",
+      "\u001b[1;31mTypeError\u001b[0m: Image data of dtype object cannot be converted to float"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/challenges/Challenge 4 - Panorama.ipynb b/challenges/Challenge 4 - Panorama.ipynb
new file mode 100644
index 0000000..6cee0d2
--- /dev/null
+++ b/challenges/Challenge 4 - Panorama.ipynb	
@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import cv2\n",
+    "import glob\n",
+    "from scipy import signal, misc\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../data/Photos\\\\IMG_20200602_164740.jpg', '../data/Photos\\\\IMG_20200602_164742.jpg', '../data/Photos\\\\IMG_20200602_164745.jpg', '../data/Photos\\\\IMG_20200602_164747.jpg', '../data/Photos\\\\IMG_20200602_164749.jpg']\n"
+     ]
+    }
+   ],
+   "source": [
+    "paths = sorted(glob.glob('../data/Photos/IMG*'))\n",
+    "print(paths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3024, 4032, 3)\n",
+      "(3024, 4032, 3)\n",
+      "(3024, 4032, 3)\n",
+      "(3024, 4032, 3)\n",
+      "(3024, 4032, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "images = []\n",
+    "\n",
+    "for kImage in paths:\n",
+    "    image = cv2.imread(kImage)\n",
+    "    print(image.shape)\n",
+    "    ##\n",
+    "    images.append(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "(3028, 10086, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "stitcher = cv2.createStitcher() \n",
+    "(status, stitched) = stitcher.stitch(images)\n",
+    "print(status)\n",
+    "print(stitched.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x230423cf988>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(stitched)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "willis = stitched[750:1400,6400:6600,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "border = 600\n",
+    "stitched_new = stitched[border:stitched.shape[0]-border,3500+border:stitched.shape[1]-border,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x230419bbdc8>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(stitched_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv2.imwrite('test.jpg', stitched_new)\n",
+    "\n",
+    "# Careful young man.. this is a dangerous road ahead..\n",
+    "# cv2.imshow(\"Stitched\", stitched)\n",
+    "# cv2.waitKey(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x230059039c8>"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "rows, cols = stitched_new.shape[:2]\n",
+    "\n",
+    "vig_size = 3000\n",
+    "\n",
+    "# generating vignette mask using Gaussian kernels\n",
+    "kernel_x = cv2.getGaussianKernel(cols,vig_size)\n",
+    "kernel_y = cv2.getGaussianKernel(rows,vig_size)\n",
+    "kernel = kernel_y * kernel_x.T\n",
+    "mask = 255 * kernel / np.linalg.norm(kernel)\n",
+    "output = np.copy(stitched_new)\n",
+    "\n",
+    "# applying the mask to each channel in the input image\n",
+    "for i in range(3):\n",
+    "    output[:,:,i] = output[:,:,i] * mask\n",
+    "\n",
+    "plt.imshow(output)  \n",
+    "    \n",
+    "# cv2.imshow('Original', img)\n",
+    "# cv2.imshow('Vignette', output)\n",
+    "# cv2.waitKey(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#lets find the willis tower\n",
+    "scale = .2\n",
+    "gray_image = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)\n",
+    "gray_willis = cv2.cvtColor(willis, cv2.COLOR_BGR2GRAY)\n",
+    "\n",
+    "gray_image = cv2.resize(gray_image,None,fx=scale,fy=scale)\n",
+    "gray_willis = cv2.resize(gray_willis,None,fx=scale,fy=scale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(366, 1077) (130, 40)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gray_image.shape, gray_willis.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x23005b7e608>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "noise_level = 0\n",
+    "gray_willis_noise = gray_willis + np.random.randn(*gray_willis.shape) * noise_level\n",
+    "\n",
+    "plt.imshow(gray_willis_noise)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr = signal.correlate2d(gray_image, gray_willis, boundary='fill', mode='same')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y, x = np.unravel_index(np.argmax(corr), corr.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x1080 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax_orig, ax_template, ax_corr) = plt.subplots(3, 1,figsize=(6, 15))\n",
+    "\n",
+    "ax_orig.imshow(gray_image, cmap='gray')\n",
+    "ax_orig.set_title('Chicago City')\n",
+    "ax_orig.set_axis_off()\n",
+    "\n",
+    "ax_template.imshow(gray_willis, cmap='gray')\n",
+    "ax_template.set_title('Willis Tower')\n",
+    "ax_template.set_axis_off()\n",
+    "\n",
+    "ax_corr.imshow(corr, cmap='gray')\n",
+    "ax_corr.set_title('Cross-correlation')\n",
+    "ax_corr.set_axis_off()\n",
+    "\n",
+    "ax_orig.plot(x, y, 'ro')\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#PARKING LOT\n",
+    "#stitched = cv2.copyMakeBorder(stitched, 10, 10, 10, 10, cv2.BORDER_CONSTANT, (0, 0, 0))\n",
+    "\n",
+    "# gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY)\n",
+    "# thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1]\n",
+    "\n",
+    "# cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,\n",
+    "#     cv2.CHAIN_APPROX_SIMPLE)\n",
+    "# cnts = imutils.grab_contours(cnts)\n",
+    "# c = max(cnts, key=cv2.contourArea)\n",
+    "\n",
+    "# mask = np.zeros(thresh.shape, dtype=\"uint8\")\n",
+    "# (x, y, w, h) = cv2.boundingRect(c)\n",
+    "# cv2.rectangle(mask, (x, y), (x + w, y + h), 255, -1)\n",
+    "\n",
+    "# minRect = mask.copy()\n",
+    "# sub = mask.copy()\n",
+    "# while cv2.countNonZero(sub) > 0:\n",
+    "#     minRect = cv2.erode(minRect, None)\n",
+    "#     sub = cv2.subtract(minRect, thresh)\n",
+    "\n",
+    "# cnts = cv2.findContours(minRect.copy(), cv2.RETR_EXTERNAL,\n",
+    "#     cv2.CHAIN_APPROX_SIMPLE)\n",
+    "# cnts = imutils.grab_contours(cnts)\n",
+    "# c = max(cnts, key=cv2.contourArea)\n",
+    "# (x, y, w, h) = cv2.boundingRect(c)\n",
+    "\n",
+    "# stitched = stitched[y:y + h, x:x + w]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/Challenge 4 - Panorama.py b/challenges/Challenge 4 - Panorama.py
new file mode 100644
index 0000000..ca219f9
--- /dev/null
+++ b/challenges/Challenge 4 - Panorama.py	
@@ -0,0 +1,196 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import numpy as np
+import cv2
+import glob
+from scipy import signal, misc
+import matplotlib.pyplot as plt
+
+
+# In[2]:
+
+
+paths = sorted(glob.glob('../data/Photos/IMG*'))
+print(paths)
+
+
+# In[3]:
+
+
+images = []
+
+for kImage in paths:
+    image = cv2.imread(kImage)
+    print(image.shape)
+    ##
+    images.append(image)
+
+
+# In[34]:
+
+
+stitcher = cv2.createStitcher() 
+(status, stitched) = stitcher.stitch(images)
+print(status)
+print(stitched.shape)
+
+
+# In[35]:
+
+
+plt.imshow(stitched)
+
+
+# In[36]:
+
+
+willis = stitched[750:1400,6400:6600,:]
+
+
+# In[47]:
+
+
+border = 600
+stitched_new = stitched[border:stitched.shape[0]-border,3500+border:stitched.shape[1]-border,:]
+
+
+# In[48]:
+
+
+plt.imshow(stitched_new)
+
+
+# In[49]:
+
+
+cv2.imwrite('test.jpg', stitched_new)
+
+# Careful young man.. this is a dangerous road ahead..
+# cv2.imshow("Stitched", stitched)
+# cv2.waitKey(0)
+
+
+# In[70]:
+
+
+
+rows, cols = stitched_new.shape[:2]
+
+vig_size = 3000
+
+# generating vignette mask using Gaussian kernels
+kernel_x = cv2.getGaussianKernel(cols,vig_size)
+kernel_y = cv2.getGaussianKernel(rows,vig_size)
+kernel = kernel_y * kernel_x.T
+mask = 255 * kernel / np.linalg.norm(kernel)
+output = np.copy(stitched_new)
+
+# applying the mask to each channel in the input image
+for i in range(3):
+    output[:,:,i] = output[:,:,i] * mask
+
+plt.imshow(output)  
+    
+# cv2.imshow('Original', img)
+# cv2.imshow('Vignette', output)
+# cv2.waitKey(0)
+
+
+# In[71]:
+
+
+#lets find the willis tower
+scale = .2
+gray_image = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
+gray_willis = cv2.cvtColor(willis, cv2.COLOR_BGR2GRAY)
+
+gray_image = cv2.resize(gray_image,None,fx=scale,fy=scale)
+gray_willis = cv2.resize(gray_willis,None,fx=scale,fy=scale)
+
+
+# In[72]:
+
+
+print(gray_image.shape, gray_willis.shape)
+
+
+# In[73]:
+
+
+
+noise_level = 0
+gray_willis_noise = gray_willis + np.random.randn(*gray_willis.shape) * noise_level
+
+plt.imshow(gray_willis_noise)
+
+
+# In[74]:
+
+
+corr = signal.correlate2d(gray_image, gray_willis, boundary='fill', mode='same')
+
+
+# In[75]:
+
+
+y, x = np.unravel_index(np.argmax(corr), corr.shape)
+
+
+# In[76]:
+
+
+fig, (ax_orig, ax_template, ax_corr) = plt.subplots(3, 1,figsize=(6, 15))
+
+ax_orig.imshow(gray_image, cmap='gray')
+ax_orig.set_title('Chicago City')
+ax_orig.set_axis_off()
+
+ax_template.imshow(gray_willis, cmap='gray')
+ax_template.set_title('Willis Tower')
+ax_template.set_axis_off()
+
+ax_corr.imshow(corr, cmap='gray')
+ax_corr.set_title('Cross-correlation')
+ax_corr.set_axis_off()
+
+ax_orig.plot(x, y, 'ro')
+
+fig.show()
+
+
+# In[ ]:
+
+
+#PARKING LOT
+#stitched = cv2.copyMakeBorder(stitched, 10, 10, 10, 10, cv2.BORDER_CONSTANT, (0, 0, 0))
+
+# gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY)
+# thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1]
+
+# cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
+#     cv2.CHAIN_APPROX_SIMPLE)
+# cnts = imutils.grab_contours(cnts)
+# c = max(cnts, key=cv2.contourArea)
+
+# mask = np.zeros(thresh.shape, dtype="uint8")
+# (x, y, w, h) = cv2.boundingRect(c)
+# cv2.rectangle(mask, (x, y), (x + w, y + h), 255, -1)
+
+# minRect = mask.copy()
+# sub = mask.copy()
+# while cv2.countNonZero(sub) > 0:
+#     minRect = cv2.erode(minRect, None)
+#     sub = cv2.subtract(minRect, thresh)
+
+# cnts = cv2.findContours(minRect.copy(), cv2.RETR_EXTERNAL,
+#     cv2.CHAIN_APPROX_SIMPLE)
+# cnts = imutils.grab_contours(cnts)
+# c = max(cnts, key=cv2.contourArea)
+# (x, y, w, h) = cv2.boundingRect(c)
+
+# stitched = stitched[y:y + h, x:x + w]
+
diff --git a/data/Photos/IMG_20200602_164740.jpg b/data/Photos/IMG_20200602_164740.jpg
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diff --git a/lectures/Lecture 10 - RegExp.ipynb b/lectures/Lecture 10 - RegExp.ipynb
index 3257686..8cc926d 100644
--- a/lectures/Lecture 10 - RegExp.ipynb	
+++ b/lectures/Lecture 10 - RegExp.ipynb	
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -170,23 +170,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Match starts at 9 ends at 11 and contains 38\n",
-      "Match starts at 32 ends at 34 and contains 36\n",
-      "Match starts at 54 ends at 56 and contains 10\n",
-      "Match starts at 76 ends at 77 and contains 8\n",
-      "Match starts at 102 ends at 103 and contains 3\n"
+      "Match starts at 8 ends at 11 and contains 102\n",
+      "Match starts at 22 ends at 24 and contains 38\n",
+      "Match starts at 45 ends at 47 and contains 36\n",
+      "Match starts at 67 ends at 69 and contains 10\n",
+      "Match starts at 89 ends at 90 and contains 8\n",
+      "Match starts at 115 ends at 116 and contains 3\n"
      ]
     }
    ],
    "source": [
-    "ages = \"Homer is 38 years old, Marge is 36 years old, Bart is 10 years old, Lisa is 8 years old and Maggie is 3.\"\n",
+    "ages = \"Papa is 102, Homer is 38 years old, Marge is 36 years old, Bart is 10 years old, Lisa is 8 years old and Maggie is 3.\"\n",
     "\n",
     "# Task: Extract all ages\n",
     "# Thinking: Find all numbers\n",
@@ -200,15 +201,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Match starts at 8 ends at 9 and contains 1\n",
+      "Match starts at 9 ends at 10 and contains 0\n",
+      "Match starts at 10 ends at 11 and contains 2\n",
+      "Match starts at 22 ends at 23 and contains 3\n",
+      "Match starts at 23 ends at 24 and contains 8\n",
+      "Match starts at 45 ends at 46 and contains 3\n",
+      "Match starts at 46 ends at 47 and contains 6\n",
+      "Match starts at 67 ends at 68 and contains 1\n",
+      "Match starts at 68 ends at 69 and contains 0\n",
+      "Match starts at 89 ends at 90 and contains 8\n",
+      "Match starts at 115 ends at 116 and contains 3\n"
+     ]
+    }
+   ],
    "source": [
     "# Task: Extract all ages\n",
     "# Thinking: Find all numbers\n",
     "# Regex pseudo code: find digits, clump consecutive digits together\n",
     "\n",
-    "regex_attempt1 = \"(\\d\\d\\d)\" # <= Find digits and 1 or more repititions\n",
+    "regex_attempt1 = \"(\\d|\\d\\d)\" # <= Find digits and 1 or more repititions\n",
     "\n",
     "for m in re.finditer(regex_attempt1, ages): \n",
     "    print(\"Match starts at\",m.start(), \"ends at\", m.end(), \"and contains\", m.group())"
@@ -287,15 +306,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Rafael\n",
-      "Isaac\n"
+      "Vescovi ,  Rafael\n",
+      "Newton ,  Isaac\n"
      ]
     }
    ],
@@ -303,37 +322,10 @@
     "bestphysicist = ['Rafael Vescovi, PhD',\n",
     "                 'Isaac Newton, physicist']\n",
     "\n",
-    "if data < armagedon:\n",
-    "    rexp = \"(?P<x>\\d)(?P<y>\\d)\"\n",
-    "else:\n",
-    "    rexp = \"(?P<y>\\d)(?P<x>\\d)\"\n",
-    "    \n",
+    " \n",
     "for k in bestphysicist:\n",
     "    m = re.match(r\"(?P<Name>\\w+) (?P<surname>\\w+), \\w+\", k)\n",
-    "    print(m.group('Name'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import multiprocessing as mp\n",
-    "import time\n",
-    "\n",
-    "def run(x):\n",
-    "  return x**2\n",
-    "\n",
-    "start = time.time()\n",
-    "\n",
-    "vect = list(range(2))\n",
-    "print(vect)\n",
-    "pool = mp.Pool(processes=1)\n",
-    "results = pool.map(run, vect)\n",
-    "print(results)\n",
-    "end = time.time()\n",
-    "print(\"Seconds:{}\".format(end - start))\n"
+    "    print(m.group('surname'), ', ',m.group('Name'))"
    ]
   },
   {
diff --git a/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb b/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb
index f9f01e0..aadd623 100644
--- a/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb	
+++ b/lectures/Lecture 6 - Matplotlib, Seaborn.ipynb	
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,12 +46,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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jITnaIZ1RxPczUUrNAf5Laz1FKXUzME9r/e0Gx34C/Ar4vdZ6ul0+FFgKzAI2aK372eVuoE5rHaeUqgeStNYh+1g+cK7WuqCZkHKBQ40d2LlzF336RGdvje6qoCCP0aNHRTsMIWKOaZqU/vNxanauotd1PyVJnRvtkE5qdD+TjmiZfB9rjASsbrWG2csBGK0oxy4/WachR4NjzWpscyzDMFo0sN4ZN6GKpqbiMgwjahsbyaZKrSNxtU5b46rf+g6BnauIm/xN6nqMpK6d7rEdNsdq/HhbgmqOUioOq3Xxtl2UD+Q0qNIbKGiivARIU0q57PIcuxzgmF3vZIslBShv/7sQQoiOFTq8hcD6t3APOY+4CVdFO5wWifTaXGOBvVrrWvv954BSSg21E8R8YJnWOg/wK6VOzsW9xS4PAquAG+3yW4Fl9uul9nvs46vaY7ykG25jHDXyXQvxdeHyo/iWP4MzO5eEWXfGxFIpLRHpZDIYq9UBgNbaD9wOvAXsAvYAb9qHFwCPKaX2AMnAE3b5PcD37EH6GcBDdvnDwFSl1E67zr1tDdbpdBEOh9p6GdFCwWAAV5T2XhAiFhm+Knzv/8laKuXS+3G446MdUotFfAA+BuUChxobM6muriQUCpKenonDceY82xnHJqLp9LhM0yQYDFBZWUpKSgZeb1JU4uqqfe2RInG1TmvjMsNBfO/8P8JleSRe/XNc2Wdef68j4zqpwZhJ1AbgO43k5DSOHy+luDifr4/7f8npdEbsmY626ExxuVzuqCYSIWKJaZr4Vy4mXLyPhNn3RCyRRJIkkwYcDgc9evRstl5X+U2oo8RqXELEisDWdwntW0Pc5G/gGRIzU4BbRTbHEkKIKAoe3EBgw5u4h04lbsLV0Q7nrEkyEUKIKAmXHsb/yXM4ew0lYWbnmbnVGEkmQggRBUZNuTVzy5uC95J/x+GOa/6kGCbJRAghOpgZ8OF7/0+YwXq8l/8AZ2JatENqM0kmQgjRgUzDwLf8GYyKY3jn3IOrR99oh9QuJJkIIUQHql/3GuEj24if9m3c/c+JdjjtRpKJEEJ0kMAXHxL84kM8Yy4lbtTF0Q6nXUkyEUKIDhDK20L9Z6/iHjiB+Kk3RTucdifJRAghIixcdhjfx0/jzMol4eK7cTi73o/erndHQggRQ4zqMnzLHsORkIL3sgdweDrP4o2tIclECCEixKyvxffeo5jhgD0FOD3aIUWMJBMhhIgAMxzE9+F/Y5woxnvp/V1mCvCZSDIRQoh2Zpompe8+TbhgNwkz78TdZ2S0Q4o4WTVYCCHaWWDj3wjsWGGtAjx8WvMndAERTSZKqauAXwFJwAda6weUUnOARwEv8LrW+iG77nhgEZAKrATu1lqHlFIDgCVAT0ADC7TWNUqpdOAVrN0cS4EbtNZFkbwfIYRoTmD3pwS2/IuU8XMwO/EqwK0VsW4updRg4BngWqy94CcqpeYCLwDXACOBKXYZWAnjPq31cMAB3GWXLwQWaq1HABuxtusFeARr3/eRwHPA45G6FyGEaInQka3Ur34JV/+xZM39XqdeBbi1Ijlm8g2slke+1joI3AjUAfu01oe01iGsBHK9Umog4NVar7PPXWyXe4CZfLlP/GLgevv1PKyWCcBrwFy7vhBCdLhwyUF8Hy3EmTkQ75x7cDhd0Q6pQ0Wym2soEFBKvQ0MAN4BdgKFDeoUAv2APmcozwKq7MTTsJyG59jdYVVANlDQkuDsvYzPWnZ2SpvOjxSJq3UkrtaRuBoXrCjk2Ad/wp2cTp8FD+NOTo+JuM4kEnFFMpm4sVoVFwI1wNuAj69uru4ADKwWUkvKsctP1mnI0eBYs8rLazCMM+/z3pRY3YZW4modiat1JK7GGXUnqPvnI2CYxF/2A477XOCrjnpcZ3K2cTmdjiZ/CY9kN1cR8JHWulRr7QP+DswBchrU6Y3Vksg/Q3kJkKaUOtlezOHLlscxux5KKTeQApRH5laEEOLrTu1LUncC7+UP4kzrHe2QoiaSyeQd4DKlVLqdDOZijX0opdRQu2w+sExrnQf4lVIn59DdYpcHgVVY4y0AtwLL7NdL7ffYx1fZ9YUQIuLMcMh6KLEsz9qXpOeQaIcUVRFLJlrrz4H/B6wGdgF5wNPA7cBbdtkevhxcXwA8ppTaAyQDT9jl9wDfU0rtAmYAD9nlDwNTlVI77Tr3RupehBCiIdM08K9YRPjYThJm3oF74PhohxR1EX3ORGv9AtZU4IY+BsY1UncbcG4j5XlY4y6nl1cA3WcStxAiJpimSf261wntX0fclOvwqBnRDikmyHIqQgjRCoFtSwnueB/P6DnEjZ8X7XBihiQTIYRooaBeRWD9X3EPmUr8BfO71UOJzZFkIoQQLRDK24J/5Yu4+o0h4cLv4nDIj8+G5NsQQohmhAq19XR71kC8l9yHwyVr5J5OkokQQjQhXJaH770/4UzJxjv3Bzg8CdEOKSZJMhFCiDMwThThW/ZHHPGJeK/4Ec6E2FweJRZIMhFCiEYYNRXUvft7ME0Sr/gRzuQe0Q4ppkkyEUKI0xj+anxL/4BZX4t37g9xpuc0f1I3J8lECCEaMAM+fEv/iFFdivfy/8CVnRvtkDoFSSZCCGEzQ/X43v8TRvlRvJfciztHRTukTkOSiRBCcHLhxqcIF+4l4aK7cA+Q9bZaQ5KJEKLbM40w/uXPED66nfgZt+EZOjXaIXU6kkyEEN2aaRr4V75I6NBG4qfeTNzIC6MdUqckyUQI0W2Zpkn9miWE9q4mbtK1xI29LNohdVqSTIQQ3ZJpmtR//gbBXcvxjL2cuInXRDukTi2iC8wopT4BegInd0D8PjAEa4MrD/AnrfVTdt05wKOAF3hda/2QXT4eWASkAiuBu7XWIaXUAGCJfX0NLNBa10TyfoQQXUdg8z8Jbl+GZ9TFxJ93o6wA3EYRa5kopRzAcGCc1nq81no81l7vvwGmA+OxdlAcpZTyYm2idQ0wEpiilJprX2oJcJ/WejjgAO6yyxcCC7XWI4CNWDsvCiFEs+q3LiWw6R+4h08nftq3JZG0g0h2c52coP2BUmqbUuo+YA6wXGtdobWuxdqy9zqsHRb3aa0Paa1DWAnkeqXUQMCrtV5nX2uxXe4BZvLllr+LgesjeC9CiC4isON9AuvfwD3kPBJm3ilLybeTSH6LGVhb9H4DmA3cDQwAChvUKQT6AX1aWZ4FVNmJp2G5EEKcUWDXcuo/ew137iQSLroLh1MSSXuJ2JiJ1voz4LOT75VSz2ONiTzSoJoDMLCSmtmGcuzyFsvMTG5N9a/Jzo7N1UMlrtaRuFqnM8dVtfVjqlf/D4lDJ9Hruh/jcHliIq5oiERcEUsmSqnpQLzW+mO7yAEcBhqumNYbKMAaS2lNeQmQppRyaa3Ddp2C1sRXXl6DYZyej1omOzuF0tLqszo3kiSu1pG4WqczxxXcuxr/p8/j6jcG58zvU1bhB/xRjysazjYup9PR5C/hkWzjpQO/V0olKKVSgNuAbwOzlVLZSqlE4FvAe8DngFJKDVVKuYD5wDKtdR7gV0pNs695i10eBFYBN9rltwLLIngvQohOKrhvrZVI+o7Ce+n9ONxx0Q6pS4pYMtFavwO8C2wBNgEvaK3XAL8EPgG2Aq9qrddrrf3A7cBbwC5gD18Ori8AHlNK7QGSgSfs8nuwZoPtAmZgTTcWQohTgvvX4f/0OVx9RuC9TBJJJDlM8+y6ejqxXOCQdHN1HImrdSSu1jlTXMH96/B/8iyu3sPxXv4DHJ74mIgr2tqhm2sQ1pDFV4+3OTIhhIgxwQOfRzWRdEeSTIQQXUrwwOf4lz+Lq9cwvJf/hySSDhLR5VSEEKIjnera6jUM79wf4PAkRDukbuOMyUQptYOvP8txitZ6bEQiEkKIs2DN2nrO7tr6D0kkHayplsl9HRaFEEK0QXDvGvwrFuHqraRrK0rOmEy01itOvlZKnQtMAF4EJtlPtwshRNRVbf0I/6eLrOm/lz+Iwy2JJBqaHYBXSt2OlUR+gvUg4j+VUnc1eZIQQnSAwK7llL37NK5+o60WiSSSqGnJbK77gfOxFlYsASYBD0Y0KiGEaEbgiw+pt9fakifbo68lySSsta46+UZrfRQINVFfCCEiqn7ru9SvfQV37kRr0UZJJFHXkqnBFfZuhyaAUmoBUBHRqIQQohGmaRLY9A8Cm/9p7Udy0V326r+RXbRRNK8lyeQBrHWyhiilCgEf1o6IQgjRYUzTJLD+rwS2LcU9fLq1sZXsRxIzmk0mWus9SqlxWFvwuqwiHWzmNCGEaDemaVC/ZgnBXcvxjLyI+Om3yA6JMabZZKKUcgLfBS4BgsBS4OUIxyWEEACYRhj/yhcI7V2DZ+zlxJ93o+zZHoNaktr/iLVvyFKsbXi/p5T6z4hGJYQQgBkO4v/4aUJ71xA3+RuSSGJYS8ZMLgHGn9xvXSm1BGt/kocjGZgQonszg/X4PnyScP4XxE+9mbixl0U7JNGEliSTarveyenABlATsYiEEN2eGajDt+wxwiX7iZ95B3EjZkU7JNGMphZ6/IH9sghYqZR6GQgDN2HthNgiSqk/AFla69vtKcaLgFRgJXC31jqklBoALAF6AhpYoLWuUUqlA68Ag4FS4AatdZFSKg54HpiMNbtsvta6xTEJIWKX4avCt/SPGMfzSZj9b3gGnxvtkEQLNDVmco79pxLYCUwEpgAHsFonzVJKzcba+/2kJcB9WuvhgAM4uSzLQmCh1noEsJEvu9AeAVZprUcCzwGP2+X3A7V2+YPA4pbEI4SIbUZ1KXVv/wajshDvZQ9IIulEmlro8Y62XFgp1QP4DfBbYJxSaiDg1Vqvs6ssBn6tlFoEzASubVC+AvgpMM8+BvAa8JRSymOX/y87zpVKqWyl1ACt9ZG2xCyEiJ7w8WP4lv4BM1hP4rwf4+o9LNohdTnBkEF9MByRa7dkavD5wM+AZKzWhAsYpLUe0MypzwK/BPrb7/sAhQ2OFwL9gCysdb9Cp5V/5Ry7O6wKyG7iWpJMhOiEwsX7qXvvMRwuD4lX/xxXj/7NnyRaZeOeEpZ8uJdJI3pyyyXD2/36LRmAXwT8D3Ad8AxWC+Ktpk5QSn0XOKq1/thedRisLrWGm205sLrLTi+HL7vRTp8DeKZzHLSw6+2kzMzk1lT/muzslDadHykSV+tIXK0Tibjq9m+m+N3f407pQc7ND+PJ6B0TcbWHWIirosrPM3/bzmc7ChnSL41vXTwsInG1JJmYWuvfKaWysAbeb8Aa12jKjUCOUmor0AOrVWMCOQ3q9AYKgBIgTSnl0lqH7ToFdp1jdr18pZQbSAHKgXy73oHTrtVi5eU1GMYZN5JsUnZ2CqWl1Wd1biRJXK0jcbVOJOKydkd8HmePfsTP/QGVoSRo5Wd0p++rNQzTZNW2At745AChsMH1Fw7h0nP707tX6lnF5XQ6mvwlvCUPLZ781APAGK21D2tW1xlprS/RWo/RWo/HGtt42x6D8SulptnVbgGW2UuzrMJKQAC3Asvs10vt99jHV9n1T5UrpaYDfhkvEaJzCWxfhv+TP+PKGU7iVT/DmZgW7ZC6jKKKOn7/6hZeek8zsFcyv77zXOZOHYgrgmuZtaRlsl4p9TrWDKt3lVLDOfsl6BcAzymlUoHNwBN2+T3AS0qph7DGPW62yx8GFiuldmLNKltglz8JPGuX12MlJiFEJ2CaBvXrXie4433cg6eQcNH37JV/RVuFwgbL1uXxr7V5xLmd3D53BDPG5nTIqgEtSSYPAudprfcqpR4E5vDlD/tmaa0XY0/d1VpvA742109rnQdc2Eh5BXB1I+V+vjrlWAjRCZjhEP4ViwjtX4dn9BziL5gvCza2k/3HTvDSsj0cK6tlyoiezJ8zjLTkjtt5sqmHFic2eBuw3xdiLfKYFOnAhBBdixmow/fBk4QLdhN37nXEjZsn62y1gzp/kDdXHGTFlmNkpMZz/3VjGT80q8PjaKpl0tSMLRPrqXQhhGiWUVOB771HMY4XknDhXXiGT2v+JNEk0zTZsKeE1z7aR1VdgEum9OfaGYNIiGtJh1P7a+qhxUEdGYgQomsKVxzFt+wxzEAd3rk/wN1vdLRD6jdE7/YAACAASURBVPRKKn0s+UDzxcEKBvZK4YHrx5LbOzWqMUUnhQkhuoXQsV34PngShyeexKt+jitrYLRD6tRCYYP3Pj/Cv9Yexul0cPPsYVw8qW9EZ2m1lCQTIUREBPeuxr/iRZzpOXjn/gfO5Mxoh9Sp7ck7zssfaArL65ikspk/ZzgZKR03wN4cSSZCiHZlmiaBTf8gsPmfuPqOxnvJvTjiEqMdVqd1ojbAG8v389nOIrLSEnjw+rGMHdLxA+zNaWo21/8BfqW1PrvHxIUQ3Y4ZDuJf8bw19VfNIH76bThc8jvr2TAMk0+2HONvKw8SCIa58oKBzDs/l3iPK9qhNaqp/8oXAzOVUjdprYs6KiAhROdk+Kvxf/Ak4aK9xE25jrjxMvX3bB0oOMGSD/aSV1TNqNwMFlwynJzM2H4io6lkMhP4BbBRKXWn1vqDDopJCNHJhCsL8L33J8zaCmtDqyHnRTukTqm6LsBbKw6wclsh6clx3H3NaKaM6NkpknJTU4MN4BGl1LvAIqXUNXy5sCJa60c7ID4hRIwLHduF78P/xuFyk3jlz3D1GhrtkDodwzBZsdXq0vIHwlx+3gCuuiAXb3zn6SJsSaRhrOXdRwMnR9FkHEUIQWDXJ9SvWWLN2Lr8AZwp2dEOqdPZl1/JKx/s5UhJDSMGpLPgUkXfrNju0mpMUwPwDqxurh8Dv9RaP9VhUQkhYppphKn/7DWCOz/C1X8s3tn/hiPOG+2wOpXj1fW8+ekBPttZREZKPP927Rgmq+xO0aXVmKZaJmuxWiLTtdZfdFA8QogYZ9bX4vv4acL5X+A55zLiz7sRRww8NNdZhMIGH244yttrDxMOG8w7fyBXnp9LfFxsztJqqaaSySbgh1rr+o4KRggR24zKQurefxyzqpT4mXcQN2JWtEPqVLbtL+MvH++j+LiP8UOzuGn2UHpmdI1ncJoagL+vIwMRQsS2ugNbqP3HH3E43Xiv/AnuHBXtkDqNwvJa/vLxfnYcLKd3j0QevH4cY4d0rRUBOs9UASFEVJimSXD7e1Sv/yvOjL54L3sAZ0rsPYEdi2r9Qd5efZjlm/OJ8zi58eKhzJ7UD7er63ULRjSZ2E/RX4c1++t5rfWjSqk5wKOAF3hda/2QXXc8sAhIBVYCd2utQ0qpAcASoCeggQVa6xqlVDrwCtZS+KXADfJwpRDtywwF8K98gdD+dSSNmIrj/NtxeBKiHVbMCxsG7645xJJlu6n1B5k5rg/fmDGY1KS4aIcWMRFLj0qpWVhP0Y8FJgP/rpQaB7wAXAOMBKYopebapywB7tNaDwccwF12+UJgodZ6BLARaytfgEew9oQfCTwHPB6pexGiOzJqyql7+zeE9n9O3ORv0vObP5JE0gI7Dpbzqxc28MzfttMvO4lf3T6F2y4f0aUTCUQwmWitVwAXaa1DWK0KN5AO7NNaH7LLlwDXK6UGAl6t9Tr79MV2uQfrSfw3G5bbr+dhtUwAXgPm2vWFEG0UKthN3d/+N8aJEryXPUD8xKs77ZTVjnKstIZH39jKY29sIxQ2+OUd5/LjmycwoFdKtEPrEBHt5tJaB5VSvwZ+BPwV6IO19e9JhUC/JsqzgCo78TQsp+E5dndYFZANFLQktszM5LO5pVOys2PzH4jE1ToS11eZpsmJ9f+i+uOX8WT2odd1PyEus2/U42pONOOqrK7n1ff38P66w3gTPHzn6tHMmzYYjzt2x0Ui8X1FfABea/0rpdTvgH8Bw/nq0/MOrKfrnS0sxy4/WachR4NjzSovr8Ewzu5B/uzsFEpLq8/q3EiSuFpH4voqM+jHv/JFQgc+x507ifgLv8sJwwt2LPJ9fVV9MMyHG46ydF0ewZDBxRP7cfX0QSR7PVQer+1y35fT6Wjyl/CIJROl1AggQWu9VWtdp5T6G9ZgfLhBtd5YLYl8IKeR8hIgTSnl0lqH7TonWx7H7Hr5Sik3kAKUR+p+hOjKjMoifB8+iVFZICv+NsMwTD7bWcTfVh7keHU9E4Zlcd2FQ2J+Vd9Ii2Q7bDDwnFIqXikVhzXo/iyglFJDlVIuYD6wTGudB/iVUtPsc2+xy4PAKuBGu/xWYJn9eqn9Hvv4Kru+EKIVgoc2Ufv3/43pq8J7xY+Jn3ClJJIz+OJQOb9evIHn391NWlIcP50/gX//1thun0gggi0TrfVSpdS5wBas1shbWuu/KKVKgbeABKyEcHJwfQFW8kkFNgNP2OX3AC8ppR4CjgA32+UPA4uVUjuBSvt8IUQLmUaI+vVvEtz+Hs7swXgvuVe21j2DvKJq3vx0PzsPHycrLYG7rxnN5BE9cUrSPcVhmt1uAeBc4JCMmXQciat1OiIuo/Y4/o+fJly0F8+o2cSffxMOV9OTIbvj91Va6ePvKw+yblcxSQlurrogl4sm9mvR4HpX+74ajJkMAg6fflyegBeimwnl78S//BnMUICEi7+PZ+j50Q4p5lTVBvjX2sN8uuUYLqeDeecPZO55A0lMkB+ZZyLfjBDdhGkYBDb/k8Dmt3Fm5OCdcy+ujL7Nn9iN+OpDvL/+CO9vOEowaDBjXA5XTxtERkp8tEOLeZJMhOgGjLpK/MufJVywG/fwaSRMuxWHR35AnhQMhflk8zHe+SyPGl+QySqbb8wcLAPrrSDJRIguLpT/Bf5P/owZ8JMw6zt41IxohxQzQmGDtV8U8faaQ1RU1TMqN4NvzRrCoJzUaIfW6UgyEaKLMo0QgY3/ILD1XZwZffBe+VPp1rIZpsn63cX8Y9UhSo77GNwnle9cMZKRuT2iHVqnJclEiC7IqCrFt/wZjJIDeNRM4qctwOGWbi3TNNm8t4x/rD7IsdJa+mUncf+3xjJuaKY8W9NGkkyE6GKCB9bjX/kiAAmz/w3PkPOiHFH0mabJjoPl/H3VIfKKqunVI5HvXT2Kc0f2kmdF2okkEyG6CDPgw7/2FUJ7V+PsOQTvxXfjTM2OdlhRZZomuw4f5x+rDnKgoIqstATuvGIk54/phUv2rW9XkkyE6ALCJQfxLX8Gs7qUuAlXETfpGhzO7vu/t2ma7Mo7zj9XH2J//gl6pMZz2+WKaefkdMldDmNB9/3XJkQXYBphAlvfJbDpnziS0vFe+bNuvTe7aZrstpPIvvwTZKTE8+1LhzNjbJ+YXhK+K5BkIkQnZVSV4PvkzxjF+3EPmUrC9FtwxHfP5yJM02Tn4QreXnOY/ZJEokKSiRCdjGmahPQq/J+9Cg5Ht14SxTRNNuwqYsmy3RwsqKJHajy3XDqc6ZJEOpwkEyE6EaPuBP6VLxI+shVXn5EkXPjdbrnSr2GabNlbyjtr88grriYzNYFbL7PGRCSJRIckEyE6ieDBDdSv/h/MoJ/48+fjGTMHh6N7/eAMGwbrd5fw7md5FJTV0jPdywM3jmf0gHQZWI8ySSZCxDjTX4N/zcuEDnyOM3sQ3gvvwpXRJ9phdahgyGDNF4W8t+4IJZU++mYl8b2rRjFlZE9690qLyaXeu5uIJhOl1K+AG+y372qtf6KUmgM8CniB17XWD9l1xwOLgFRgJXC31jqklBoALAF6AhpYoLWuUUqlA69g7ehYCtygtS6K5P0I0dGChzdTv+olzPoa4iZ/09pO1+mKdlgdxlcfYuW2At5ff4TKmgCDclK47+JzGD8sSx42jDERaxfaSeNSYAIwHpiklLoZeAFrC9+RwBSl1Fz7lCXAfVrr4YADuMsuXwgs1FqPADZi7bAI8AjWVr0jgeeAxyN1L0J0NMNfje/jZ/B/8ASOxFQSr/1fxE+8utskkqq6AH9feZCfPL2W15fvp3ePRH5403geunUyE4dnSyKJQZFsmRQCP9RaBwCUUruB4cA+rfUhu2wJcL1Sahfg1Vqvs89dDPxaKbUImAlc26B8BfBTYJ59DOA14CmllEf2gRedXc2ez6hb+mdMfy1xk75htUZc3aNHuqTSxwfrj7B6eyGBkMHE4dnMnTqAIX3Soh2aaEYk94DfefK1UmoYVnfXk1hJ5qRCoB/Q5wzlWUCV1jp0WjkNz7G7w6qAbKCg3W9GiA5g1FVSv/plqg9vwpk1EO8VP8aV2T/aYXWIw0VVvPf5ETbsKcHpcHD+mN5cfu4A+mR1z+dmOqOI/7qjlBoNvAv8GAhhtU5OcgAGVneb2YJy7PKTdRpyNDjWLHsv47OWnZ3SpvMjReJqnViIyzRNqrd9TMXH/4MZCtLj4ltIO++qmOzSas/vyzRNNu0p4e+f7mf7/jISE9x8Y9ZQrp45mMw0b9Tiak/dKa5ID8BPA94CHtRa/0UpNQvIaVClN1ZLIv8M5SVAmlLKpbUO23VOtjyO2fXylVJuIAUob2ls5eU1GMbpeaplsrNTYnL2iMTVOrEQl3GiCP/KxYQL9+DqPRzvzDtJHzYs6nE1pr2+r2AozGc7i/lgw1EKymrJSInnhouGMnNcHxIT3BiBUKs+Jxb+Ozamq8XldDqa/CU8YslEKdUf+Adwo9Z6uV38uXVIDQUOAfOBF7TWeUopv1JqmtZ6DXALsExrHVRKrQJuBF4FbgWW2ddaar//rX18lYyXiM7CDIcIbFtKYMvb4PIQP+N2PCNmdunnRqpqA3y65RjLN+dTVRekf89kvnvlSM4d2UueEekCItky+RGQADyq1KmF554BbsdqrSRgJYQ37WMLgOeUUqnAZuAJu/we4CWl1EPAEeBmu/xhYLFSaidQaZ8vRMwLFWrqV72EUVmAe9Bk4qd9G2dierTDipj80ho+3HCUz3YWEwobjB2SyaVT+jNyYIZsSNWFRHIA/gHggTMcHtdI/W3AuY2U5wEXNlJeAVzdtiiF6DiGv5rA528Q1KtwJGfivfxB3APGRzusiDBMk+37y/lw41F25x0nzu1k+jm9uWRKf3IyZVC9K+oe8w2FiCLTNAjqVdR//gYE/HjGziV+0rU4PF1vG906f4jVOwpZvimfkkofGSnxfGvWYGaN70uy1xPt8EQESTIRIoLCpYfxr3kZo+QArhxF/LRbcfXoG+2w2t2xslqWb85n7RdF1AfCDO2XxjdnDWbi8GwZD+kmJJkIEQGmv4b6DW8S3L0ChzeFhAvvwj3sgi41RhA2DLbuK2P55mPszjuO2+XkvJE9mT25H7m9U6MdnuhgkkyEaEemESa4+1PqN/4NAj48Y+YQP/kbOOISox1auzlRU8/KbQV8urWA49X19Ei1urJmjOtDamJctMMTUSLJRIh2EirYTf3aVzEqjuLKGUH8BQu6zBPspmmyfX8pf/9kP1v2lhI2TEbnZvDtS4YzbmgWTmfXaXGJsyPJRIg2MqpKqF/3OqHDm3AkZ5Iw517cgyZ3iS6tGl+QNTsKWbG1gKKKOpIS3Mye1I8LJ/Sld4+u09oSbSfJRIizZAbqCGx5h8COD8DpspaIH3s5Dnfn7uoxTZO9RytZua2ADXtKCYUNhvRJ5YEbJzCyXypxnthb5kVEnyQTIVrJNEIEd39KYNM/Mf01uIdfQPyU63AmZUQ7tDapqg2w9osiVm6zWiHeeBczxuVw4fi+9O+ZHLPLg4jYIMlEiBYyTZPQ4c0E1v8V40QRrj4jiZ96I66s3GiHdtYMw2Tn4QpWbStgy74ywobJkD6p3HnFSKaM6El8nLRCRMtIMhGiBcJF+/B//jpG8X6c6Tl4L3sA14DxnXZcpKTSx+rthazZUcjx6nqSvR5mT+rHjHF96CvLvouzIMlEiCaEK/IJbHiLUN4WHInp1oKMakZMLg/fHH8gxMY9pazeUcjeo5U4gNGDe3Dz7GGMG5qFxy0PF4qzJ8lEiEYYVSXUb/onoX1rwZNA3JRvETfm0k63BIphmui846z5oohNupT6YJheGV6+OXMwF4zpTY/UhGiHKLoISSZCNGDUVBDY8jbBPavA6cQz9nLix8/DkdC2zdQ6WkFZLZ/tLGLdziLKq+rxxrs4b1Qvpp3Tm6F90zpt95yIXZJMhMDaMjew9V2Cuz8B08QzchZxE67qVDO0TtTU8/nuEj7bWUReUTVOh4PRg3pw3YVDmTAsS6b0ioiSZCK6NaOuksC2ZQR3LQcjjGf4NOImXo0zJTvaobWIrz7E5r2lrNtVzK7DFZgmDOyVwk0XD+W80b1JS+rcz7yIzkOSieiWjNrjlG15g9rNH4IRwj3sAuInXoMztWe0Q2tWIBhm+4Fy1u8uZtuBcoIhg6y0BOadP5Cpo3rTR2ZjiSiIeDKxd05cC1yptT6slJoDPAp4gde11g/Z9cYDi4BUYCVwt9Y6pJQaACwBegIaWKC1rlFKpQOvAIOBUuAGrXVRpO9HdG5GVSmBbUsJ6lVgGriHTSN+wpU403pFO7QmBUMGOw9XsGF3MZv3lVEfCJOa6GHmuD5MHdWLwX1SZRxERFVEk4lS6jzgOWC4/d4LvADMAo4C7yql5mqtl2EljO9qrdcppZ4H7gKeBhYCC7XWf1FKPYy1Xe9PgUew9n2fp5S6BXgcay94Ib4mfPwYga1LCe3/DBxOPMOn03v2DVQGY3d9qVDYYNfhCjbsKWHz3jJ89SES492cO6In543qhRqQjssp03lFbIh0y+Qu4F7gZfv9ucA+rfUhAKXUEuB6pdQuwKu1XmfXWwz8Wim1CJgJXNugfAVWMplnHwN4DXhKKeXRWgcjekeiUwkV7SOw9V3CR7aCKw7P6DnEjZuLMykDT3oKxNjyIMFQmPU7i1i+Po8t+8qoqw/hjXcxcVg2U0b2ZFRuD9lsSsSkiCYTrfV3AZRSJ4v6AIUNqhQC/ZoozwKqtNah08q/ci27O6wKyAYK2v1GRKdiGgahvC0Eti/DKN4P8UnETbwGz5g5OBNSoh3e1/gDIb44WMFGXcL2A+X4A2G88W4mDsti8ggrgcgDhSLWdfQAvBMwG7x3AEYryrHLT9ZpyNHgWLMyM9v23EB2duz9UILuHZcR8FO9/VOqNrxDsKIQd1pPMi69k5Rxs3HGNf5wXrS+rxM19WzYVcRnO4rYsreEYMggNSmOmRP6ccHYHMYOzY7JBNKd/32dje4UV0cnk3wgp8H73lgtiTOVlwBpSimX1jps1znZ8jhm18tXSrmBFKC8pYGUl9dgGKfnqZaJ1dVTu2tcRk05wZ0fE9j9KQTqcGYPJmHOPbhzJxFwuig/EQS+3vvZ0d9X8fE6tu4rY8u+MvblV2KakJESz6xxfZg4PJth/dNwOZ3d9r/j2ZK4Wuds43I6HU3+Et7RyeRzQCmlhgKHgPnAC1rrPKWUXyk1TWu9BrgFWKa1DiqlVmENrL8K3Aoss6+11H7/W/v4Khkv6T5M0yRcuIfgFx8RytsMgHvQZOLOuQxnzyExMbPJMEz2HzvBtgNlbN1XRmF5HQD9spO48vxcJg7PZkCv5JiIVYi26tBkorX2K6VuB94CErASwpv24QXAc/ZU4s3AE3b5PcBLSqmHgCPAzXb5w8BipdROoNI+X3RxZsBHcP9nBHcuxzieb42HjJ2LZ9TFOFOyoh0eNb4gOw9VsP1AGTsOVlDjC+JyOhjeP50LJ/Rl/NAsstO90Q5TiHbXIclEa53b4PXHwLhG6mzDmu11enkecGEj5RXA1e0Zp4hd4bI8gntWENy3FoJ+nJkDiZ95B56h50d1Z0PDNDlaXMP2g+XsOFjOgWMnME1I9no4Z3Am44dlMTq3B4kJ8nyw6NrkX7iIWWbAR/DgeoK7V2CUHgSXG/fgc4kbPRtn9uCodQ9V1QXYdaiCL+w/VbUBAAb2TmHe+bmMG5LJoJxUnE7pvhLdhyQTEVNM08Qo3k9QryR4YD2E6nFm9CH+ggVWKyQKq/cGQ2H2H6ti56EKdh6q4EhxNSZW62NUbgbnDM5kzOBMWQdLdGuSTERMMGrKCe5dQ3DfGswTxeCOxzPkPDwjZnb4gLphmBwpqWb34ePsOlzBvvwTBEIGLqeDIX3TuHbGIMYMzmRgrxRpfQhhk2QiosYM1BE6uJHgvrWECzVg4spReMZfiXvQZBxxHTNQbZgmBaW17D5ynD15x9FHKqmrt56T7ZuVxMzxfRg1sAdqQDreePlfRojGyP8ZokOZoQChI9sI7V9H6Og2CIdwpPUibtK1eIad3yGr9hqmSX5JDZ/tKWHTrmL2Hq2kxmfNKs9KS2CSymbkwAxGDMwgPblz7awoRLRIMhERZ4QCBA9vJnRwA6G8LRD04/Cm4hlxoZVAIjyYHgwZHC6qYl/+CfYerWRf/gl8dssjKy2BcUMyUQMyGDEwnaw0mbYrxNmQZCIiwgz6CR3dQejQJvKObsMM+CA+Cc/gc3EPnYorZwSOCK14W1Ub4MCxE+wvOMH+/BMcKqwmFLZW2undI5EpI3oyvH8a54/vhyMUjkgMQnQ3kkxEuzHqThA6spXQ4c2Ej+2CcBBHQgrJIy8g1HcCrj4jcDjb959cKGyQX1rDgWNVHCw4wYFjVZRU+gBwOR3k9k5h9qS+DOuXztC+aaQ2mHGVnZEYk8tdCNEZSTIRZ800TYzyI9YYyJFtGCUHARNHciaeEbNwD5qEq/dwsnult8sPbcM0Ka6o43BhNYcKqzhUWEVecc2pVkdaUhxD+qYxa3wfhvZLI7d3Ch637HsuREeQZCJaxfTXEDq2i9DRHYTzd2DWVQLgzB5M3KRrcedOwNmjf5vHQAzDpPh4HXnF1eQV2X+Kq/HVW91ScR4nA3tZrY7BfdIYlJNCZmqCrHMlRJRIMhFNMkMBwsX7CR/bRejYTozSw4AJcYm4+43B3f8cXP3PwZmYftafUR8MU1BWy5Hiao6U1HC0uIajJTXUB63E4XY56d8zmamjepPbO4VBOankZCXKLoNCxBBJJuIrzFA94ZKDhAv2EC7cQ7j4ABghcLhw9RpC3KRrcPcdjbPnYBzO1nUhGYZJSaWPY6U1HCutJb+0hqOltZRU1J3atMYb76JfdjIzxuYwoFcKA3ol0ycrSXYXFCLGSTLp5oy6E1bLo3gf4eL9GKWHwAgDDpyZA/CMmYO7z0hcvYe3+CHCUNig+LiPwrJaCstrqagJcvBYJYXldafGNxxAdrqXfj2TmTqqF/2ykxnQK5msNOmqEqIzkmTSjZhBP+GyPIzSw4RLDhAuPYhZXWYddLpxZucSd85luHIUrl5DccQnnfFahmlSWV1P8XEfxRV1FFXUnfq7tNKPYX658Vh2hpfeGYmMys2gT1YS/bKT6ZOZRHycDI4L0VVIMumiDF8VRvlRjPIjhMvzMMqOYFQWcnIXZEdSD1y9huAaPRtXz6E4s3NxuDxfuUZ9IExZlZ+ySh9lJ/yUHPdRWmn9Kan0EQx9uUuyx+2kZ4aX/j2TmTKyFzk9EsnJSqR3j0T6982QKbhCdHGSTDox0zQxfVUYJ4owjh+jrL6MuoLDGBX5mL6qU/UcST1wZg4gbsi5uLJzcWblQkIaVXUBjlfXU3HCT/nRIiqq/JRX+Sk/Yf1dXffVjSvjPE56pnvpmeHlnMGZ9Mzwnmp1ZKTG45TuKSG6rU6dTJRS84GHAA/wJ631U1EOqd2ZRhizrhKjugyzqgSjqgSjqhSjqhjjRBEEfKfqBjwJkJZDqPcYfN5eVHl6UubKoqLeTWVtgBN5ASp31lNZs5PK6sBXuqIA4txOeqQmkJmWwIBeKWSlJZCVnkBWmpfstARSk+JkPEMI0ahOm0yUUn2B3wCTgHpgrVLqE631ruhG1jJmOIRZX4Ppqzr1x6ittBJH3XHC1RWYtcfBV4nD/LI7ycSB35NGjTuDSreizJVOUSiZI/5UCmri8BcbDT7FBxwFICnBTXpyPOnJcfQekEFGSjw9UuLJSEkgIyWezLQEkhLckiyEEGel0yYTYA6w3N6+F6XUm8B1wP+J5Ice2bWDvJpy6nz1YBrWzCczDOGwNYU2HMJhBHCEAjjCARzhelxhP66Q9bfb8BNn+PCYwUavX2+6qTK8HDeSqDTSqTT6Um4kczycRIWRTIWRRBgX8XEuUrwekrwekhM99MzyMCYzCTeQmhRHamIcqUlxpCVZf3vcMrVWCBE5nTmZ9Pn/7d17kJV1Hcfx99mzF3aXZbi4SeFlzPJrdkOxvOFEIzZNOpMN0sVIHVuBSaeZBnQcwUmosYsBhhY2KsLgkCRdjBgdMigJUzPd0MxvjIGTSo1jhUGwy2X74/c79bCes+zynH2es+3nNcPAczm7X77Pb5/v/p7L7wfsTCzvpMwc8pWMG3d0M/a9tnkpIwr7GXuE/bp66unuqaerp56unga6Co10MYLuwii66prpLrZwoNjMgYaRHGxs41DTSGgZTWNLK81N9bQ0NTCmuZ4JTaFgtDbX09rcQFtLI63NDUPuvYv29ra8QyhLcQ2M4hqY4RTXUC4mdUDyon8BOFRh3zd5/fXdHDrUc+Qde2ma/nWKPfvY9UYXhQIUivUU6ooU6ooUGxqpa2ikrr6BlmKRUcUCxWKhqm9qd+/tpntvd9lt7e1tNfnUlOIaGMU1MIprYI42rrq6Qp+/hA/lYvIycH5ieTzw6mB/01Fjx9VsIxERyctQLiaPADebWTuwB5gGzMw3JBGR4WloXXhPcPdXgHnAJqATWO3uT+YblYjI8DSUeya4+2pgdd5xiIgMd0O2ZyIiIrVDxURERFJTMRERkdSG9D2To1SE8Mx0Gmk/P1gU18AoroFRXAPz/xRX4jNl544o9PQM/MW9IW4ysDnvIEREhqjzgV/3Xjkci0kT8AHC8CsHc45FRGSoKAJvBX5LGFz3MMOxmIiISJXpBryIiKSmYiIiIqmpmIiISGoqJiIikpqKiYiIpKZiIiIiqamYiIhIasNxOJWjZmaXAfOBBuA2d/9OjrF8GfhkXFzv7teb2b2EN/z3xPUL3P3HGce1CXgLsD+umgWcTI55M7MO4NrEqpOAVUArOeXLzEYBjwEXu/sOM5sKLAaagTXuPj/uNxG4GxgFPArMdvcDGcY1E/giYYrsp4BZ7t4d299VyTdIWgAABg1JREFUwD/iR+8azONaJq6ybb1SHrOICzgNuCWxeQLwhLtfnGW+KpwbBr196aXFfjKzCYQhBCYR3v58DPiMuz+fQyxTgQXAhwk/5A8DdwALgY+4+86sY4pxFQjTKZ9YapC1lLcYz7uBnwDnECZWyzxfZnYWcBdwKnAK8DfAgQ8BfwHWE4ruQ2b2HNDh7o+b2T3AU+6+LKO4GmMsk4B/ASuATndfYmbrgFvc/TeDEUtfccVi8iy9jp2ZNVMhj1nFldg2HtgCfNTdt2WVrwrnhruBbzDI7UuXufpvKrDR3f/u7nuAtcClOcWyE5jj7t3uvh/4I3BC/LPczLaa2QIzy/r4Wvx7g5n93syupbbyBrAMuBH4N/nl62rgGuDVuPxBYJu7b49F+D5gupmdCDS7++NxvxXA9Azj6gK+4O5vuHsP8CwhZwBnAjfG3N1hZiOyisvMWih/7MrmMau4erkVuNPdt8XlrPJV7txwChm0LxWT/nsb4UCV7ASOyyMQd/9DqQGY2TsJXdqHgY2ErvTZhMHYPp9xaGOAXwCfAC4AZhN+6Gsib/G3tmZ3fwAYT075cvcOd08ONlqpbWXa5nrH5e4vufvPAcysnXCp8EEzGwk8A1wHnAGMBm7KKi4qH7tc81USfyanAEvjcmb5qnBuOEQG7Uv3TPqvjtBtLCkQDlJu4iWb9cB17u6Ek3hp2+3A5YRueCZiF/6/3fjYbV4MfDWxW555m0WIB3f/MznnK6FS26qJNhcvVT4E3OPuv4yrP5bYvghYDszLIp4+jt1aaiBfwEzgu+7eBeDuu8k4X8lzA3CA0DspGZT2pZ5J/71MGDGzZDzlu7eZMLPzCL2AG9x9pZm918ymJXYp8L+b4FnFNNnMLugVww5qIG9m1ki4ZvzTuJx7vhIqta3c25yZnUq4z7XS3b8S151gZlcldss0d30cu9zzFV0C3F9ayDpfvc8NZNS+1DPpv0eAm2N3fw8wjfAbSObM7HjCTeRPufvGuLoA3GZmG4HdMbaVGYc2GlhoZucSnty6ApgB3FcDeXsf8Kd43wZqI18lTwBmZu8AtgOXAcvd/SUz22dm57n7FuBzhB5CJsysDdgAzHP3VYlNe4Fvxif3dhDuG2T51GClY1c2jxnGhZkdQ7iUuj2xOrN8VTg3ZNK+1DPpJ3d/hdAt3QR0Aqvd/cmcwpkLjAAWm1mnmXUC5wJfIzxB8jzhqZvvZxmUu/+M0LV+BvgdocFuoTby9nbCb2KlWLeSc74SsewDrgR+GGN5gXDJBuCzwBIzewEYSbwOn5EO4FhgTqmdmdlCd3+NcMlwHeHpqQKwKKugKh27I+QxK4e1M4CM81Xu3HAlGbQvPRosIiKpqWciIiKpqZiIiEhqKiYiIpKaiomIiKSmYiIiIqmpmIhUiZmdYWa7zOzMxLpjzOxFM7uozP47kvtW+JorzGzuAOOYEgfwE8mMiolIlbj708D1wANmNsbM6oEfEN63WZ9vdCKDS2/Ai1SRu3/PzCYT3rx+Efgnh89x8SZxxNslhEEL2wgvtXXEFz4BJpvZpYQ5JzYAc939gJm9C/g2MA4oAkvdPdM3vkVK1DMRqb7ZwHsIQ+1fHodv78tZhBFcz3H30whDg9yQ2H4cYRTmicD7gatjr2ctYfylSYRxx+aa2dlV/Z+I9JOKiUj1GaGHMZowsVSf4mjL84FZZvYtQhEamdhllbvvcfduwlwUFxJGgT2ZMKdHJ/Arwix6p1fzPyLSX7rMJVJFcaC/HwFfIoyRdL+Zne7uf+3jMxcRLlctAh4kjJ00I7HLwcS/6wgjzhaBXe4+MfF1jgV2ES6XiWRKPRORKjGzIrAGWBcHHryXMGnZmritkgvjZ5YR5lm/hFAsSj5tZk1xdr4rCCO7OrDXzGbE73088Bz96AmJDAYVE5HquRVoBeYk1l0DjKXvm/B3AlPivOZPE27cn5SYRng7sJkwGvOjhLlFuoGPAx1mtpVwY/6mxE17kUxp1GAREUlNPRMREUlNxURERFJTMRERkdRUTEREJDUVExERSU3FREREUlMxERGR1FRMREQktf8A6a884NNyo6YAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -63,12 +63,13 @@
     }
    ],
    "source": [
-    "x = np.linspace(0, 2, 100)\n",
-    "plt.plot(x, x, label='Raf Line')\n",
+    "x = np.linspace(0, 200, 100)\n",
+    "plt.plot(x, x*x, label='Raf Line')\n",
+    "plt.plot(x, x*x*2, label='Raf Line 2 ')\n",
     "\n",
     "plt.xlabel('X label')\n",
     "plt.ylabel('Y label')\n",
-    "#plt.title(\"Simple Plot\")\n",
+    "plt.title(\"Simple Plot\")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
@@ -82,33 +83,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223928c148>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223c17a2c8>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "import seaborn as sns; sns.set()\n",
-    "sns.lineplot(x, list(range(len(x))))"
+    "sns.lineplot(x, 2*x)"
    ]
   },
   {
@@ -121,27 +123,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x2223a38f3c8>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -149,32 +143,40 @@
     "x = [1,2,3,4,5]\n",
     "y = [4,2,5,6,3]\n",
     "\n",
-    "plt.scatter(x,y)"
+    "plt.scatter(x,y, label='Points1')\n",
+    "plt.scatter(y,x, label='Points2')\n",
+    "plt.xlabel('X label')\n",
+    "plt.ylabel('Y label')\n",
+    "plt.title(\"Simple Plot\")\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x222380d9408>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223c3db788>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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iybDYiYgkw2InIpIMi52ISDIsdiIiybDYiYgkw2InIpIMi52ISDIsdiIiybDYiYgkw2InIpIMi52ISDIsdiIiybDYiYgk41Sx79y5E4mJiTAajdizZ8+Q8YaGBqSkpCA+Ph7Z2dkYGBhwe1AiInKOw2KvqanBl19+iaNHj+LQoUPYt28frl+/PuiajIwM5OTk4OTJkxBCoLy83GOBiYhGMp1OC6HTosXSDaHTQqdz/8KJwz8xIiICe/fuhV6vR1tbG2w2GwICApTx27dvo7e3F2FhYQCAlJQUVFZWuj0oEdFIp9NpYem2IqukCu98cApZJVWwdFvdXu5O/Wl+fn4oKiqC0WhEZGQkJkyYoIy1tLTAYDAoxwaDAc3NzW4NSUQkgwEAeaU1aLH0AABaLD3IK62Buxev9c5emJ6ejlWrViEtLQ3l5eVYtGgRAMBut0Oj0SjXCSEGHTtjuE1ZnWEwjH2u13sKc7mGuZynxkwAcznSYulWSv2bcz2ARuPWjA6L/dq1a+jv78fUqVPh7++PuLg4XLlyRRkPDg5Ga2urcnz37l0EBQW5FKKtrQt2u3DpNQ/JuAO5JzGXa9SYS42ZAOZyik6LoED/QeUeFOgPCOFSRq1WM+wNscOlmFu3bmHTpk3o7+9Hf38/Tp8+jRkzZijjEydOxOjRo1FbWwsAOHLkCKKiopwOSET0/4UegGlFxIMyx4NSN62IcH7pxIX3GVZ0dDTq6uqwYMEC6HQ6xMXFwWg0YtWqVUhPT8f06dNRUFCATZs2oaurC9OmTcPy5cvdHJOIaOSz2ewIDPDDlrWvARoNIAT0/zvvThohxLOtgbgRl2K8h7lco8ZcaswEMJernifXcy/FEBHRyMJiJyKSDIudiEgyLHYiIsmw2ImIJMNiJyKSDIudiEgyLHYiIsmw2ImIJMNiJyKSDIudiEgyLHYiIsmw2ImIJMNiJyKSDIudiEgyTm3cUVxcjBMnTgB4sPHGhg0bhowfOnQI48aNAwC89dZbWLp0qZujEhGRMxwWe3V1Nb744guYzWZoNBqsXLkSp06dQmxsrHJNfX09duzYgfDwcI+GJSIixxwWu8FgQGZmJkaNGgUAmDx5MpqamgZdU19fj927d+P27dv4/ve/j40bN2L06NGeSUxERMNyuMY+ZcoUhIWFAQAaGxtx4sQJREdHK+P379/H1KlTkZGRAbPZjI6ODpSUlHguMRERDcvpPU+vXr2K1atX47333sMbb7zx1OsuX74Mk8mEiooKt4UkIiLnOfXD09raWqSnp8NkMsFoNA4aa2pqQnV1NRYuXAgAEEJAr3fqj1VwM2vvYS7XqDGXGjMBzOUqn25mfefOHaxbtw4FBQVDSh0AxowZg+3bt+PmzZsQQmD//v2DfrBKRETe5fDW+pNPPkFfXx+2bt2qnFu8eDHOnDmD9PR0TJ8+Hbm5uVizZg2sViteffVVvP322x4NTURET+f0GrsncSnGe5jLNWrMpcZMAHO5yqdLMURENLKw2ImIJMNiJyKSDIudiEgyLHYiIsmw2ImIJMNiJyKSDIudiEgyLHYiIsmw2ImIJMNiJyKSDIudiEgyLHYiIsmw2ImIJMNiJyKSDIudiEgyThV7cXExjEYjjEYj8vPzh4w3NDQgJSUF8fHxyM7OxsDAgNuDEhGRcxwWe3V1Nb744guYzWZUVFTgX//6F06dOjXomoyMDOTk5ODkyZMQQqC8vNxjgenZ6HRaCJ0WLZZuCJ0WOh2/WSOSlcOvboPBgMzMTIwaNQp+fn6YPHkympqalPHbt2+jt7cXYWFhAICUlBRUVlZ6LjG5TKfTwtJtRVZJFd754BSySqpg6bay3Ikk5fAre8qUKUppNzY24sSJE4iOjlbGW1paYDAYlGODwYDm5mYPRKVnNQAgr7QGLZYeAECLpQd5pTXgghmRnPTOXnj16lWsXr0aGzZswKRJk5TzdrsdGo1GORZCDDp2xnCbsjrDYBj7XK/3FLXkarF0K6X+zbkeQKNRTUZAPfP1ODXmUmMmgLlc5alcThV7bW0t0tPTYTKZYDQaB40FBwejtbVVOb579y6CgoJcCtHW1gW7Xbj0modk3IHc7XRaBAX6Dyr3oEB/QAjVZFTVfD1CjbnUmAlgLlc9Ty6tVjPsDbHDpZg7d+5g3bp1KCgoGFLqADBx4kSMHj0atbW1AIAjR44gKirqmcKSZ+gBmFZEPChzPCh104oI579dI6IRxeHX9ieffIK+vj5s3bpVObd48WKcOXMG6enpmD59OgoKCrBp0yZ0dXVh2rRpWL58uUdDk2tsNjsCA/ywZe1rgEYDCAH9/84TkXw0QohnWwNxIy7FeA9zuUaNudSYCWAuV/l0KYaIiEYWFjsRkWRY7EREkmGxExFJRhVPvGm1rv2DJne/3lOYyzXM5Tw1ZgKYy1XPmsvR61TxVAwREbkPl2KIiCTDYicikgyLnYhIMix2IiLJsNiJiCTDYicikgyLnYhIMix2IiLJsNiJiCQzooq9q6sL8+bNw61bt4aMNTQ0ICUlBfHx8cjOzsbAgPe2ah4uV3FxMWJiYpCcnIzk5GTs37/fK5mKi4thNBphNBqRn58/ZNwX8+Uok6/maufOnUhMTITRaMSePXuGjPvqs+Uol6/m66Ft27YhMzNzyPmmpiYsXboUCQkJWLNmDe7fv6+KXGazGa+//royX4WFhV7Js2zZMhiNRuV9L168OGi8uroaSUlJiIuLc18mMUL885//FPPmzRPTpk0TN2/eHDJuNBrFP/7xDyGEEFlZWWL//v2qyLV69Wrx97//3StZHqqqqhKLFi0SfX19or+/Xyxfvlz8+c9/HnSNt+fLmUy+mKu//e1vYvHixcJqtYqenh4RExMjrl27NugaX3y2nMnli/l6qLq6WvzgBz8QGzduHDL27rvvimPHjgkhhCguLhb5+fmqyJWbmyv+9Kc/eS2LEELY7Xbx+uuvC6vV+sTxnp4eER0dLf7zn/8Iq9UqUlNTxV//+tfnft8Rc8deXl6OzZs3P3Gj7Nu3b6O3txdhYWEAgJSUFFRWVvo8FwDU19dj9+7dSEpKQm5uLvr6+jyeyWAwIDMzE6NGjYKfnx8mT56MpqYmZdwX8+UoE+CbuYqIiMDevXuh1+vR1tYGm82GgIAAZdxXny1HuQDfzBcA3Lt3D4WFhUhLSxsyZrVacf78ecTHxwPw7tficLkA4NKlSzCbzUhKSsL777+P9vZ2j2e6fv06ACA1NRXz589HWVnZoPG6ujq88sorePnll6HX65GUlOSW+Roxxf7hhx9i5syZTxxraWmBwWBQjg0GA5qbm32e6/79+5g6dSoyMjJgNpvR0dGBkpISj2eaMmWKUkSNjY04ceIEoqOjlXFfzJejTL6aKwDw8/NDUVERjEYjIiMjMWHCBGXMl5+t4XL5cr5ycnKwfv16jBs3bsiYxWLBiy++CL3+wX8c6835Gi7Xwyxr167F0aNHERISgtzcXI9n6ujoQGRkJHbt2oXS0lIcOHAAVVVVyvjjn6+goCC3zNeIKfbh2O12aDTf/DeWQohBx77ywgsv4Pe//z0mT54MvV6P1NRUnD171mvvf/XqVaSmpmLDhg2YNGmSct6X8/W0TL6eq/T0dJw7dw537txBeXm5ct7Xn62n5fLVfB08eBAhISGIjIx84viT5scb8+UoFwDs2rULM2bMgEajwcqVK/H55597PFd4eDjy8/MxduxYjB8/HgsXLhz09+Spz5cUxR4cHIzW1lbl+O7du09dGvGmpqYmfPrpp8qxEEK5k/G02tparFixAr/4xS/wxhtvDBrz1XwNl8lXc3Xt2jU0NDQAAPz9/REXF4crV64o476aK0e5fDVfx48fR1VVFZKTk1FUVIQzZ84gLy9PGR8/fjw6Ozths9kAAK2trV6ZL0e5Ojs7UVpaqhwLIaDT6Tye68KFCzh37tyg93307+nxz5e75kuKYp84cSJGjx6N2tpaAMCRI0cQFRXl41TAmDFjsH37dty8eRNCCOzfvx+xsbEef987d+5g3bp1KCgogNFoHDLui/lylMlXc3Xr1i1s2rQJ/f396O/vx+nTpzFjxgxl3FefLUe5fDVfe/bswbFjx3DkyBGkp6dj9uzZMJlMyrifnx9mzpyJ48ePAwAqKiq8Ml+OcgUEBODjjz9WnkgpKyvzynx1dnYiPz8ffX196OrqgtlsHvS+oaGh+Oqrr3Djxg3YbDYcO3bMPfP13D9+9bKYmBjl6ZOVK1eKuro6IYQQDQ0N4s033xTx8fHi5z//uejr61NFrsrKSmE0GkVcXJzIzMz0Sq5f//rXIiwsTMyfP1/59Yc//MGn8+VMJl/MlRBCFBUViblz54p58+aJoqIiIYQ6PluOcvlqvh46dOiQ8vSJyWQSf/nLX4QQQty6dUv85Cc/EXPnzhWpqani3r17qsh1/vx5sWDBApGQkCDS0tJER0eHV/IUFhaKhIQEERcXJ0pLS4UQQsyfP1/897//FUI8eJInKSlJxMXFiQ8//FDY7fbnfk/uoEREJBkplmKIiOgbLHYiIsmw2ImIJMNiJyKSDIudiEgyLHYiIsmw2ImIJMNiJyKSzP8B2RXr8SKHxScAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -193,38 +195,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6 6 3 7 5 7 1 9 1 9 8 3 2 5 7 2 1 6 2 5 5 3 8 9 0 5 3 7 9 1 3 2 8 2 8 4 4\n",
+      " 6 1 2 0 8 1 3 8 8 2 2 0 4 2 6 3 3 3 5 3 0 4 9 9 3 1 4 8 6 3 2 5 5 4 7 3 3\n",
+      " 3 3 8 1 6 0 1 0 8 1 0 4 5 5 1 4 7 0 4 4 6 3 4 8 6 4]\n"
+     ]
+    }
+   ],
    "source": [
-    "x = np.random.randint(0,10,100)"
+    "x = np.random.randint(0,10,100)\n",
+    "print(x)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 9., 12., 14.,  9.,  9.,  7.,  7., 14., 14.,  5.]),\n",
+       "(array([ 8., 11., 10., 17., 12., 10.,  9.,  6., 11.,  6.]),\n",
        " array([0. , 0.9, 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, 9. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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a0lmx86XM3N50LUeIFbT/J/tZ4MPAmSzT008RcSpwGfAu2hPC/YDnzBu2k/aKYbO6XdirSBFxFrAd+HJm3t10PQ3bBJwXEU/RXk//9yLitoZratIrwCOZOZ6Zu4Efs0z/FwucD2zPzF9m5h5gK7Ch0YoGZCmdZnFhr46IOB64F9iUmY82XU/TMvPc2ccRcSmwITO/2FxFjXsQuDsi3g5MAhfQPl6Wo58BN0fEWtqnWTbyFkvILnVLZmbuwl5zXA2sAW6NiKc6f/6w6aJ0ZMjMx4GbaS81/QzwIvCdRotqSGZuA74PPAk8TfsD0G80WtSAuNCWJBVgyczMJUlvzTCXpAIY5pJUAMNckgpgmEtSAQxzSSqAYS5JBTDMJakA/wvLrXHmcatJEQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -234,27 +249,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a4ca088>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223c41b8c8>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -264,27 +281,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a565888>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223be947c8>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -294,27 +313,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a565b48>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223be40c08>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -324,27 +345,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a62a188>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223bc81288>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -361,27 +384,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a6c0188>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223c059fc8>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -392,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -411,18 +436,20 @@
        "<BarContainer object of 4 artists>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -432,27 +459,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a6eb688>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223c0ea388>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -469,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -478,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -513,152 +542,520 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>16.99</td>\n",
-       "      <td>1.01</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>10.34</td>\n",
-       "      <td>1.66</td>\n",
+       "      <td>239</td>\n",
+       "      <td>29.03</td>\n",
+       "      <td>5.92</td>\n",
        "      <td>Male</td>\n",
        "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
+       "      <td>Sat</td>\n",
        "      <td>Dinner</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <td>240</td>\n",
+       "      <td>27.18</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
        "      <td>2</td>\n",
-       "      <td>21.01</td>\n",
-       "      <td>3.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>241</td>\n",
+       "      <td>22.67</td>\n",
+       "      <td>2.00</td>\n",
        "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sat</td>\n",
        "      <td>Dinner</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>23.68</td>\n",
-       "      <td>3.31</td>\n",
+       "      <td>242</td>\n",
+       "      <td>17.82</td>\n",
+       "      <td>1.75</td>\n",
        "      <td>Male</td>\n",
        "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
+       "      <td>Sat</td>\n",
        "      <td>Dinner</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>24.59</td>\n",
-       "      <td>3.61</td>\n",
+       "      <td>243</td>\n",
+       "      <td>18.78</td>\n",
+       "      <td>3.00</td>\n",
        "      <td>Female</td>\n",
        "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
+       "      <td>Thur</td>\n",
        "      <td>Dinner</td>\n",
-       "      <td>4</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   total_bill   tip     sex smoker  day    time  size\n",
-       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
-       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
-       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
-       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
-       "4       24.59  3.61  Female     No  Sun  Dinner     4"
+       "     total_bill   tip     sex smoker   day    time  size\n",
+       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
+       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
+       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
+       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
+       "243       18.78  3.00  Female     No  Thur  Dinner     2"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tips.head(5)"
+    "tips.tail(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x22238a69ec8>"
+       "<seaborn.axisgrid.FacetGrid at 0x2223c01f308>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.catplot(x=\"sex\", y=\"total_bill\", data=tips)"
+    "sns.catplot(x=\"day\", y=\"total_bill\", data=tips)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 70,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>77</td>\n",
+       "      <td>27.20</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>78</td>\n",
+       "      <td>22.76</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>79</td>\n",
+       "      <td>17.29</td>\n",
+       "      <td>2.71</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>80</td>\n",
+       "      <td>19.44</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>81</td>\n",
+       "      <td>16.66</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>202</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>203</td>\n",
+       "      <td>16.40</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>204</td>\n",
+       "      <td>20.53</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>205</td>\n",
+       "      <td>16.47</td>\n",
+       "      <td>3.23</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>243</td>\n",
+       "      <td>18.78</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     total_bill   tip     sex smoker   day    time  size\n",
+       "77        27.20  4.00    Male     No  Thur   Lunch     4\n",
+       "78        22.76  3.00    Male     No  Thur   Lunch     2\n",
+       "79        17.29  2.71    Male     No  Thur   Lunch     2\n",
+       "80        19.44  3.00    Male    Yes  Thur   Lunch     2\n",
+       "81        16.66  3.40    Male     No  Thur   Lunch     2\n",
+       "..          ...   ...     ...    ...   ...     ...   ...\n",
+       "202       13.00  2.00  Female    Yes  Thur   Lunch     2\n",
+       "203       16.40  2.50  Female    Yes  Thur   Lunch     2\n",
+       "204       20.53  4.00    Male    Yes  Thur   Lunch     4\n",
+       "205       16.47  3.23  Female    Yes  Thur   Lunch     3\n",
+       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
+       "\n",
+       "[62 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df = tips[tips[\"day\"] == \"Thur\"]"
+    "df = tips[tips[\"day\"] == \"Thur\"]\n",
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:893: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  return n/db/n.sum(), bin_edges\n"
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "      <th>ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>77</td>\n",
+       "      <td>27.20</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>78</td>\n",
+       "      <td>22.76</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7.586667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>79</td>\n",
+       "      <td>17.29</td>\n",
+       "      <td>2.71</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6.380074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>80</td>\n",
+       "      <td>19.44</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6.480000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>81</td>\n",
+       "      <td>16.66</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>202</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>203</td>\n",
+       "      <td>16.40</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6.560000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>204</td>\n",
+       "      <td>20.53</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5.132500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>205</td>\n",
+       "      <td>16.47</td>\n",
+       "      <td>3.23</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Lunch</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5.099071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>243</td>\n",
+       "      <td>18.78</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6.260000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>62 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     total_bill   tip     sex smoker   day    time  size     ratio\n",
+       "77        27.20  4.00    Male     No  Thur   Lunch     4  6.800000\n",
+       "78        22.76  3.00    Male     No  Thur   Lunch     2  7.586667\n",
+       "79        17.29  2.71    Male     No  Thur   Lunch     2  6.380074\n",
+       "80        19.44  3.00    Male    Yes  Thur   Lunch     2  6.480000\n",
+       "81        16.66  3.40    Male     No  Thur   Lunch     2  4.900000\n",
+       "..          ...   ...     ...    ...   ...     ...   ...       ...\n",
+       "202       13.00  2.00  Female    Yes  Thur   Lunch     2  6.500000\n",
+       "203       16.40  2.50  Female    Yes  Thur   Lunch     2  6.560000\n",
+       "204       20.53  4.00    Male    Yes  Thur   Lunch     4  5.132500\n",
+       "205       16.47  3.23  Female    Yes  Thur   Lunch     3  5.099071\n",
+       "243       18.78  3.00  Female     No  Thur  Dinner     2  6.260000\n",
+       "\n",
+       "[62 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"ratio\"] = df[\"total_bill\"]/df[\"tip\"]\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a760fc8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223dcf6888>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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djhBnhHTpiBlzhAfxt9XjWbd+XvTdj3Zybfxu6ccXuUMSvpixYONhDGy8567LdCjT5iwsApB+fJFTJOGLmbFt8hsOMlRUjmP4xiLzien3J5dYkJk6IodIwhcz4u1qwTXUR2/1ykyHMiPvLbEgLXyROyThixnJbzhIwulioHxxpkOZsWTC78a27akrC7EASMIX02YkEgRaTzBQvgR7Hk3FHM9ZVJxcYqFPllgQuUESvpg2X2cjZiJO/zxu3cPwmjrIwK3IHZLwxbQFWk6QcLoYKq7IdCiz4iwoTC6xIFMzRY5I6fu4Uuou4EuAC/im1vrb4/Z/GPgqYABHgU9orbuUUouAB4AyQAN3a6370xi/ONMsi0BbHYOltWA6Mh3NrBhOJ45gvtzUXOSMKVv4Sqlq4H7gcmAdcJ9S6qxR+/OB7wI3aq3PA94G/nZ493eA72itVwO7gC+nNXpxxvm6WnDEIgyUTb3swnzgLCqSFr7IGal06VwDbNVad2qtB4BfAreN2u8CPqu1bhjefhtYpJRyAVcM1wf4EXB7WqIWGRNoPY5lOhgsqc50KGnhLCrG6u/HikYzHYoQcy6VLp0qoGnUdhNw4ciG1roDeBhAKeUD/hr4V6AE6NVax0cdV5OGmEWm2DaB1joGS6rn9eyc0U4O3EorX+SAVD61JjB6orIBnHJvOKVUAcnE/5bW+sfDXUHjJzhP655yoVDedKpnRGlpMNMhnGR3DhLM855SPlHZVGJ2jIR37NvD2deNMzJIrKIW76h9brcDf9CTPM6yOdBpcajb4kiPRW/UZigGpgEldQcoKy1g1eJCzl1ewuKKfGDmr2HYHiI6/Lgz5aupoAdwDvXh97vxniaWbPo9TyTb4wOJMR1mG18qCb8e2DRquwJoHF1BKVUJPAVsBf58uLgVKFBKObTWCaBy/HFT6ejox7Ky96KY0tIgbW3ZM4d7MBI/ZWXMGa+WGY0TDo9dVrmgMbl6Zm9+OfFR+6LRBN0dYV5qsXmlBfpiyVZBVQCK3FDhgYQNQ7bN24faeH53PQBFQQ9XX1DL+ctDVJUEph2jYzBKX19k2seNZttODLeb/qZWBgej9E3w+8y23/N42R4fSIzpkEp8pmlM2lBOJeE/A/ytUqoUGABuBe4b2amUcgCPAg9prf+/kXKtdUwptR24E3gQuAfYksLjiSzl62gk6s8n7n+vlWHbNi8f7uWJPTaRBKwphEsrDJbng9cx9kbmoQsUiUAB7T1D6BPd7NzfysPPH+ZX2w6xdnmIGy5ezKraM3s/3PeWWJAuHbHwTZnwtdYNSqkvAtsAN/B9rfUOpdQTwFeAWmA94FRKjQzm7tJafxr4DPBjpdSXgBPAR+fiSYgzwErg62ymr3r5yaLBuM1rrdB+tJNVBXDLUoNynzHJSZJKCnyUnOvjsnMrcXpdPPzsAX63q56v//QNzl5azJ1XraCm7Mx15zmLigkfOohtTavHUYh5J6WRN631gyRb6aPLbhj+cRenme2jtT4OvG8W8Yks4e1uw7TiDIaqAOiO2LzYDDEL7tgQ4iJXJ4YxdbIfryjo5YOXLeW6Cxfx3O4GHn3pGH/zwx1cua6a265cjt8794PDzsIi7HicWGcnjmDRnD+eEJkiV9qKlPg7GrENg6HiClqHbLY1Jkfkr6qCjUuCM0r2o3lcDq6/cBFf/6NLuHp9Dc+/2cCXvv8quw/M/W0IR26GEm5omKKmEPObJHyREl9HI+GCUjriLl5sBr8TNldDoWd2iX68PJ+Lu69dxZfuuYA8n4t//fUe/vOJfUSiibQ+zmgjSyxEGqc1p0CIeWdhTKYWc8qMRfH0dnJs2QVsbwaPA66oBJ9zesneYQADPWPKwvYQjsFTL3pakQ9fvV3xyI5GHtvVxKETnXzm/ctYXOp/Ly4rPX8EkkssBKWFLxY8SfhiSt7uVsKmi8edyZudXFEx/WQPYEUjdLy1d0xZNOiZdGrlVS6oOcvgpwcjfO2hfdyxzGBDafKxS84767THTZezqFha+GLBky4dMSVPdytPlF/GoOXg8goIutPbjTOVlQUGn19rUBuAnx6y+c0xCyvNNy1xFhUR6+ggMTSU1vMKkU0k4Ysp6SEvBwO1nBsyCHnPbLIfEXQb/PFZBpdXwPNN8CNtE4mlbxqlsyg5cBttqE/bOYXINpLwxaS6BxNsD6xiidXNyvzMxuIwDT6y1OSWJQbvdsHXHz1Kfyw9Lf2RNXUidXVpOZ8Q2UgSvjgty7bZ1ZrAn4hwUXBg1lMv02VTpcG9yuBER5hvv2vTE5190jf9AUyfj0i9JHyxcMmgrTitAz3QZbn4SNuLsPSy09Zz5geJM/X6+L2ml0TV2HqDbgeJ4PRn25xVBX9yboBvPbKXb+138IebKigKzO7t7KyqZqjuBAORsWsI2Z2DDI4r87icOKW5JOYZSfhiQr1DMd7tguXRVhYbfdS7Tr8qZcwy2Ln7+JTnzKuJ0l/fNKbM63Weskhbqi55/0VcVmazvTnON5+p56oq8M9g9tCICm8RvgNvsXNvExjvZfOJFqDbuKYcp0c+PmJ+kTaKOIVt27yquzCxua75JcKF5ZkO6bRCXoMrKyGaSA7mhuMz796JF5dhxqI4ervTGKEQ2UMSvjhFQ9sAjV1hzvMPURDtI1xYmumQJlXkMdhUCUPxZNKPJmaW9OPFZQC42pumqCnE/CQJX4xhWTav6zbyfU7OiSavPI0UZHfCByjxJqds9sfgpRZIzOA+CrHiMmwMnB3NcxChEJknCV+McbC+h56BKBuWFxLobSPh8hDzZ/ddgEaU+Qw2lkF7GHa0JbumpsXpIlEYwtUuCV8sTJLwxUnReIK3DrVTXuRjUYkPb3cb4YISyJLpmKlYlGewthjqB2BP5/SPj5VU4JIWvligJOGLk/Yf7yYcTbBhdSlmLIJroGdedOeMt6oAlgVB98CJ/um18mOhCpw9nRjR2d06UYhsJAlfABCNJdh7rJOasjxKCny42hoxIOsHbCdiGAbnl0CJF3a1QVck9aQfK6kAwNnRMlfhCZExkvAFAPtPdBONWZy3PASAq6UBG4jkl2Q2sBkyDYNLysBtwsstqc/ciYeSCd/VITN1xMIjCV+Mad2HCrwAuFobiAUKsFzuDEc3c16nwaXlyemaO1McxE0EC7HcXhm4FQuSJHxxSuse28bdlrzD1XxX7DU4LwSNg3CwN4UDDCM5cCsJXyxAkvBzXDxhse9YF9WlgZOte0dfN2Z4kEjB/OzOGW9FPlT54e0O6EyhPz9WWoWzvQnSdEctIbJFSglfKXWXUmqvUuqgUuqzk9T7iVLq3lHbH1dKNSml3hz+d38aYhZpdKihh0gswTlLi0+WuVqHL7jKD2UqrLQyDIONpeB1wI7WqS/KipVVY8ZjOLvaz1CEQpwZU67+pJSqBu4HNgAR4GWl1Dat9d5RdaqA7wGbga2jDr8A+Aut9X+nNWqRFpZls/doFyUFXsqKfCfLXa0N2KZJJFiUwejSy+0w2Fhm80ITvN0J50/y5SVWWgUkX4d4KHvXERJiulJp4V8DbNVad2qtB4BfAreNq3M38BvgoXHlG4GPK6X2KKUeUEotnAyyAJxo6aN/KMY5y4rHrHXvbm0gXlwOpiOD0aVfuc9gRT4c6oWWwdO38uOFJVguN642ucetWFhSSfhVwOg5ak1AzegKWut/1Fp/f4Jjm4CvAWuBOuBbM4xTpJlt27x7tJN8v4uasrzRO5IzdEoqMxfcHDq3GIIu2NUO8dN17ZgmsZJK3MNdW0IsFKks6G0Coz8ZBpDSzUS11reM/KyU+gfg8HSCC4Xypq6UYaWl2bPOjN05SDDPe0r5RGWNbf109Ea4cn0NBcH3unOMrnbMaBi7shqvN7X13h0OM6W6p6uX6uOMZxgzO/bSaounjiXY12uyseK9bzFutxPX8GtlVC3C9fZrBP3JaanjX0O/30NpsX9Gcc+FbHofno7EOHuzjS+VT0s9sGnUdgUw5XddpVQB8Emt9TeGiwxgWne66Ojox5rBqodnSmlpkLa2vkyHcdJgJH7KjTomunkHwOv7W3C7TKpDvjH7vUePADBUVE64P5bS4yYSVko3MXFOUG82N0CxbWZ0bL4Jy/Nhf6dFldc6eWN2RzROePi1iBeVUxSLMlRXh2/x4lNew8HBCG2J7JjFk23vw4lIjLOXSnymaUzaUE6lS+cZYLNSqlQp5QduBZ5M4bh+4AtKqYuGtz8HPJzCcWKO9Q/FqGvpZ2VNIU7H2LeAu7UB2+EkXjT/5+BP5txi8DmSSy9YE1yQdXLgVvrxxQIyZcLXWjcAXwS2AW8CD2qtdyilnlBKXTDJcQngDuC7Sql9JGf5fCE9YYvZ0Ce6wAC1qPCUfSf77xfYgO14LtNgfQn0xuBgz6n740UlWE6XJHyxoKTUAaq1fhB4cFzZDRPUu3fc9nZg/SziE2kWi1scrOthUVkeeT7X2J22hautkaHV6zIT3BlWFTCo8tu82wW1eTaB0TtNB/GSSlytDUQzFaAQaSZX2uaYo029ROMWq5ecOkPW0d2BGYsQLauZ4MiFaV0oOSPh7Y5T90XLa5IzdbKkr16I2ZKEn2MO1HVTmOemrNB3yr6RaYixsuozHVbGBFwGqwuhbgCausYOzMbKazASccw2WTlTLAyS8HNIR0+Yzt4Iq2oLx1xoNcLV2oDldBEvWhhr6KRqdQEEnLDjYNeYAdxoeS0AjsYTmQpNiLSShJ9DDtR14zANllXlT7jf1dqQnJ2ywAdsx3OYBucWQ9dAjCMN7y2pmcgvIuEL4Gg8nsHohEgfSfg5Iha3ONrUy5LKIG7XBAndSg7Y5lJ3zmg1ASgJutl9sJ14Yvi6QsMgVl6D2SQtfLEwSMLPEUebeoknbFbVnjoVE8DZ1YYZj+VswjcMg40rChmKxNl7rOtkebS8FkdHK0ZkKIPRCZEekvBzxMhgbUnBqcsswHtLIo9ccJSLygu91Jbl8e6RToYiySt4Y8P9+O6W+kyGJkRaSMLPAVMN1sLwgK3LTbwwtwZsx1u/qpS4ZfH24eQ8zWh58huPq6Uuk2EJkRaS8HPAVIO1kJySmRywze23REGem1W1hRyo66anP4Lt8ZEIleNulha+mP9y+9OdA6KxxOSDtQCJBK72JmKludl/P955K0I4TZM3DiTveJWoXoy7+QTYKS0SK0TWkoS/wB2s6550sBbA2dWKkYgTK5eED+B1OzlnWTF1rf20dA6SqFmKGRmSWx6KeU8S/gL37tGOSQdr4b0rbKM5OkNnImuWFOHzOHjzYDvxmqUAuBuPZTYoIWZJEv4C1tETpq1raNLBWhgesHV7SRQsjJuWp4PTYXLushAtXUPURT0k/HmS8MW8Jwl/ATtQ143TMflgLQxfYVtWnbyFlDhpZW0Bfq+T1/a2EKlcgrtJrrgV85sk/AVq5MraFTWFpx+sBUjEcbU3S3fOBBymydrlIVo6BzlUvAJnXzdmX3emwxJixiThL1AjV9aevWzybhpXRwuGlcjZK2ynsqK6gPyAmx2RQmzAI618MY9Jwl+gDg5fWVs+xY22XTm4JPJ0mKbBBWvKaR+0OJC/TPrxxbwmCX8B6ugJ0zHFlbUjXK31JLx+EsHTT9vMdWpREfl+F9tLzsclK2eKeUwS/gKUypW1I9wyYDsl0zRYu6KEdjPA4agXc7A/0yEJMSOS8BeYKZdBHi0ew9nRKt05KVhSGaTQa/Bi8Xm46g5nOhwhZkQS/gJzchnkmqm7aFztTRi2JQk/BaZhcN6qcjrchRw/IVfcivlJEv4CMzJYW1J4+itrR7jkCttpWVSZT8geZFekEMuypz5AiCzjTKWSUuou4EuAC/im1vrbp6n3E2Cr1vpHw9uLgAeAMkADd2utpQN0jpxo6aOjN8KFa8qmHKyFZP99wp+HFZi6r18kb5Kyodji6a48dr5+mKsvX53pkISYlilb+EqpauB+4HJgHXCfUuqscXWqlFKPAreNO/w7wHe01quBXcCX0xK1mNDLe5pTHqwFucJ2JqpX1lIa6eKpXY3SyhfzTipdOteQbLV3aq0HgF9yamK/G/gN8NBIgVLKBVwxXB/gR8Dtsw1YTGwoEmfX/laWVKQwWItMvtQAACAASURBVAsY0QjOrjbpzpmmRFEplwweoC0MO/a1ZDocIaYllS6dKqBp1HYTcOHoClrrfwRQSl0+qrgE6NVax0cdVzOd4EKhvOlUz4jS0mCmQwDgqVePEYklWKfKCOaN7b8fvw3gqGvEsG2ci5ZOuD9mx0h4U+rxw+Ew8aZQ93T1Ujl2IoYx82Mn4nY7cU3wWsDY17CkIkhpfw+Pv3qcG65YgcPM/DekbHkfTkZinL3ZxpfKp8UERn93NYBU7gQx/jhSPO6kjo7+rP7aXFoapK2tL9NhAPDY9iNUhvz43SZ9/eGT5cE875jtEYHjRwHoDZZiTbDfjMYJh+OnlE8kkbBSquucoJ7X60z5ccazbWZ87EQc0TjhCV6L8a9hrHo5l730Co+4CnjihUNcfHZF2mKYiWx6H56OxDh7qcRnmsakDeVUunTqgcpR2xVAYwrHtQIFSqmR/oXKFI8T03S8uY9jzX1cdm5lSoO1kOy/T+QVYAWyu0WTjSKLVqAG66j0JPjtS8eyulEixGipJPxngM1KqVKllB+4FXhyqoO01jFgO3DncNE9wJaZBipO7/k3G3A5TTauKU/5GHdrA9GyqjmMauGyvX48y1awqX8/zZ2D0pcv5o0pE77WugH4IrANeBN4UGu9Qyn1hFLqgikO/wzJWT17gU0kp3aKNBqKxHllbwsXri7Dn2J/thEJ4+xulwuuxjENMCNDp/yL9fWdUuZdvYalx1+npsjNo9sPY/R14xjoGfPPmYhk+ikJMUZKGUJr/SDw4LiyGyaod++47ePA+2YenpjKa/taiEQTXLku9eTtaq0HIFpeO1dhzUt2PM5AfdMp5YkJxhmM6koM4IrECR7sreCZp3azoXRsd1rogvMh4JnLkIWYFrnSdh6zbZutrzdQU5rH8urUL55yN5/AxiBWNq1JU2IUR2kZpt/PinZNpR+errexbOnLF9lNEv48drC+h/q2fjZvqE55sBbA3VxHvLgU2zP18gtiYoZh4K6uIdbYwHVVFm1h2C1L7IgsJwl/Htv6Rj0+j5OLz5rGtEDbxt1cR7RCunNmy1O7CDseZ1W4SVr5Yl6QhD9PdfdHeF23sWltJR731FfWjnD0dCQHIisWzWF0ucFdUYnhchGrO871NQZtYXhdWvkii0nCn6eef7ORhGVz1fnTm2njbq4DZMA2HQyHA3dNLZG6Os4utKkJwFN1NnGZly+ylCT8eSiesHjuzQbOWVY85T1rx3M3n8Bye4gXl85RdLnFu2gxdjRCorWZGxcZdEbgZZmWL7KUJPx5aPfBdnr6o1y9fvqzbFwtdcTKa8CQX306uKuqweEkcuI4qwpgZT48U28TTkgrX2Qf+dTPQ8++Xk9JgZe1y0LTOs6IRXG1t0h3ThoZTiee6moidSfAtrlxsUF/HJ6XRUREFpKEP8/Ut/ZzoK6bq9ZXY05zlUZXSx2GbRGtlAHbdPIsWYo1NESspZlFeQZri+G5RpveoVimQxNiDEn488zWN+pxOU02rZ3+OjjuxmPYGEQrFs9BZLnLU12D4XIRPnoEgBsWGcQseHRnc4YjE2IsSfjzyGA4zivvtnDRmnLyfK5pH+9pPEastEIuuEozw+nEs2gxkRPHsRNxynwGG8tg65422ruHMh2eECdJwp9HXnirkUgsweYNM1gSIRHH1VxHtHJJ2uMS4F26DDsWI1KfXKfo+hoDw4BHXjya4ciEeI8k/HkinrD43a461iwuYnHF9Newd7U1YsZjRKuWpD84gau8AtPnO9mtU+gxuPa8Ml55p5kTLdl7Uw2RWyThzxM797XS1Rfh+gtnNuDqaTwGIAl/jhimiWfJUqIN9ViR5N2xbrqggoDPxYO/O4AtSy6ILCAJfx6wbZsnd5ygqiTAucuKZ3QOd+MxYoUlWP7sv0/wfOVbvgIsi/CRZCs/4HHykSuXcaC+h537WzMcnRCS8OeFfce7qGvt5/oLa6e1KuZJtoW76bi07ueYs6gYZ6iEoUPvteivWFvFovI8Htp2iEgskeEIRa6ThD8PPPHqcQoC7umtijmKs70ZMxKWhH8G+FasJNHdTbw9uYqaaRrcdc0qOnsjPP7KsYzGJoQk/Cx3uLGHvce6uP7CRbicM/t1eeoOAxCpWZbO0MQEPEuWgsPJ0KGDJ8tW1RZyydkVbHn1BPVt/RmMTuQ6SfhZ7rGXjpHnc/G+82d+w3FP3SFixWVYeQVpjExMxHS78S5ZQuTYERLh8Mny39u8Ap/HyY+37Jc180XGSMLPYseb+3jrcAfXbqzF607tBuWniMfwNB4jUrsivcGJ0/KtVNjxOD07d54sC/rd/N7mFRxu7OW53Q0ZjE7kMkn4WezxV47h8zjZPINVMUc46o9iJOJEapenLzAxKVdpKc5QCV3bt4+ZjnnJ2RWcvaSIXzx3mFa5AldkQEoJXyl1l1Jqr1LqoFLqsxPsX6eU2qWUOqCU+r5Syjlc/nGlVJNS6s3hf/en+wksVHWt/byu29i8oQa/d4ate8Bx9AC26SBatTSN0Ymp+NRqoi0tDO3fd7LMMAzu/cAaTMPg+4/uJWFZGYxQ5KIpE75Sqhq4H7gcWAfcp5Q6a1y1B4DPaa1XAQbwB8PlFwB/obVeN/zvi+kLfWF7+IUj+DxOrr9wdksZO48dIFqxCNvtSVNkIhXeJUtw5OXR9ezvxpSHCrx87LpVHGro4fFXjmcoOpGrUmnhXwNs1Vp3aq0HgF8Ct43sVEotBnxa61eHi34E3D7880bg40qpPUqpB5RSRekLfeE6VN/Dm4fa+cDFiwh4p79I2ghzaABHSwORRdKdc6YZDieFl1zCwFtvEm0be9HVxWdXcPFZ5fz2xWMcqu/JUIQiF6XSV1AFNI3abgIunGJ/zaif/wl4Gfh74FvA3akGFwpl/1WhpaXTX9dmMrZt88+/eIvCPA+/d/0avJ7Uu3PszkGCee+thOk8+g4ADnXOmPJUxOwYiRS7khwOE28KdU9XL5VjJ2IYMz92IpM9j/HlbrcDf3Dyb03+tVfTuW0b4e1bqf7DPxiz70/v2sCff+M5/u237/DPf3YloQLfrGJP9/twLkiMszfb+FL5tJjA6HlkBmClsl9rfctIoVLqH4DD0wmuo6MfK4tvCF1aGqStLb0LY71zpIN3Dndw1zUr6esdYjpnH4zE6et/bypg0b63sIIF9OSVwKjyVJjROOFwPKW6iYSVUl3nBPW8XmfKjzOebTPjYycyUXwwcYzRaIJEX2TS87ndfoIXX0rLM1vxX3sDzmD+mP2f+fA53P9fr/O1H7zKX921HqdjZnMo5uJ9mG4S4+ylEp9pGpM2lFN5h9UDlaO2K4DGqfYrpQqUUn8+qtwA0vfpXIASlsXPtx6itNDLleuqZ3UuIxbFc+Ig8ZXnJJvCIiOKr38/djRK99ZnT9lXU5bHJ29cw+GGXn7ypJYF1sScSyXhPwNsVkqVKqX8wK3AkyM7tdbHgbBS6rLhoo8BW4B+4AtKqYuGyz8HPJy2yBeg53Y30tA+wJ1Xr5zxVbUj3PWHMeMx4qvOSVN0YibclVUE1p1P99ZnsMKnfsvauLqMD166hBf3NPGL5w5PmfTjFgxE4mP+tXYOnlKW7n9xmVC0IEzZpaO1blBKfRHYBriB72utdyilngC+orXeRbJf/j+UUvnAG8C/aK0TSqk7gO8qpXzAAeCeOXsm81z/UIxHth9hzeIizl9ZMuvz+Y7sxXJ7SdQuB7m3akYV33ATdX//Nbq3baX4Azecsv/mTUvpD8d48rUTBLxObrxkyWnPFYnF2bmvZUxZMM87pitvLmxcU45zGuNJIjul9BvUWj8IPDiu7IZRP7/F2IHckfLtwPpZxpgTfvPiUQYjcT66eeXMVsQczUrgObqf8BIFDgcgCT+TfMuW4z/nXLqe2kLhVVdjescOoBuGwd3XrmIoHOdXzx8hGrO4edPS2b8PhBhHrrTNAkcae9n6Rj1XnV9NTdnsZya5G4/jCA8SXrYmDdGJdAh96GYS/X10b31mwv2mYfCpm9Zw+dpKHn35GD/csl8uzBJpJwk/w+IJix9t2UdhnoePXJGe+fJ+vRvL5SGyWKXlfGL2kq38tXQ+tYXE0MTLKjhMk098YDUfumwJL77dxD8+uJuOnrntqhG5RRJ+hj214wT1bQP8/rWrZrWEwggjFsV76B2GVpyD7XKnIUKRLiU334I1MEDXk0+cto5hGNy8aRl/cNNZHG/t52/+cwc79rXIDB6RFpLwM6ixfYDfvHiMC1Qp568qTcs5vUf2YsaiDK0+Py3nE+njXbKU4IUX0/X0k8Q6Oyete8k5FfztJzZSXuzj337zLv/nwd0cbeo9Q5GKhUoSfobE4hbf++27eN0O7r52VdrO69u/m3h+EdGqxWk7p0ifko/cCrZNxyO/nrJueZGf//2xDXzsekVTxwBf+/Euvv2rtznR0pfVFySK7CXzrDLkV88fpq61nz+5bS0FeelZ2CzR1Ymn7jD9G98Hhvwtz0auklIKN19L19NPUnj1ZrxLJl/F1GGaXHV+NRefVc4zu+rYtruB/Se6cbtMqkIBqkoC1FbYOE1wmAtvVo8zEcGe4PqF2TC8XuKO3FxMUBJ+Buw50sHTO+vYvL6GdStmP+d+xMCLzwMwuFpmwmaz4hs/SO+rL9PywE9Y9L+/jGFO/cfZ53HywcuWcuX6Gn6z/QgnmvtoaB/gWHMfvNOMYUDA68LnceBxOTBNA9Mwkv8f9YdgZCxgZEjAtu3kuijD206nidtp4nKauJ0O3C6TgNdFW/cQ7pAfl9OR5ldjcnY4TMeu3Wk9Z+iC8yEgCV+cAS1dg/z7b9+lpjTA7VelbxVLKxKh/4VthJetIVFQnLbzivRz+P2U3vlRmv/93+h5biuFV1+T+rGmQW1ZHrVledi2Tc9AlKGoRVNbPwPhGEORBAPhOLZtY1k2lg0JywZsDAyG/wOSA8QjU/0Nkjk/nrCIxS3iibFdRk/vrAMg3++ipNBHZchPVUmAylCAqpCfkkIfplw3kPUk4Z9BQ5E4//qrPQB87ta1uF3pay31vrQde3CQ/vMvT9s5xdwJbryI3u3baX/4V+St34CzcPorhxuGQWGeh9o8L5XFs1ttczzLsonFLSKxBAPhGOVFfgaGYnT0RmjtGuSdI528tKf5ZH2Py0FteR6Ly4MsGv5/VUlgxgvCibkhCf8MSVgW//HoXpo7Bvn8nedRVpi+D6htWXT97ilcS5cRq5TB2vnAMAzKfv8ejn/1yzT/8AdU/9nns+rKWtM08LgdeNwO8gNuNq4pJzBuaYWBcIymjkEa2weoa+3nREsfL77dRCSWAMDpMKguzWNJRZC1q8ooDbqpCgXGdDGJM0sS/hlg2TY/2rKfNw+1c/e1q1izJL1dLn07dxBra6P4Q7em9bxibrnLyym9/fdo/elP6Nn27LS6drJBwOtiRXUBK6oLTpZZtk1r1xDHm/s40dLH8ZY+du5r5fk3kwvset0Ollbms6wqn2XD/0/XpAUxNUn4c8y2bX727EFe2tPMhy5bwuYNM78h+USsWJT2X/8CT20t3nXrQbel9fxiYs78IHEWTVqnz3ZiRyZfEdx5ySY8u9+g7RcPwdKVuKomXxY7U7MxDdNgYIrnMiIYcHPO8hDnLA8ByT8C/eEE+452cLy5l2NNfWx57cTJqaVFQQ9LKoIsrsxnSUWQ2vI83MODw4btxFFZRaKp8bSPNxXbtrEGB7EGB7CiUQYKgxihMlzlFZiumd9Rbj6ShD+HLNvmoa2HeGZXPddcUMOHL0//jcS7n/kd8Y4OKu79FHYKsz1EesQsg527J78nbaDTwvJM3XVnXnQjpceO0fitf6Htjj/GnuSY89J0gd50RWIJ3jow88ZEMM+LacDSynyWVuYTT1h09oZp7w7T1hPmQF03uw+2A2AaECrwUVHsoyLPwZVLffin+XhWJELk+DEiDfXEWluwo9GT+3pG1jNyOPBU1xBYu5a89RdA6cJfSlwS/hyJJyx++MQ+Xnm3hc0bavi9dKyCOf4xenrofPxRAuetw7/mrJRbYCK7WIF8ut7/UUKP/ICipx+i88aPwQL/4+10mJQV+Skrei+VD0XitHUP0dY9REvnEO8c7WSPDc/ugZoALAvCigKD5fngcUz8WUr09zHw9luEjx4By8LMy8NTuxhXSQlmIIDpdhNcsYxoNE60vp6hQwfpfPwxOh97lM4Vywlsuor8iy7GcC7M1Lgwn1WG9Q/F+N5v3+Xdo53ccsUybrpkcdqTvW3btPzXj7BiMUpvvzOt5xZnXrRqCT2bbqLw+d9S8MKj9Fz5oZy7U5nP42RReZBF5cn7tsbiFu1t3STCUY429rC9GZ5rsnEYsDRoowoNVAFUBcAODzG4522GDh5InmvFKrwrVuAsDp3y2QusXIk3UHByQfdEXx99O1+jb/tztPzw+3Q+9ltCH7qZ4EUXp3SNxHwiCT/Njjb18p2H36FnIMInPrCaTedVzcnj9Dy3lYE3d1N6x0dxV1ROfYDIeoPnXIijr4vgG9ux3F76Lr0+0yFllMtpUl3s4+ziAI7iPqIJm2N9oHtsdDc8fsLmcSBAjMX9zSwdjLF62Roq1p6FIxBI+XEcwSCFV1/Dijtu5tizL9LxyK9o/sG/07nlcUIfvoW89RuyagbVbEjCT5N4wmLLayd49KWjFAQ8/K/f38DSyvypD5yB8InjtD30M/znrKXwmmvn5DFEBhgGfZdcjxmNEHzjBQzbpvfS62SZjGFuh8GqQlhVaHBjVYzWvQfZe6KLo+4yjgVr2Zu3hMeB6sOwutBiTaHB4rzUl5wwDIO889YROHct/a/vov03v6bpu9/Cu3QZJbfejn/1/L+/hCT8NDjU0MNPntTUt/WzcXUZH7tekeebm9H/aHMTDd/4vzjyglR88tML7itnzjMMeq78IBgGebu3Y/b30H3NreCQjyqAnUgwdPAAA3vewgyHWV9TyxXrQpiFHhoHYX837O+y2dYAzzbYeB2wqsBmdaHB6kIo9Eyd/A3TJLjxQvLWb6D3lZfo+M0j1P/T/8F/zlpKb70dT23tGXimc0PeRbNQ19LHDx7Zw+sH2ijIc/O5j5zL+jmcRRFtaab+//4DADWf/0uc+XPzDUJkmGHSc8UHSeQVkv/KU7i6Wum67k7ixWWZjixj7ESC8JHDDOx5C2tgAFd5BXnvW4+r9L3XpCaQ/HdNtcFQ3OZgD+zrTnb/vN2ZnAJa4bM5P1zPOSrBippCXM7TN5gMh4OCy68geOHFdG99hs4nHuP4332F/IsvJXTzLbhC6VsH60yRhD9Ntm2z/3gXv9tVz1uH23G7HNy8aSnXbazF6567l7N/9xs0/+d/gMNB7f/8K+m3X+gMg/4NVxArLqPw2V9R8tB36L/wauylH8p0ZGeUEQ0z9Npuwq+9jDU0hDMUIv/iy3BVVk7ar+5zGqwNwdqQkZzgMAT7umF/t83Tb7WyZXcLbpfJyuoCVi0qQtUWUlg08eRP0+2m+P03ULDpSjq3PE73s7+jb+dr5F96GUXXvX9efRYl4afAtm3qWvvZpVt59d0W2nvCBP0u7rxGcfGaUvL9c3dnqXh3N+2/+TW921/As3gJVX/8WVwlmZmLLc68yNLVtH30f1C47RHyX36KzgNv4DvvCoZWrV3Q3TzO9mb8+97Av3cng7EoropKgpdejruyatoDqIZhUOGHCj9cVWUQWLuWd9st3j3aia7r4uEXjgDg/vmbyesEqpIXgC2tzKekwHvy8RyBAKW33UHh1dfQ+cRj9L60nZ7tL+A/62wKrriSwNp1WX8hV0rvGKXUXcCXABfwTa31t8ftXwd8H8gHXgD+SGsdV0otAh4AygAN3K217k9j/HPCsmyaOgc53NDDwfpu3j3aSXd/FMOAs5YUc/OmpWxcXUZVZSFtbX1zEkOkoZ6e7S/Qs/157HicomuvJ/SRWzHltoU5xwrk03nTPXiOH6Ts9WcoevZX5L/yFIOrzye84lwILMt0iLOXiONqa8R7/ADeI3txdbRgmybhpaupuPwyPFYsbQ/ldTtYt7KYdSuTXTL9QzEO1HVzon2APQfbeGZX3cnVQvN8LhaV51EVClBZklwZtLIkj7K7P0boQzfT89xWera/QNN3v43p8xFYdz55556Hf81ZOILBtMWcLlMmfKVUNXA/sAGIAC8rpbZprfeOqvYA8Gmt9atKqR8AfwB8F/gO8B2t9c+UUl8Gvgz8VbqfxHTFExZ9gzH6BqP0Dkbp7ovS0jVIc8cgzZ2DtHQNEU9YAAS8TtYsKebcpcWsXR5K+7oftm1jDQ0Sa2sj2tjA0JEjDO3bS7S5CRwOghs2EvrwLbjLy9P6uGL+iSxeSdE1l6CffZXAWy+T9+ZLySmcgSDOysXESquJF5UQLywhXlCcnd8ArASOgT4cfd04ertwtTbgbqnD1daEYSWwDYNo5WJ6Nt2U/BZjmtQUm9B4Ys5CyvO5WL+qlOsvW0ZbWx/xhEV9Wz9Hm/o42tRLQ1s/2/c0EYkmTh7jdTsI5XsJFSylePNqguFuXI3Hcew/gPeNX+C3IuQX5ROsqcRXW4undhGuUAmO/HwcwWDGJluk8o64Btiqte4EUEr9ErgN+Lvh7cWAT2v96nD9HwFfVUp9H7gCuHlU+fOklvAdwIxW1esfivHYy8foH4oRS1jEYhbxhEU0niCWsInFLMLRU69IdZgGxfleVtYWcum5lVQU+6gtD1I66ivdRGaz8l/vKy/T+cSj2LH3Wi+Gy4V/xQpCN95IcN16zGnMJ3Y6TPzesV8pfR4nifj0v2aaZgKCqa3o6XI5CaRQ1+9zY4+r5/E4cbhmdoWw02mm9Lipmig+mDjGVJ6z3+fGcqf3K77L6cCxag3hVWuIRIbwnDhEoL0BV8MxnIffOFnPNkwsjx/b48Hy+LA8XmyXG9t0gGliO5ww8rNpcnKV/DFv5/c2Ym0+SruHxpXbGAkLrETyjipW8mfDssC2MKwEZjSCEY3giEcpDA/hGBrAsK2T57UcLuKl5cTOOo9YSQXR8kXY3uTr6gXMaBiXx8D0p+/3bDod2Kf53Jqmgdt0sKyqgGVV7y0KN3LvgdbOIVq6h+jsDdPdH6GrL8Kxln4GwzY4FsGKU9dXctQncNX34LQ6cVlxXHYCl2njMAwMI/lNwjTAXVKCu7AIpyN5l7OK0KljClPlm1H7J1x7PZWEXwU0jdpu4uQ1aqfdXwOUAL1a6/i48lRUAhQVpZ7sRoSAz94x/bXFZyoUypv5sTddx9KbrktjNFBTWTB1pZSdnXLNFWsyM1Vt6T2Zuw4hU895Wc3o9/fCX/8l6awz8iiTfZ5LSmB5hlcfn0a+qQQOjy9MJeGbnLwBGpD8826lsH98OeOOm8xOYBPJPxKJKeoKIYRIcpBM9jsn2plKwq8nmXxHVACN4/ZXTrC/FShQSjm01onhOqmucRoBXkyxrhBCiPec0rIfkcrIwTPAZqVUqVLKD9wKPDmyU2t9HAgrpS4bLvoYsEVrHQO2AyMre90DbJlB8EIIIdJgyoSvtW4AvghsA94EHtRa71BKPaGUumC42t3AN5RS+4E84F+Gyz8D3KeU2kvyW8KX0v0EhBBCpMaw7QzdQkcIIcQZJStvCSFEjpCEL4QQOUISvhBC5AhJ+EIIkSOycLGNqRdryzSl1N8AdwxvPq61/kIm45mMUuqfgBKt9b2ZjmU0pdQHgb8BAsDTWus/zXBIp1BK/T7wv4Y3t2it/2cm4xmhlMoHXgZu0lofU0pdA/wz4AN+rrXO+Gy4CWK8D/gTkhdj7gL+UGsdzaYYR5V/DrhNa/2+DIU2Esf41/AS4BtAEHgb+Ph0X8Osa+GPWqztcmAdyWmdZ+a66hQMf7iuA84nGd8GpdQtmY1qYkqpzcDHMx3HeEqpZcC/kVxnaS2wXin1gcxGNdbwNSf/AlwJnAdsGv7dZ5RS6iKSFyWuGt72Af8JfBhYA2zM9Gs5QYyrgL8ELiX5+zaBz2YsQE6NcVT5WcBfZySosXGMfw3zgV8D92mtR9Y8+dR0z5t1CZ9Ri7VprQeAkcXaskUT8HmtdXT44rJ9wKkrJmWYUqqY5B/Ov890LBO4hWRLtH74NbwTeC3DMY3nIPn5CJD8pukChiY94sz4A5LJcuSq9QuBg1rro8PrVj0A3J6p4IaNjzECfEZr3au1toE9ZP4zMz5GlFIe4HvAVzIV1Cjj47sWeEVr/fbw9v8AHp7uSbOxS2eqxdoySmv97sjPSqmVJLt2Ljv9ERnzPZIXzGXjDThXAFGl1G9JfvAfI7l0dtbQWvcNL+m9HxgkudLry5mNCrTWnwZQSo0UnW7xwowZH+Pw1fjHh8tKgc8B92YovJGYxr+OAP8/yW9LRzMR02gTxLcC6FdK/QxYDbwEfH66583GFv5Ui7VlBaXU2cDvgL/UWh/MdDyjKaU+DdRprZ/NdCyn4ST5Te5TwCXARWRZ15NSai3wSWAxyaSaALKiD3+cefF5gZPdtc8CP9BaP5fhcMZQSl0LLNJa/zDTsZyGE7ie5JjSBpLfPKfd9ZSNCf90i7FljeF1g54F/lpr/eNMxzOBO4HrlFJvkrxvwYeUUt/IcEyjNQPPaK3btNZDJL+aZs23uGHXA8/q/9feHbPEEUVRHP/3QZA0qeyEK6T1A2wrmHrRKkhIbEPs0og2YQs/gc1iIQqBQFItIXW2CCQpgqdOE2xFMIVgcce4wdliA+Y9mfODhW12OQy8y7w7w33SqaTf5HkOvaKJ2lW/XgAiYoncIQ0l7ZbO02INeNysmX1gOSKOCmea9Av43LTuLoFj/mHN1NjS+QhsN1u/c3JY2/OykW5ExALwDuhL+lQ6TxtJf4bER8RToCfpZblEt3wAhhExD5wBK+Q1rck3YBARD8iWzhOmjJwtbAxERCySrYh1si1RjYiYKuaBPgAAALtJREFUA0bAa0kHpfO0kbRx/T0iesC2pP70X/x3I/JgqQVJP4FV4Musf1LdHf60YW1lU/1lizyMZy8ivjafzdKh7hNJY2BAvoXwg+zvVrWVljQCDslF9Z18aPumaKgWki7Ifvhb8lqekC861OQZ8Ah4NbFmdkqHuk+aIv8CeN8MqXxIPnOYiYenmZl1RHV3+GZmdjdc8M3MOsIF38ysI1zwzcw6wgXfzKwjXPDNzDrCBd/MrCNc8M3MOuIK2g8xGrufErwAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.distplot(df[\"total_bill\"].tolist(), bins, color = \"g\")"
+    "sns.distplot(df[df[\"sex\"]==\"Female\"][\"ratio\"].tolist(), color = \"r\")\n",
+    "sns.distplot(df[df[\"sex\"]==\"Male\"][\"ratio\"].tolist(), color = \"b\")"
    ]
   },
   {
@@ -670,32 +1067,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x2223a800148>"
+       "<seaborn.axisgrid.FacetGrid at 0x2223dddaa88>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.catplot(x=\"day\", y=\"total_bill\", kind=\"box\", data=tips)"
+    "sns.catplot(x=\"sex\", y=\"total_bill\", kind=\"box\", data=tips)"
    ]
   },
   {
@@ -707,47 +1106,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x2223a928ac8>"
+       "<seaborn.axisgrid.FacetGrid at 0x2223df073c8>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.relplot(x=\"day\", y=\"tip\", data=tips)"
+    "sns.relplot(x=\"sex\", y=\"tip\", data=tips)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 435.725x360 with 1 Axes>"
+       "<Figure size 436.35x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -757,32 +1160,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x2223aa00c88>"
+       "<seaborn.axisgrid.FacetGrid at 0x2223f09e6c8>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 419.1x360 with 1 Axes>"
+       "<Figure size 435.725x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.relplot(x=\"total_bill\", y=\"tip\", size=\"size\", sizes=(15, 200), data=tips)"
+    "sns.relplot(x=\"total_bill\", y=\"tip\", size=\"size\", hue=\"smoker\", sizes=(15, 200), data=tips)"
    ]
   },
   {
@@ -795,22 +1200,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.regplot(x=\"total_bill\", y=\"tip\", data=tips);"
+    "sns.regplot(x=\"total_bill\", y=\"size\", data=df);"
    ]
   },
   {
@@ -823,57 +1230,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.JointGrid at 0x2223babe388>"
+       "<seaborn.axisgrid.JointGrid at 0x2223ef82fc8>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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XoWXbxFPThRpJg+hM0UayULRxJZpl4EJs3kINTVPxuTVcuorbpeHSVFwuFZem4tY1FFVBUwvTgaoCKAp+nxvDMFGnpwqhcBX3wM5VNAfcSznVdcAIIN2Er6LU4FbE64Chag9CCCFKaD1wttqDqCW1GD460FftQQghRAnJlc91ajF8hBBCNDipaxRCCFFxEj5CCCEqTsJHCCFExUn4CCGEqDgJHyGEEBUn4SOEEKLiJHyEEEJUXC2Gj06hy0Ettv4RQohyWHHve7V4on3A0MREEnsxrX2v0trqZ2qquHb09aZRz03Oq/406rmV+rw6O0PFNIhc9vteLbrZudfilc+y6Xrjdp9u1HOT86o/jXpujXpetaYhw0cIIURtk/ARQghRcRI+QgghKk7CRwghRMVJ+AghhKg4CR8hhBAVJ+EjhBCi4iR8hBBCVJyEjxBCiIqT8BFCCFFxEj5CCCEqTsJHCCFExUn4CCFEmSgKpLP51mqPoxZJ+AghRFk4JDN5Lo6nmqs9klok4SOEECXmAPF0nlTWrPZQalYtbiYnhBB1LZbKkc1ZqEox+8itTBI+QghRIrbjEEsa5PJWtYdS8yR8hBCiBGzbIZrMYZh2tYdSFyR8hBBimUzbIZrIYlpOtYdSNyR8hBBiGQzTJpbMYdkSPIsh4SOEEEtk5C2iSQPbkeBZLAkfIYRYgoxhEk8ZSO4sjYSPEEIsgqJAMmuSTEvwLIeEjxBCFM0hnjZJZ/JI7iyPhI8QQhTBcSCRyZOWrgUlIeEjhBALcHCIpQyyhiweLRUJHyGEuAnbcYgmDQzpWlBSEj5CCDEP27GZShjkpWtByUn4CCHEHEzLIZrIYcri0bKQ8BFCiOtI14Lyk/ARQoirJDMG0UROuhaUmYSPEEJQWDyaypkYSUWCpwLKGj7hcPi3gX86/cefRSKRf1TO4wkhxNI4JDMmyUyeVreraqNIZ028bq1qx6+ksm2jHQ6H/cD/CzwE7AT2hMPhr5breEIIsRQOkMiYJNL5qrfLseyVU1VXtvABtOnXDwCu6V+ZMh5PCCEWLZ4ySGXy1R7GilO28IlEIgngnwFfAiPAWeCDch1PCCEWo7B4NEcmJ+1yqkFxynSdGQ6HdwB/DnwdiAF/AXwUiUT+3QJPXQcMlWVQQggB5E2LqXiOvFXeaS4FyOTM9Rv7Ws4u8NB1wFA0kaMl5CnrmCpMme9/lLPg4OvA25FI5ApAOBz+AfA/AwuFDwATE0nsJdbYd3aGGBtLLOm5ta5Rz03Oq/7U67lZts1UModpzv3+0tYWYHIyVZJjqYqC1+8u+vETk0nyWaMkx64FnZ2hef9fOcPnCPBvw+FwAEgD3wI+LuPxhBDipvKWTayGuxaUayaqFpXzns8vgL8EfgUcpVBw8H+X63hCCHEzRt5mqoaDB6CGh1ZyZV3nE4lE/g3wb8p5DCGEWEg2bxFL5qpeSr0QZwWlj3Q4EEI0rHrb8tqqgzGWioSPEKJBOSTSJqk62vJ6Jd3zkfARQjQcB0ik62/L66VW+NYjCR8hREOZ3fI6V387j66gCx8JHyFE47Adh1jSIFenW16vpG7aEj5CiIZg2zbRpIFRx1tem2XuuFBLJHyEEHXPtKe3vK7zcrF8XsJHCCHqQiNteV2v04VLIeEjhKhbubxFLGk0zL0Sw5TwEUKImpYxTOKp+lg8WqycTLsJIUStckhlC1teN1LwAOSM+lqXtBwSPkKIupLImA2782iujiv1Fquc22gLIUTJOA7E6mzL6+wid0ldSQUHEj5CiJpX6FpQP1te502b1z44y7/8weK2MMsZKyd8ZNpNCFHTbMcpLB6tk6uCy5Np9u0f4NJketHPNaTgQAghqs+ybaLJHPl5tryuJY7j8NHJK7z+4Tnylo2qwN47+xb1GlJqLYQQVWZa010L6mDxaCqb58cHznDy3BQArSEPzz6yifWrmhb1Oivpno+EjxCi5tRT14KBCzFefGeARLpQCLFjYzvf2bMer3vxb69yz0cIIaokm7eI10HXAtOyeeuTEQ4eGcUB3C6Vb9+/nl2bO1AUZUmvWS/3tUpBwkcIURMUBVI5k0QddC0Yj2XYt3+AC2MpAPo6Azz36Gbam7zLet2VtM5HwkcIUQOc2cWjtRw8juPw6akxXjt0FsO0UYAHd/Xy1a/0oanLX7liyLSbEEJURr1seZ3Jmbx8cIhjZyYAaAq4eeaRjWzsbS7ZMYy8heM4S562qycSPkKIqoqnjJpfPHr2UpwX9g8QTRoAbF/Xyncf3IDf6yrpcRwKa308bq2kr1uLJHyEEFVhOw7xpEG2hm+yW7bDO5+O8M5nF3AccGkq37hvLXdt7Srb1Uk2b0n4CCFEOVi2QyxV210LphJZ9u0fYPhyEoBV7X6ee3QzXS2+sh43Z5gQcJf1GLVAwkcIUVGm7RBNZjFruGvB0cFxXj44RHa6AOCB21bx2N1r0LXyt8PMrpCiAwkfIUTF5C2baKJ2F4/mDIsf/OQEvzx+EYCgz8VvPryRLWtaKjYGCR8hhCghI28TTeWwazR4zl9Jsm//aSbjOQDCa1p4+uGNBH2lLSpYyEppsSPhI4Qou2zeIpbM1eQaHtt2OHh0lDc/HsF2HHRN5fHd/dx7S3dVSp5XSosdCR8hRNkoCiSzJsl0bXYtiKUMXnxngDOjcQC6Wn38nd+4Db9eva3OZNpNCCGWxSGeNkln8tRg7nDi7CQvHTgzu8Zo9/Zunrinn56uEJOTqaqNK2+tjBY7Ej5C1KGjg+O8cXiY8ViWjmYvj+/uZ8fGjmoPa1Ytdy0wTIvXPzzHRyevAOD36Dz90Aa2rWur8sgK8iukv5uEjxB15ujgOM+/eQpNU/F7daIpg+ffPAVQMwFUq10LLk6k+OHbA4xFMwBsXN3EMw9voqmG1tXkV8iGchI+QtSZNw4Po2kqHldhFbzHpZGb/vtqh4+DQyxp1Nx9C8dx+OD4Jd44PIxlO6iKwmN3r+GBHatQa6yPmlz5CCFq0ngsi9977Y+uW1cZj2WrNKIC23GIJmuva0EibfDSgUFOnY8B0N7s5bm9m+jrDFZ5ZDfSNZVcXsJHCFGDOpq9RFPG7JUPFHb+7Ghe3l4yy2E7NlOJHPka61oQGZ7iRwfOkMoUdhm9M9zJN+9bd83XrpYoCmSN2puuLAcJHyHqzOO7+3n+zVPkKFzxGKaNZdk8vru/KuMxLYdoIodZQ4tH86bNzz8a5oPjlwDwujW+s2cDOza2V3lkN6epiky7CSFq08x9nVqodjNMm1iyttrlXJ5Ks+/tAS5NpgFY2xPi2Uc20RryVHlkC1NVVUqthRC1a8fGjqoXFyQzBtFEDrtGVo86jsPhk5d5/cNzmJaDqsDeO/t4aNdqNLW2igrmI1c+QggxD0WBVNbEQKmZ4Eln87x04Awnz00B0Bry8Owjm1jbE6ryyBZHUxVMCR8hhLieQyJjksrkafVUtuHmfAYvxHjxnQHi6UJRwY6N7Xxnz3q87vp7e1PVwlTmSlB//zpCiKqZCZ5aYFo2b30ywsEjoziA26Xy1P3ruX1zR1UagpaCpqoYeal2E0IIABwHEhmjZtrljMcy7Ns/wIWxQg+2vs4Az+3dTHsVy81LQdPkno8QQgDTXQvSBtlc9RePOo7Dp6fGeO3QWQzTRgEe3NXLV7/Sh6ZWrxN1qWiKhI8QQmA7hXY5tbDBWSZn8vLBIY6dmQCgKeDm2Uc2sqG3ucojm5uigNejEfK708U+R9NUDOntJoRYySzbIZaqjXY5Zy/FeWH/ANGkAcD2da1898EN+L21UfRwPY9LI+h34dJUOtv8V4p9nky7CSFWNMu2mUrmMKvcLseyHd75dIR3PruA44BLU3ny3rXcva2rJosKdFUhGHDjXWL7Hl1VMKS3mxBiJaqVdjlTiSz79g8wfDkJwKp2P8/t3UxXq6+q45qLqij4fToBr47C0kNRVRUM08JxnJoM11KS8BFCzKqVdjlHBsZ5+eDQ7L2m+2/t4eu7+9G12ioqUBTwuDVCPldJCh50TcVxCh8AXLqEjxBiBTDyFtGkUdWuBTnD4rUPhvj01DgAAZ+LZx7eyJY1LVUb03xcukLI58bj1ijVl0ydbgNkmBYuvbaCttQkfIQQZPMWsWSuZG+iS3H+SpJ9+08zGc8BsGVNC08/tIGQv3Z2GYVCQAR9LnxuHUWhpF+zmSu7nGERqNFiilKR8BFiBVMUSOVMEimjasFj2w7vHRnlrU9GsB0HTVV44p5+7r2lp6bueygK+Dw6Aa+rbI1Kda3wuumcSVtZjlA7yho+4XD4W8A/BwLALyKRyB+W83hCiMVwSGZMkpl81YInlszx4ruDnBmNA9DV6uO5vZtY1R6ozoDm4XZpBH0uPC61rF8rj7tQJZdM10YLo3IqW/iEw+ENwB8Du4HLwP5wOPxEJBL5WbmOKYQollP1Pm1fDE3y4/fOkMkVWvbcva2LJ+9di1uvnV1Gry+dLndIz+ywmqiR/nnlVM4rn98A9kUikRGAcDj8HFDdTeaFEDhAIp2vWp82w7R4/cNzfHSysPbS59F5+qENbF9XOxNNqqLg8xZKp9UKTv3NBG8ibVTsmNVSzvDZBBjhcPhVoB/4CfDPyng8IUQRYqlc1fq0XZxI8cO3BxiLZgDY0NvEM49sojlQG0UFigIeXaOt2YNehV5xu7Z08Oc/K3w4aHTlDB8deBB4GEgCrwJ/A/hBMU9ubw8u6+CdnfW1idRiNOq5yXmVl2XZTMZz+AMK/hLdUmlrK+6FHMdh/yfn+at3Bwq7jKoK335wI1/b3V/RK4ubmZliK1SZVWch64XxDH6vjunUzvdNuZQzfC4Bb0UikTGAcDj8V8DdFBk+ExNJ7CUudOvsDDE2lljSc2tdo55buc/r6OA4bxweZjyWpaPZy+O7+yuyDXWt/HvZjkM0Wdo+bW1tASYnUws+LpE2eOnAIKfOxwBob/by3N5N9HUGiU4V3XOzbFRVIeBz4fbopBNZAl5XSf/NFhMiyVSO5oCb0SuJmvi+Wa6bnXs5w+cnwJ+Hw+EWIAE8AbxcxuMJMaejg+M8/+YpNE3F79WJpgyef/MUQEUCqNps22YqaVSlYWVkeIofHTgzW9hw55ZOvnn/utkb69VUidLppWhr8jIea/zb42ULn0gkcjgcDv9b4H3ABbwJ/NdyHU+I+bxxeBhNU2ff8Dwujdz03zd6+Jj2dJ82q7K11HnT5ucfDfPB8UsAeN0a39mzvma+3pUqnV6K9mbvbOl5IyvrOp9IJPJ94PvlPIYQCxmPZfF7r/1Wd+tqw3+6zFs20UTl+7Rdnkqz7+0BLk0WptTWdod4du8mWkOeio5jLrqqEPC78Lp1FMpfOr0UbU1eMjmTVDbf0F0OpMOBaHgdzV6iKeOaqR7DtOmo8y2Xb8bI20RTuSXfN10Kx3H46OQVfvrh2UJRgQKP3NHHw7evrvq0VrVKp5eiq6VQ7HBpIs3G1bW5UV4pSPiIhvf47n6ef/MUOQpXPIZpY1k2j+/ur/bQyiKbt4hXuEFoKpvnxwfOcPLcFAAtQTfP7d3M2p7qVmwpFLoGBP1u9Bq6r3MzPe1+AC6MpyR8hKhnM/cZqlHtVmlpo/J92gYuxPjROwPEp9em7NjYznf2rMfrru7biz7TddqlwjL22Km09iYvbl1ldHzhSsJ6JuEjVoQdGzsaMmxmKAoksybJdOWCx7Rs3jg8zMEjoziA26Xy1P3ruX1zR1Ubgs6UTgc89fn2pqoKPe1+Lowlqz2UsqrPfx0hxFUc4mmTdCZPpS54xmMZ/uTVLzh3qbAWpa8zwHN7N9NexftoM6XTQa+OWoXuBKXU3x3i89PjDb2jqYSPEHWs0n3aHMfhs9PjvPr+EIZpowAP7urlq1/pK8lOnktVy6XTS7FhVRPvH73IWDRDV6u/2sMpCwkfIepYPGXMdoUut0zO5OWDQxw7MwFAS8jD0w9tYGNv9W6K10Pp9FKsX9UEwJmLcQkfIUTtcHCIJQ2yRmUahJ69FOeF/QNEk4Vuy9vXtfL7374NI1Od7suKAn6vqy5Kp5didWcAt65yZjTOPdt7qj2cspDwEaLOlKNP23ws2+GdT0d457MLOA64NJVv3LeWuyVqnNgAACAASURBVLZ2EfS5mKxw+CgUOlQE/e7ZXT8bka6p9PeEGLrYuJ0OJHyEqCOV7NM2lcjxwv4Bzl0uFBWsavfz3N7NdLVWp+NzvZZOL9WGVU3s//QCpmWja/VdQDEXCR8h6kQl+7QdGRjnlfeHZqf17r+th6/f3V+VN0FVVQh49YZuNTOXDb1N/OLj84yMJVnX01Tt4ZSchI8QdaBSfdpyhsVrH5zl01NjAAR8Lp55eCNb1rSU9bhzURTwunVCvvovnV6KmaKDodG4hI8QovIM0yaaLH+ftpErSfbtH2AiXmi4umVNC08/tIGQv/K7jDZa6fRSdDR7CfldnBmN88gd1R5N6Un4CFHDcnmLWJn7tNmOw8Ejo7z58Qi246CpCk/c08+9t/RUfIGjNl067Wuw0umlUBSF9auaONOgRQcSPkLUqIxhEi9zn7ZYyuDFdwZm94/pavXx3N5NrGov0T7bRWr00umlWtcT4tjgBFnDrHqvvFJrrLMRogFUqk/bibOTvHTgzOwi1d3bu3ninn7cemV3GfWugNLpYm1b34aiKqSm/0162gM4wOmRGBuu6nDtcenodX4bTMJHiJrikMiYpNLl69NmmBavf3iOj05eAQr90J5+aAPb17WV6Yhz0zWFoN+Nd4WUThfj5NAk6Wx+9s8zbZPeP3px9l4cwF3butHrtHHqjPoevRANZKl92iLDUxw8MspUIkdryMOenb2E+1vnfOzFiRQ/fHuAsWgGKJTzPvPIJpoDlSsqmCmd9nt1FAmdm/J5NLxujcl44+26K+EjRI1YSp+2yPAUrx4aQtNUvB6deCbPq4eGeAquCSDHcfjg+CXeODyMZTuoisJjd63hgZ2rKnaPZaWXTi+Foii0N3uvueppFBI+QlTZcvq0HTwyiqaps/dp3LqGMf33M+GTSBu8dOAMp85HgcJmZc/t3URfV7Bk57AQl64WuhO4V27p9FK1NXkZHU81XKcDCR8hqmi5fdqmEjm81839uzSVqUQOKFwZ/ejAGVKZwn2EO7Z08q371+FxVaaoQEqnl68l6MZxClOyrSFPtYdTMhI+QlSJ7dhMJZbXp6015CGeyV9ToZa3bJqDbn7ywVk+OH4JAK9b4zt71ldsN9fZjd18LimdXqam6UW+8ZQh4SNEozs6OM4bh4cZj2XpaPby+O7+kr5xG6ZNPJnDnKNrwWIKCPbs7OXVQ0MYFK548paNYVjELIezlwrBs7YnxLOPbCr7G1dkeIpDxy5iOQ7dLX7uv62HW9a33/C4cn9tG03TdDFIIl2d7SvKRcJHiOscHRzn+TdPoWkqfq9ONGXw/JunAEryJpnNW8Tn6VpQbAHBjHB/K09RuMczGc+iayqprIllOygK7L2jj4dvX42mlvfqIzI8xTufjRAKeDBNi7OXEwyOxvje17Zc8zUr99e2Ebl0FZ9HI57KL/zgOtI4d6+EKJE3Dg+jaSoel4aiKHhcGpqm8sbh4WW+skMqmyeWzM3bLufqAgJFUXDrhWMfPDI676uG+1v5a1/bwqqOIOPxQvPRlqCbv/OtW3j0zr6yB4+qKHw5PIXXo5POmGQNe96vWfm+to3N73WRzjVW+MiVjxDXGY9l8Xuv/dFw6yrjsaWXuzoOJDILr+FZqIBgLoMXYrz4zgDxdOHNacfGdr79wHp8FViE6PVohHwuhi8ncenqNb3g5vqaleNruxL43BqpRa7/qnUSPkJcp6PZSzRlXFMRZpg2Hc3eJb2e7TjEU8WVUs9XQDDX/RrTsnnrkxEOHhnFofAm/tQD67l9c0fZG4K6dJXWkAeP4uA40BxwF/U1K/XXdqXwefSGC2iZdhPiOo/v7seybHJ5C8dxyOUtLMvm8d39i36tXN5iKpEreg3Pnp29WJaNYRaObZiFY+/Z2XvN48ZjGf7k1S94bzp4+joD/P2nd3DHls6yBo+mKjQF3LQ1efF7XbOl08V+zUr5tV1JfB6drGGVfVuNSpIrHyGuM3Pje7kVWYZpMxnLLKqU+uoCgrmq3RzH4bPT47z6/hCGaaMAD+7q5atf6UMrY9eAhUqni/2alepru9LMTKFmDeuGact61RhnIUSJ7djYsaw3xJmKtpZW/6KfG+5vnbOyLZMzeeX9IY4OTgCFEtxnHtnIxt7mGx5bSh6XRtDvwrXA6vpiv2bL/dquRD5PYZoykzMlfIQQN1IUSGVNEiXeDuHcpQT79p8mmiys9di+rpXvPrgBv9dVuoNcR1cVggE33gp1QxDzm7nyyRiNU3Qg4SNEyZR+OwTLdnj3swvs/3QExylUvn3jvrXctbWrbPd2VEXB79MJSNfpmjEbPotsPFvLJHyEKIGlbodwM1OJHC/sH+Dc5QQAq9r9PLt3E91LmMorhqKAx60R9LnQpet0TfG5Z6bdltYDsBZJ+AhRAkvZDuFmjgyM88r7Q7NVcvff2sNjd/fjKtP2lS5dme46rUnzzxpUWHisypWPEKJgOdshzCVnWLz2wVk+PTUGQMDn4jcf2jBvb7flUlWFoG+667QiXadrmd+rN9RCUwkfIZZoudshXG/kSpJ9+wdmNw7bsqaZpx/aSMhf+l1GZ0qnA15X2dvviNII+FyzW2M0AgkfIZbAtm2mksvbDmH2tRyHg0dGefPjEWzHQVMVHt/dz7239pRlOwK3q3Bfx+OSjd1qzbb1bZjW3N9TAxdifH56nJ1bOlFUhdR1U3Ael06ZZmXLQsJHiEUybYdoIodpLf+dO5YyePGdAc6MxgHobPHxW49uYlV7YNmvfT1NVQj6XXhlY7eadXJoknR27qubTM4knTX55MTlOe/93bWtG70C/fxKpX5GKkQNyFs20UShc/RynTg7yUsHzszeRN69vZsn7um/pq9bKcjGbo0hML2mK53N0xys/03lJHyEKJJh2sSSyw8ew7R4/cNzfHTyClAIhqcf2sD2dW2lGOY1ZqbY3PU0HyPmFJjubJDMmBI+QqwURt4mmsotu7HjxYkUP3x7gLFoBoANvU0888gmmgOlLSq4fopN1L/mYOF7JJrMsbqz9NOylSbhI8QCcnmL2Dw7jxbLcRw+OH6JNw4PY9kOqqLwtbv62LOzt6RTYTLF1ri8bh2fRyN6k72d6omEjxA3kTFM4qnl9WlLpA1eOnCGU+ejALQ3eXnu0U30dQZLNMoCqWJrfC1BD1NJCR8hGpaiQDJrklxmg9Avzkzw/de+mF2fcceWTr51/7prNlNbLk1VCPinF4oiVWyNrDXkITIcxXacur+ylfAR4gYOibRJKrP0BqGmZfPzw8McOn4JAK9b49sPrGfnptJtJSBTbCtPS9CDZTskUvnZe0D1SsJHiKuUokHo5ak0L+wf4OJEGoC13SGe3btpzq2wl0qm2Famme+haDIn4SNEo3AciKeX3iDUcRw+OnmF1z88R96yURT45v3ruXtrV8la2MgU28rWHHSjUOh4vrYnVO3hLIuEj6iao4Pjs9spr+oM8ujtvVXd4TKWzpFdYsv6dDbPj987w4mzUwDomoLPrREZjtIWdC+7MahMsQkAXVMJBdxEG6DooOjwCYfDrYAViUTiZRyPWCGODo7z/Jun0DQVv1dnKp7h+TdPAVQlgKKppQfP4GiMF/cPEE8XigpcukrI78Lj0ogls7x6aIinYMkBVOw21mJlaA26mWyAcusFv5vDBR8DY8BEOBw+EA6H+8s/NNHI3jg8jKapeFwaiqLgdetomsobh4crPpalBo9l2/z8o2G+/5OTxNN53LpKZ7OXpsD04k5FwePS0DSVg0dGF/36uqbQEvLQGnJL8IhZLSEPiXS+JE1tq6mY7+gfAH8K+IAg8CPgz8o4JrECjMeyN7R8cesq47Fsxcbg4BBNLi14JmJZ/uSVLzjw+SgOsLozwN9/egemZd/Qm82lqUwt4pOqqiqE/C7am714XRpIjwJxlZmig1iqvq9+ipl280cikT+56s9/FA6H/3a5BiRWho5mL9GUcc16F8O06Wj2VuT4tlPYBC63yL14HMfhs9PjvPr+EIZpowAP7url0Tv70DWV1pCHeCZ/TQDlLbuoSjdFKaxiD/l0VNnGWsyjZbqv21TCoKPZV+XRLF0x3+FfhsPh+2b+EA6HbwWGyjcksRI8vrsfy7LJ5S0cxyFrmFiWzeO7yz+jO7MJ3GKDJ5Mz2bd/gB+9O4hh2jQF3Pz+N7bx9bv70aenxfbs7MWybAyzcF65vIVl2ezZ2XvT13a7NFpDXlqCbgkecVNBvwtVVYivgCuftcCBcDh8BDCB24FL4XD4KEAkEtlxsyeHw+F/D3REIpHfXeZYRY24ukqto9nL47v75ywSmHnchfEUluWgawq9HYFrHn91tdvGniBvHB7mL35x6qavuxy2YzOVyJE3F65RjgxPcfDIKFOJHD6PRiyVn93GePu6Vr774Ab8023uZ4T7W3kKZp/X2Rbg3u1d8xYb6NOl07LHjiiWqig0+V3EUvW9q2kx4fO/LfXFw+Hwo8DfAH661NcQteX6KrVoypizSm3mcXnTJp0zwYFc3uHy1LVVbTPPOTee5j//6PMFX3c5TGt6E7giOlNHhqd49dAQqqqQtxymJgpdqDVV4Vv3r+OurV0o85Q8h/tbZ8OmrS3A5GTqhseoioLPqxPw6lI6LRatKeAmmjSqPYxlmff6PhwOb53+z8RcvyKRyIFIJHLgJs9vA/4v4F+Xbrii2q6vUpup5rq+Sm3mcRnDQqHwpq0oKpmcOefjf/zuQFGvu1RG3mYqkS0qeKBw5YKikEjlSU73ZdNUhVVtPu7e1j1v8CxEodBqp63JS0jW7Iglag64SaSNZW/xUU03u/L598A3gY+AYa4tuQkAXQu89p8A/wewZikDa29fXsffzs76Xv17M9U8t8mkQcinX/Pmq2sKU0njmnHNPG48mkFVFBRFQVMcTMsh4NVvePzlyXRRr7sUibRBPmXQ3OIv+jmXo1nS2fzsNFjI76I54CJj2LS1LW4vlZnHayqE/G78XteSw6vWNOrPWbXOKxjwoBWxk21XewDnzCS2otAcLBTp+P0eOtuK/x6vtpuFz1+fvno5ATxMIXwcwA3Me8UDEA6H/xZwPhKJvB0Oh393KQObmEguOdU7O0OMjSWW9NxaV+1zawu6b6hSy+UtWoPua8Y18zhNU7EsGxWwnUKgpLLmDY/vbvMzNpVe8HUXxyGVNUlm8kXfS8kZFq99cHa2C7WqFNZVeN062bxFs9815zTafNraAkSjaQI+F26PTjqZI90Aq9Oh+t+L5VLq81pMkK3pCmBav16/o6sqc3Vm8roU3v74PEGPztY1zQBYeavm/j1udu43K6v5S2AcuIXCAtMr078PA58scMzngMfC4fDnwP8JPBUOh//jIsYsatT1VWoz1VzXV6nNPM7n1nAAy3ZwHBufR5/z8d99eFNRr1ssB0hkTBLp4oNn5EqS//TjY3x6agz49SJPj0vDMIurWrvaTOl0e5OXgEc6WYmFnRya5MipsdlfqgIBj37Dr/XdTQBMxbOzf1dvO6XP+xMRiUS+DhAOh78fiUR+fzEvGolEvjbz39NXPg9HIpF/sNRBitpxfZXafFVpVz/OuqrarbvVN+fjv7Ktm+99bUtRVXTFiKeKbxBqOw4Hj4zy5scj2I6Dpio8cU8/bSEP7x+9yFQiR2vIw56dvUW3yJnpOt3e7GXMqO+qJFF7/F6dpoCbi5Ppag9lyRb8OLbY4BGN7+oqtVI8bqmPn8tiF4/GUgYvvjPAmdFCy8KuVh/P7d3EqvbCfZqta9sWdfzru04LUS49bX4uN3L4LFckEvkBhRY9QpSVbdtEkwZGkT2vTpyd5McHzhRKwYHd27t54p7+G9rjFEO6TotK62nz8fnp8WoPY8lkIlo0BNOeXsNjLXyDxzAtXv/wHB+dvAIUQuPphzawfd3irnJmzEyxXd+rTohy6m7zE0/nSWfzNyx2rgcSPqLuGaZNLJnDKqI68uJEin37B7gyVVg0uqG3iWce2URzYPG7QsoUm6imntZCWfXlqQzrV0n4CFFRubxFLGlgL1DS5jgOHxy/VCiAsB1UReGxu9bwwM5Vi54mkyk2UQt62gvhc2kyzfpVTVUezeJJ+Ii6pCiQypkkUsaCpdSJtMFLB85w6nwUgPYmL8/t3URf1+IXMs9MsXlcqvRhE1XV2eJDVRRGx4tfd1ZLJHxEHXJIZExSRSwePXU+yovvDs4uGr1zSyffvH/dNYtZi6GpCkFpACpqiK6p9HYEOHe5thaWFkvCR9QVB0ik86SzN1/DY1o2Pz88zKHjl4BCP7Xv7Fm/6FJumWITtWxdT4gjg+M4jlN3LZskfERJFLvNwnIVs3j0ylSGfftPc3GisAZibXeIZ/duKmpDt6tJFZuodWt7Qrx/7CKT8RztFdqIsVQkfMSyFbvNwnLYjkM8aZC9yeJRx3H46OQVXv/wHHnLRlFg7x19PHz7arS5GmTN4/o9doSoVTOFBoOjMQkfsfJcvc0CgMelkZv++1KEj2U7xFIGxk2CJ53N8+P3znDi7BQALUE3z+7dxLqe4quAVFXB79Xxe2SPHVEf1vYE8Xk0Tpyd4u5t3dUezqJI+IgluXqaLZbM0RLywFU38d26yngsu+zjWLbNVDKHeZOdRwcvxHjx3UHiqcLmWrdtaOc7e9bjK7KZ50wD0JBPly2sRV3RVJWt/a18MTRZd/d9JHzEol0/zRZPGUzGsyiKMvuGb5g2HcucBlho51HLtnnrkxHe+3y0sNeHrvKt+9dxx5bOon8IpXRa1Ltb1rfx2elxrkxl6G6Q/XyEmNP102zNQTeT8RzRRA6vW8Mw7WVthwALdy2YiGXZt/80I2OFNQ6rOwP81t7NRc97S+m0aBS3rC+0hToyMM5jdy/9Z67SJHzEoo3Hsvi9v/7W8XtdOI5DLFkogV5utdvNuhY4jsNnp8d59dAQRt5GAR7c1cujd/ahawtPmUnptGg03a1++ruCHD55RcJHNLaOZu8Nu5nqusbG1T7+8f90x5Jfd6GuBZmcySvvD3F0cAKAJr+LZx7ZxMbVzUW9vpROi0a1+5ZuXnxnkCtTabpa62PqTcJHLNrW/hZ++stCjzSXpuLzaLh09YZptsWs/XEcp9ChN5Nnrhmwc5cS7Nt/mmiyUFSwfV0r331wQ1HdfGem2Hxu+XYXjenurYXwOXziMt+6f321h1MU+WkUi3J0cJxDxy8R9LlIZ/OYlkUq4/CNe68NlsWu/ZmIZ2db4FzNsh3e/ewC+z8dwXHApak8ee9a7t7WtWBRwUwVW9DnWtQ6HyGqZdv6Nkzr1/tRKapCqogdeb1enY2rm3j/2EUevrPvmillj6s2t9iW8BGLMlNs4PdqNE1vQ5DLW3w5HOWpOR630Nof23GIpwz8wRvDYSqR44X9A7O9q3ra/Dz36Ca6i5hW0HWFJp8bj1uTYgJRN04OTZLOLm3b9VXtAQYvxHn14BCrOwOzf3/Xtm70IpcdVFLtjUjUtOuLDWDuNT3FPM52nMLOo3mL6+Pk6OA4Lx8cImsUFpbef2sPj93dj2uBj3CqohDw6QSmp+MkeMRKsbYnyCdfakSGp64Jn1ol4SMWZa5ig7nW9Cz0ONuxmUrkyF+3eDRnWLz2wVk+PTUGQMDn4jcf2kC4v3XBsXldGqGAC00WiooVSFNVNq9p4djgBIm0Qci/+A0SK0l+SsWiPL67H8uyyeUtHMchl7fmXNMz3+OeuKcf03KYjN0YPCNjSf7Tj4/NBs+WNc38wdO3LRg8mqrQHHTTEnJL8IgVbcuaZhQFvjwXrfZQFiRXPmJRZu7XLFTFNtfjnrxnLVvWtDKZyGJftXjUdhx+/stzvHJgENtx0FSFx3f3c++tPTddiyMFBUJcK+B1saG3iVPno9y6oa3oFlPVULsjEzVrx8aOohaQzjzu6OA4+z8d4fVfnuP1X56lrzPImdE4U4kcIb8Lw3S4NFnY/qCzxctzezeTSBt8/ycnmErkaA152LOz95orIJeucnEyxdufjHBhLFXWbRyEqCe3bWhn8EKck2enuCPcWe3hzEvCR5TV0cFxXjowSCjgIZU1iaVyRM7HCPpc6JrC+bHUbFHA3du6ePLetQyNxnn10BCapuL16MQzeV49NMRTwPZ1bQR8LoYuxviLn0dQ1fJt4yBEPWoKuFnXEyIyHOWWDW3VHs68ZIJclNV7R0YJ+FzEUwaW7RSq1xxIZfNMJQqdDBQFejsCfGfPBty6xsEjo2iailvXUBQFt67h87o4cXaK9mYvfo/OTz84h6oWSrkVRcHj0tA0lTcOD1f7lIWouts2tpO3bL4Ymqz2UOYlVz6ibGzHIZoyyOZMmN6WzTRtHMCZXkfndqm0BNzkrtqrZyqRwzs9V60qhe4EHpfKhfHU7D2ghUq5K7WzqhC1qDXkYf2qECfPThFN5gjU4L0fufIRZWHbdqEVju1gmDaO45DM5Lm6SXXI76K9yYsNtDf7Zv++NeQhb9n4PDodrV4UBS5PZWjy/7qVTkezF8O0udpMKfdMd4VoyrhmSu7o4Hi5T1uImrFrcweOA69/eK7aQ5mThI8oOdNymEzkMPIWe3b2ks9bjMeys5u9Afg9hSafeauw/cJjV5VqP3z7alpDHly6QjyZYyqRu6Gc+2Yl31d3V5ApObFShfxuwv0t/PKLS1wYS1Z7ODeQ8BEllctbTCaymFbhEkdRFHJ5m/z0VUrQp/PwrlX0tPnJ5kyafC7u3NLJLw4P8x9++BkvvHOa1iYPT923DhyIp/K0BNx872tbrpk227Gxg+99bQstATfprHnNY8Zj2Rs6V5dqZ1Uh6sltG9vwujX+8u3TODXW7qP2JgJFnXJI5SyS6UIRgWnZ/PzwMIeOXwLA69b49gPr2bnp2vsukeEpXj00RGuTl9XdIeJpg//2RoTvfW3LgtszzFfyXWwXBiEandet84371vGjdwb5+Msr3L2tu9pDmiXhs4KU6ya8AyTShY3kAK5MZdi3/zQXJwprd/q7gzy3dxOtoRvf/A+fuExPRxCvSyWaKGwgNzNFttSxPb67n+ffPEWOwhVPKXZWFaJe7dnRy0cnLvPDt09z24b2mll4WhujEGW32C0OimU7Dp+eGuPnh88xEcuiaypTyUJZtaLAI7ev5pE7+m7oQKAAHreG7ThYhkk+r8zuXLrcKbJiuzAIsRKoqsLvfD3Mv/5vv+KV94f4rUc3V3tIgITPilHsFgeLYdk2n0Su8ML+ARRFIZO3yaUK7eADXp3vPbaFdT1NNzzPpSuEfG7cLhVVUYhn8wR9v65kK8UUWbFdGIRYCTb2NvPgrl7e/OQ8d23rYmNvcbv/lpMUHKwQpb4Jb5g2k/Ecb350HssubI2Qm97+wO1S6Wj23hA8qqrQFHDTFvLhdmmAMlu1ljXMmzYqFUIszzMPb6I15OHPfnIS46p1ddUi4bNC3GxdzOI4pHMmU4kshmkxOp4inspj2w4K0BJ00xbyXFNWrQBej0Z7U6E7wdW9Qmeq1lqbfDdUrQkhSsfv1fm9J7dxaTLNj987U+3hyLTbSlGqm/Dx6cKCiViWfftPk80XAs2lq7SGPOiaimFatIY8AOiqQjDgxutSmelycL0dGzt49J71jI0llnOKQogF3LKujUduX82bH5/n9s0dRe2TVS4SPivEcm/Cm3Zhu+ucYfLZ6XFePTSEMR08XrdKYHpbA8MsTJs9uKsXn0cn5HfddFuEeiXte0S9euaRjXwxNMmf/uQE/+L3757d9bfSJHwa2FxvkAutnbmeokDGsIinDFKZPK+8P8TRwQkAmvwunnlkE6Zlc/DI6Oz2B4/c2ccdWzrxuQv3dRpNuSoHhdi2vg3Tshd+4CIoqkIqZ17zd7/zRJj/uO8If/qTk/zNb25DKfEHRI9LZ4Ed7yV8GtVi3iDn+xTv4BBP5cnkTM5eTLBv/+lCvzbApSm0hjyYlk24v3X28t3r0mgKuFAbeEfRclQOCgFwcmiSdDZfkWPdvrmDX0XGeP4XkZJPv921rRt9gfVEEj4Nqtg3yLlCat/+AXRNpbvNT9awePezC+z/dGR2352ATyfkc5HKmbP77Gxb10bQ78LfoFc7V1uoo7YQ9WD7ulYuTqT5+Msxulp9cy4CL6fG/Xi6whVbWn19E87mgJu2Zi8/+eAsV6Yy/OlPTvD2rwrB49ZVWkJumgMeVLWw347bpXH0zAQdTR78bp1GDx4oZeWgENWjKAr339aDx6Xy3ucXZ/svVoqET4Mq9g1yJqQUpbADosulEkvkOH85wR+9dJRzlwoVaPff2oPfo+Fz//oTv9+r09MeYDyaaehptuvdrKO2EPXE59F5YMcqYimDj09eqeixV847xgpT7BtkR7MXB2gJesgaFuPRLGOxLKmcRdawCPhc/I3Hw3zjvnW0NXnJWzaaqtDW7MHv0RmPZqpWLVMtN+uoLUS9WdUe4LYNbQxciM1+2KwEuefTQK4vHLj/1h6+HI7etBz4G/et5acfnONKNEPOMJlKGNjTO75tWdPM0w9tJOR3A7BnZy9v/eo8TUE3hmERT+cLgfbwxoqfa7VJ+x7RSHZu6mB0Is2HX1yio8VbkQ+UEj4NYq7CgUPHL930E7ntOKzuDLJ7exevHTrLZKJQyaYqCk/c08+9t/bMrtFRgF2bO+ls8fLCO4NcHE8BCt2tcp9DiHqnqgp7dqziJx+c5dCxS3ztK30lL7++4ZhlfXVRMYvZvVNRpjd9i2W5Mpnh/WOXZoOns8XH3/vurdx/26rZ4NFVhZaQh5agG9NyyORM2lt8rOrwYzrIFtVCNICmgJuvbO3i0kSak2enyn48ufJpEMWW/1q2QzKTJ5szOXF2kpcOnCE9vQDt7m1dPHnvWtx6oTxbUQo3JIO+QpcCx5E1LkI0ss19zYyMpfjs9DhruoOzU+7lIOHTIBbevdMhm7dJpAyyhsXrkdN+5wAAHa5JREFUvzzH4ROXgULAPP3QBrava5t9rq4qhAJuvG6Nq3ffraU1LtLiRojSUhSF3du7eOXgEIdPXOHRO1eXbfpNpt0axHzVbU/c04/tOMRSeWKJHCNjSf6/vzo2Gzwbepv4g9/cMRs8CoUwamv24nFdGzxQO2tcZu5xRVPGNR0cZPpPiOUJeF3s2tzB6HhqdjficpDwaRBzlf/+9cfDbF7TymQsSzqb59Dxi/yXl49zZSqDqih8/e41/P6T22gOFC6tNVWhefreznzNQGtljcti7nEJIRYn3N9CwKvz2akxnOs/gZaITLs1kKvLf23HIZEuXO0kMnleeneQyPkoAG1NHp7bu5k1XcHZ53rdGk3+Qk+2m32v1coW1bU0/SdEo9FUlR2b2vnw+GUuT2boafeX/BgSPg2ncG8nmTIwbYdT56P86N1BkplCs8I7tnTwrfvW43H/uqgg4HMR8LqKboxTC2tcFr7HJYRYjvWrmvjVl2OcOh+V8BE3Z9kOiYxBLmeRt2x+/tEwh45dAgpVad/Zs56dm34dGrqq0BTw4HbNPft69Q19r1sDpxBstXBzv1Sb4wkh5qZrKutWNXFmNIZlO2hqaQsPyho+4XD4nwPPTv/xp5FI5B+X83j1YLkVWn/5iy95+d1BsoaJ163z2F19bOxr5tDRi5y/ksTj0rhtYzvvHbnIVCIHFLY/cByLffsH2Ld/AFWB3s4APrdOzrCwbZt4Oo9pOWiawuqOAFv7Wzh0/BKaVuj7NjKWmh3DRDzLuUtx/u63b61aANXK9J8Qjay3w8+p81HGYxm6W0t79VO28AmHw18FHgNuBxzgjXA4/BuRSOSvynXMWrfcTcheff8Mr31wDigUB+Qtm/eOXuLjL69g2g4KcCWa4ZX3z84+x+tSZ7e6hsJVQmuTl3gqz5XJNJbt4DjgOIVVzphwaTLN4GicoM+F36sxMpW5ZhyOAxnD5r+/8SX/7u89sPwvzBLVwvSfEI2so9kHwFQiV/LwKWe120XgH0YiESMSieSBk8CKnhNZboXWLz4eAaUQPH6v6/9v786D47juA49/e+7BzOAiQVIEL0Egn6iLIilRpo6lSFGxZFuyYkmmJe/GSpXtTWq962zZym423rLjKqcqyVrOJusqJxV7V15ZFeqKVocPxSaogzpt8RBJ8YkEL5HgiYPAYDBXd+8fPQABXhgAc3Xj9/lHBNgz3Q+g+jf93u/9fsxuipI3LY6eTmFg0JfMkhw627FwRkMEc9Tr49EgzQ0R+pIZ+gezZPM2huGjUMoNv8/AwOlcalr2SFMr0xqbgTD88D1cFUEI4U2RwtpwJmuOc+TEle3JR2u9a/jPSqnFONNvtxT7+hkz4uMfdAktLYkpvb4cepJZEtHAmE1bAb9BbzJb1PWmcyahgEFDLEzA76NnIEMq7QSbU31DI0Fi+N1jkQB9A2kMA5oSEfw+g5O9QyOFQwH8PsibzqOpYRj4ANO0CAV85PIWwQv1wjUA23lNKX/Otfg7KwWvjgu8O7ZqjSseC+MP+Mc/sEJs28ZngN/vJxEvPpmnri5MS/Oln5TKnnCglLoaeBl4VGu9t9jXdXcnx9wkJ6KlJcGpU5UrDV6s5njovAytTM6kKR4q6nqb4mHCYR/ptElPf3pM/r1ZmHZriIdIprIYhkHetAmF/NTXhUln8/T0n/+kMtwu3sD5h2bZNn6/j0jIj2naI1lyow2fNhz0leznXKu/s6ny6rjAu2Mr9bgmEsiSg5mKtdEuRjqbx7LBb8BAsvhtDKlUhlOmecmxlzvh4BbgWeBPtNb/XM5zucFUMrR2H+qmMR7k0IlB8qY1/PAxIuA3aEqEsYFwKODkUBuwcFaCQyeSDGXy571nKGBgWhY+wwkopmWDAdGQn2DAx6dXL2DP4T76kxlSmbGP3YYBd0tmmRAltfTyZvJm5TqKBnw+LpXEtueQU2B05ZWzuPry5osfeI5wcPzQUs6Eg/nA88AGrfWmcp3HTSaToWUY8MH+bp7q2Ec6axIJGSSHxgaeay5vZnAoS18yS1MizKc/sZBgwMe7H56k80gfmax5XrBK1AUJB/0jKdSjs93mNNeNXNe9heNfeGM/r7x3ZEyW3b23tpX6RyTEtPbhgZ6KPvncuHQ2sfDFw8Dej/sI+A2Wtc0Y2RtYKuV88vkmEAEeU0oNf+9HWusflfGcNW8iGVqWbZNM5XhxywHSWZOAz4dln/2Y4vcZfOnuK2lvbRjzumDAx4meFCd7UyTiYWaMesqabMfNe29tk2AjxDSSyZm8ufM411xe+sAD5U04+Drw9XK9v9dlcxb9qQx506brdIp0zsQ0zz67OE8tvvMCTyTsp74uxE9e2g2FjLrh46X1gRCiWK9u6yI5lOPuT5Rnel0qHNQYG5vkUJ5UOodtw4cHexjK5Bmde2EYEAwYzKiPjPlePBokFgkABqek9pkQYpJ6BzL8vzcOsHRhE4vnNZblHBJ8akg2bzGQypDLO5GmdyDD0x2dYwJPwA+WBemsyW3L5gLO5tCGWGhMFt3Fap9Fgj7++sn3pSqAEOKCbNvmp7/cg2la/MFdavwXTJIEnxpg2U5K81AmP5LGvKOzm+df308652SZhUN+LMvCssDvh1DAj1rQRCBg0BiLEPAbhdc55Xu6Tg8ylDWJRQLUx0Jk8xapoRwYBnmbSVVYEEJ430tvHmR7ZzcPrV9c8qoGo0nwqaqxFajBWeR7actBfvfRKcBJKohFA8SjTs+dgN8glcnTEHWy1RpG9d4ZXb6nMRHGn8qRTOUwLZvWmTECPoO8Zcs6kBDign675yT/8voBVl89h/Ur55X1XNJMrkosy6ZvMMuZgcxI4DlyKsn/eu6DkcCzZH4D969pw+8zyObPNm8Dm0/dvJDGeHhM07dzy/fUx0LMaIzQOjPGnz68gnTWJHROxQJZBxJCgDPb8g8v7OKK1noeuVuVrX32MHnyqTAbGMrmSaZyIxUcLNvmje3HeOW9j50KAz6DT65awM3XzsFnGETDAV7f3kXvQIa5sxLcuXIey9pnnNf0bbwGa9IDRwhxIbsO9PDDf/mA1pYY//nBZQQrUOJHgk+FGAZkchbJVJZs/uwO5jODWR7/xR6O9zi90oN+H3fdNJ/V11w2coxa0IRa0ITPZ7BofhPvbj/KX/3MSRqIBH1gGKSzJql0HtOyqS+0xYaxwaXaPXCm2k5CCFF6v91zkn98cRdzmuv4xobrqYsEK3JeCT4VYFkWA0N50tn8mKeV3Qd7eLqjszCVBj4D8qbFL94+zFAmz7qV80eODfgMGhJh9hzo4f++ovH7fYDNsZ4hwKa5PkIo6OPMoNPDJ1EXPC+4VLMHzlTbSQghSu+N7V081bGPtrn1/MmDy4hVKPCABJ+yG8zkGRw6O8WmD/fy6rajHDudIlN4AjIolGIzDPw+p8ba5m1dtLbEUQuaCAZ8NCVC+Awfz27eN7Ku0zuQwZmW9TGQyjG7UEU2kzXx+4wLBpdq9cAZvR4FkuwgRDVZls17e06iD/dx3RUz+OPPXlOWKgaXIsGnDAwDMlmLgaGze3bACTzPvdZJKm2O6ZHjBB9jpMCfz3AC0Ovbu7juipljMtpO9KScqTYgn3eKghqFJyaA+liIVDrPX//xzZUZbJHGW48SQlRGOpvn1W1dnOgZ4o6V83jojsVOI8kKk+BTYqZlM5geu2cHnI1bL715kIHU2erSibogQ+kceQuCo373Nk59NsNn0JgIYXD2L2c313GqN0U46CcQ8GGaFrbt9FuH2kwg2NF5mlQ6T89AhmDAR31dkLpIsCavVQgv6x3I0PH+UVKZPLdcO4f7/k1bVQIPSKp1ydhAKpunpz9NKj028CSHcvz0l5rufmc9ZnhKLFEXIlFIDnDaWTv9dAyfwbyZMRpjYwMPwOdub8c0LTI5k0Q0UGiBbZGoC5LJmRVNICjG8FpPOOTHwCaft+gZyHAmmam5axXCyw6fGOAXbx/CtCzuWjWfK86pC1lp8uQzRWen2LLk8uf34fjo4z6e2dw50pQtHPTRlIiMfNrw+300xUMMDDmbQSMhP3NnxMib1kj5nNFuWDqbL965ZCRp4LLm6Ei2W2MsVHMZZMNrPXURp0dQ/2CWXN4km7N45O4ra+pahfAi27bZ0dnN9n3dzGyIcPvy1vOmwKuh+lfgYqbllMU5N4sNnDWYX71zmC07jwPOAvuqpbPYeaCbvGURNHzkTCcb7d5bLwfg7V3HMQyDcMjP2uWtF70xVytpYDJGr/VEwwGi4QC2bZNK510zBiHcKpe32PLBMQ6fSNI2t57VV88uZMpWnwSfSbEZypojpWvOdbJ3iI2b9nKs29m7s2B2nA3r2mlKRGibWz+yYbQpEea2ZXNRC5rYd7SPpkSYwyeS1BM67z3dSja2ClEdyaEcHe8fpW8gw0rVwlWLmspetWAiJPhMkGlZDKRypLPmeX9n2zbvfniSn791iJxpYRiwdnkra1fMw1+YZhveMDpaZ9cZ3tx5nORQjkDA56k9MNXe2CrEdHSiJ8XmrV1Yts26lfNobYlV+5LOI8GnSDaQOmfPzmipdJ7nXutk90Gn53ljPMTn17WzaE79Jd834DfYtb+HgVRuJGPNS3tgqrmxVYjpSB/u490PT5CoC7F2eSsN8dqcSZHgM46RsjhDObK58592wHlyebqjk/7BLADXtjVz321tRC/RGx0Y2Tza2XXmvGO9tAfGTWtUQlTTsvaZI3v2Jsq0bJ7d3Mk7u0+wdFETj9y9dNzEgnCweiFAgs8lXGzPztm/t/j1b4/w2rYubJyAcc8ti1ixpGXcudVQ0E9jYfPojHpZFxFCQDTkx7ImnhCQyZr8+OXdbNt3mk+ums+Dt7dXbf9OsST4XIANDGXyJC8yxQbQfSbNxk17OXJqEIDWlhgb1rUzsyE67vtHgn7qR1UtkHURIcRk9Q9m+Z/P7ODg8X6+eOcS7ihzH55SkeAzynh7dsBJKti69zQvbDlANmdhALctm8v6G+aNrNlcSjQcoCE2dg5W1kWEEJNxoifFD57aTl8yw9d+/1qWL2mp9iUVTYJPwaX27AxLZ/M8//oBdnR2A1BfF+SBte20F7lTOBYNEo9euGqsrIsIISbi4PF+Htu4HYBHH1pe9YoFEzXtg8+FmrtdyKHjAzzVsY/eAadEztKFTXxuTVtRJcgNA+LRILEa2FUshHC//V39fH/jNurCAb75hetHKtq7ybS9GxaTxQaF9gZbj9Lx/hEs20mN/vTqRaxaOquoDVuGAQ3xMJFgZcuVCyG8yQk8W4lHgzz60PKi1plr0bQMPuNlsQ3rHcjwVMc+Dh0fAGBOcx0b7mhndlNxnzL8PoOGWJhQsDbKWQgh3K3r9CA/eGobsUiQ//LwCprr3ZsRO62Cj41TU2wwnb/kFBvAjs5unn99/0glg5uvmcMnVy0gGCgukAT8Bo3xCAF/bac7CiHcoXcgw/c3bsPv9/HNL1zv6sAD0yb42KRzFslUlrx56aCTyZm8uOUg7390CnCSBB5Y03ZeSZxLCQV8NMbDNZ9nL4Rwh2zO5O+f3UEqk+fPvriCWUXOvtQyTwef4XWdwaEcmUus6ww7cirJxk376C5UFlgyv4H711xBoq748hSRkJ/62Nk9PEIIMRW2bfP4L/dw6PgAX7v/WhbMTlT7kkrCs8Gn2HUdAMu2eWP7MV5572Ms28bvM7jrpgWsvmbOhILIhfbwCCHEVLy58zhv7TrBfbdezvLF7tnHMx7PBR8b6E9m6O5Pj7uuA87u4Kc376PzaD8ALY0RNqxbzNyZE6sCWxcJTOgJSQghxnOyb4gnXvkINb+Rz9y8qNqXU1KeCT7DU2wDqSwJjKICz4cHe3j21f2kMnkAVi2dxadWLyQUmFhadCwaJBH1zI9SCFEDbNvmiV9pDAO+cs9VnltD9sQdcyJTbOB09/v524d4Z/cJwJkuu39NG1ctap7QecduHvXWPwwhRHX9Tp9i54EeHlq/2PWZbRfi6uAzXJ1g8CIdRS/kWPcgGzft42TvEABtc+t5cG37hNdqDAMSdSFikUBRAU8IIYqVNy2eebWTeS0x7ljhjkKhE+Xa4JPNj1+dYDTbtnlr13F++c5h8qaNzzC488Z53Hbd3Ak/zhoG1MdCREMSeIQQpffmzuOc7B3iP91/neem24a5LvhYtlMAtNgpNnB6mT+7uRP9cR8AM+ojbFjXzrxZ8Qmf32cY1MdDUi5HCFEWlm3zq3cPs3B2gmXtM6p9OWXjquAzXAC02Ck2gI8+7uOZzZ0kh3IArFjSwj03LyIcmnjw8PkMGqVcjhCijHYf6OFYd4qv3HNVUfUj3coVwSdv2QwMZovaKDryGtPiV+8eZssHxwEIB/3cd9vlLGufXNsCv8+gIR4mVGR5HSGEmIw3PjhGPBrkxitnVftSyqrmg89gOsfgUB5rAosrx04P8o/P7+RYdwqABbPjbFjXTlNichkjAZ9BY0LqtAkhyiudNdm69zS3XndZUc0p3axmg0/OtOgbyFy0o+iF2LbNe3tO8vJbh8jlLQwD1i5vZe2KefgnuWgX8Bs0JSKTfr0QQhRr39Ez5PIWNyhvP/VADQefM8mJBZ5UOsdzr+1n98FeABrjIT6/rp1Fc+onfQ3BgI+meMiz2SZCiNqiD/cSDftZPM9dXUkno2aDz0RSmDu7zvB0Ryf9g1kAVl45i0/dtIBoePLDCwX9NMalQKgQonIOnRigvbXR81NuUMPBpximZfGb3x7h1W1d2DitDO65ZRHrP7GI3t7UpN83HPTTIIFHCFFhJ3pS3LR0drUvoyJcG3y6+9Ns/M1ejpwaBKC1JcaGde3MbIhOKT0xEnICjyHlcoQQFWbbTGmpwE1cF3xs22br3tO8sOUA2ZyFAdy2bC7rb5g35UdVaYkghKi2hXO80a9nPK4KPulsnudfP8COzm4A6uuCPLC2nfbWqS/OSUsEIUS1RUIBmhLhal9GRbgm+Bw6PsBTHfvoHcgAsHRhE59b00YsEpzye9dFAtRL4BFCVFljYvrch2o++JiWzeatR+l4/wiW7ey7+fTqRaxaOmvKpScMIFYXJB6p+R+DEGIaaIxPj6ceqPHg05fMsHHTPg4dHwBgTnMdG9a1M7u5bsrvbRgQrwsRl5YIQoga0STBp/r2HO7jZ69o0lmnntvN18zhk6sWECxBbTVpiSCEqEWxadQRuWZH+uKWA6SzJrFokAfWtKEWNJXkfaUlghCiVtWFp76G7RY1G3wAlsxv4P41V5QsC01aIgghalk0PH0+FNds8Fm3Yh7XtDWXrMqAtEQQQtS6SKhmb8klV9aRKqUeBr4FBIG/1Vr/sNjXrlQtEyoseinSEkEI4QbT6cNx2UaqlGoFvgfcClwPfFUpdVW5zncxgYBBU31YAo8Qoub5p9F9qpxhdj2wSWvdo7UeBJ4BHijj+c4TDPhojofx+6bPpwkhhHv5p0E162HlnHabCxwb9fUxYFWxL25srMO0Jp8HPbslQVN9pCSp2bWmpcWbtZ9kXO7j1bFVa1zNjTHP/kzPVc7g4wNGRw8DKHoRp68vNek1n9mzEti5PH29g5N6fS1raUlw6tRAtS+j5GRc7uPVsZV6XBMJJoPJNKdOeWfq7VJjL+djwRHgslFfzwG6yng+ACJhPzMaotKLRwjhOr5ptOZTziefXwPfUUq1AIPA/cBXy3i+kcrUfml7LYRwocA0uneV7clHa30U+HOgA9gGPKm1frcc53LqtAWprwtKCzghhGt5cY36Ysq6z0dr/STwZDnPYRiQiIWIhaVOmxDC3RJ1IawpJFq5iau3046u0yaBRwgh3MO1wcfnM2iUcjlCCOFKrgw+AZ9BQyJMcBptyBJCCC9xXfAJBAya4hHJaBNCCBdzVfAJBnw0xcP4JPAIIYSruSb4hIN+GuIh2TwqhBAe4IrgEwk5gceQXTxCCOEJNR98ouEADbHSdDIVQghRG2o6+AyXyxFCCOEtNRt8YtEgkaCkUgshhBfV7N09FgmArPEIIYQn1WzwkXI5QgjhXTUbfIQQQniXBB8hhBAVJ8FHCCFExUnwEUIIUXESfIQQQlScBB8hhBAVJ8FHCCFExUnwEUIIUXESfIQQQlScBB8hhBAVJ8FHCCFExdViVWs/MOVW2V5ute3Vscm43MerYyvxuBYBR4B8Kd/U7Qy79ip43gq8Xu2LEEKIErocOHiJv18EHCjiOM+oxeATBm4EjgFmla9FCCFKYbwnnwAwr4jjPKMWg48QQgiPk4QDIYQQFSfBRwghRMVJ8BFCCFFxEnyEEEJUnAQfIYQQFSfBRwghRMVJ8BFCCFFxEnyEEEJUXC3WdpsSpdTDwLeAIPC3WusfVvmSpkQpVQ+8CXxGa31QKbUeeAyIAhu11t+q6gVOglLq28DnC1++rLX+Uy+MC0Ap9V3gAcAGfqy1fswrYwNQSv0PYKbW+hGl1PXAPwH1wGvAH2mtXbU7XynVAcwCcoVv/XvgCjx0D6lVnnryUUq1At/DqQ93PfBVpdRV1b2qyVNK3QS8ASwpfB0FfgJ8FlgK3KiUurt6VzhxhRvx7wHLcX5HK5VSD+HycQEopdYA64DrgBuA/6iUWoYHxgaglLoD+NKobz0BfE1rvQQwgK9U5cImSSll4Py/tUxrfb3W+nqc8jaeuYfUMk8FH2A9sElr3aO1HgSewfkU6lZfAf4D0FX4ehWwV2t9oPAJ8wngwWpd3CQdA76htc5qrXPAhzg3ALePC631q8Dawhhm4cwsNOKBsSmlmnFuyn9Z+HohENVav1045P/gvnGpwn9fUUptV0p9De/dQ2qW14LPXJyb27BjOMX6XElr/WWt9egK364fn9Z61/ANSym1GGf6zcLl4xqmtc4ppf4C2A38Bg/8zgr+AfhzoLfwtRfG1YTzO/p94A7gj4AFuH9cruC14OPDmWsfZuDc2LzCM+NTSl0N/CvwKLAfj4wLQGv9baAFmI/zVOfqsSmlvgx8rLX+zahvu/7fotb6La31H2itz2itTwM/Br6Ly8flFl4LPkeAy0Z9PYezU1Ze4InxKaVuwfnE+V+11o/jnXFdWViER2udAp4Dbsf9Y9sA/J5SahvOzfle4Mu4fFxKqVsL61jDDJxeOq4el1t4Ldvt18B3lFItwCBwP/DV6l5SSb0DKKVUO07jqYdxFrNdQyk1H3ge2KC13lT4tuvHVdAG/IVS6lacT8+fxZmu+hs3j01rfefwn5VSjwC3a63/UCm1Uyl1i9Z6C/DvgF9U6xonqRH4rlLqZpzMti8B/xZ4wsP3kJrhqScfrfVRnHnpDmAb8KTW+t3qXlXpaK3TwCPAszhrCntwFkTd5JtABHhMKbWt8Gn6Edw/LrTWPwdeBrYCvwPe1Fr/Mx4Y20V8EfiBUmoPEAf+rsrXMyFa65cY+/v6SSGQevYeUkukmZwQQoiK89STjxBCCHeQ4COEEKLiJPgIIYSoOAk+QgghKk6CjxBCiIqT4COEEKLiJPgIV1BKvaKUmlmCY25XSu0s4nz2hd5LKXWvUurvCn/erJR6QCm1SCmVHO89hRBnea3CgfCuO8c/pKhjpkRr/QLwQrnPI4TXSfARNU8p9b8Lf+wolL3/DjADp4TN97XWPz3nmE8By4D/BoRw2hs8rrX+7xM89feUUjfizBB8S2v9UqG8zANa689MaVBCTHMy7SZqntb6Dwt/XItTF+3vtdbXAXcDf6mUWn3OMUeAbwBf0lrfAHwC+LPxpuQuYL/WegVOva/HC/W+hBAlIMFHuMlVQERr/RyA1roLp2baXaMP0lrbwD04XVK/jdPC2gBiEzzfjwrvtxOnLtvqKV29EGKEBB/hJjZje62A8284OPobSqkYTrHIFcD7OD2DcjgBaCLMc86Tm+DrhRAXIcFHuIWJ02slp5T6HIBSai5Oyft/HXVMEFgM1OOs07yI01MnDPgneM5HCudZAbTjtH4QQpSABB/hFk/jNKC7D/i6UmoHTv+m72qtO0Yd8ypO58mXgD1KqQ9xpuB24wSQiWhTSm0F/gn4gta6Z+rDEEKAtFQQQghRBZJqLaYlpdSjOM3QLuRvtNY/q+T1CDHdyJOPEEKIipM1HyGEEBUnwUcIIUTFSfARQghRcRJ8hBBCVNz/B0io24cYBjoZAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x432 with 3 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.jointplot(x=\"total_bill\", y=\"tip\", data=tips, kind=\"reg\")"
+    "sns.jointplot(x=\"total_bill\", y=\"tip\", data=df, kind=\"reg\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223a9bfa08>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223f419c88>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -883,27 +1294,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2223bbf5848>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2223f521c08>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -920,52 +1333,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x432 with 3 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "fig = sns.distplot(tips[\"tip\"])"
+    "fig = sns.jointplot(x=\"total_bill\", y=\"tip\", data=df, kind=\"reg\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "matplotlib.axes._subplots.AxesSubplot"
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'annotate',\n",
+       " 'ax_joint',\n",
+       " 'ax_marg_x',\n",
+       " 'ax_marg_y',\n",
+       " 'fig',\n",
+       " 'plot',\n",
+       " 'plot_joint',\n",
+       " 'plot_marginals',\n",
+       " 'savefig',\n",
+       " 'set_axis_labels',\n",
+       " 'x',\n",
+       " 'y']"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(fig)"
+    "type(fig)\n",
+    "dir(fig)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig1 = fig.get_figure() \n",
-    "fig1.savefig(\"test.png\")"
+    "fig.savefig(\"test.png\")"
    ]
   },
   {
@@ -1431,6 +1883,269 @@
     "dir(fig)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "sns.set()\n",
+    "\n",
+    "# Load the brain networks example dataset\n",
+    "df = sns.load_dataset(\"brain_networks\", header=[0, 1, 2], index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>network</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">3</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">4</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">5</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">16</th>\n",
+       "      <th colspan=\"7\" halign=\"left\">17</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>node</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>3</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">4</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hemi</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>...</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "      <th>rh</th>\n",
+       "      <th>lh</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>915</td>\n",
+       "      <td>-7.429513</td>\n",
+       "      <td>-4.813219</td>\n",
+       "      <td>-3.670537</td>\n",
+       "      <td>1.442261</td>\n",
+       "      <td>-19.680431</td>\n",
+       "      <td>-29.109356</td>\n",
+       "      <td>-3.376562</td>\n",
+       "      <td>-8.764893</td>\n",
+       "      <td>0.017912</td>\n",
+       "      <td>-25.305094</td>\n",
+       "      <td>...</td>\n",
+       "      <td>58.285793</td>\n",
+       "      <td>68.557411</td>\n",
+       "      <td>22.833048</td>\n",
+       "      <td>76.179489</td>\n",
+       "      <td>51.934669</td>\n",
+       "      <td>-6.614513</td>\n",
+       "      <td>-6.690762</td>\n",
+       "      <td>22.893030</td>\n",
+       "      <td>48.274380</td>\n",
+       "      <td>76.228455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>916</td>\n",
+       "      <td>-33.554138</td>\n",
+       "      <td>-38.605621</td>\n",
+       "      <td>-25.664803</td>\n",
+       "      <td>-30.252352</td>\n",
+       "      <td>5.562785</td>\n",
+       "      <td>20.182186</td>\n",
+       "      <td>17.911247</td>\n",
+       "      <td>24.653582</td>\n",
+       "      <td>-32.935612</td>\n",
+       "      <td>-21.783203</td>\n",
+       "      <td>...</td>\n",
+       "      <td>59.864819</td>\n",
+       "      <td>23.004578</td>\n",
+       "      <td>7.657463</td>\n",
+       "      <td>50.962399</td>\n",
+       "      <td>13.696922</td>\n",
+       "      <td>63.503616</td>\n",
+       "      <td>57.401176</td>\n",
+       "      <td>24.974548</td>\n",
+       "      <td>51.972153</td>\n",
+       "      <td>64.538788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>917</td>\n",
+       "      <td>-78.539566</td>\n",
+       "      <td>-74.197189</td>\n",
+       "      <td>-54.041595</td>\n",
+       "      <td>-39.970291</td>\n",
+       "      <td>-14.277059</td>\n",
+       "      <td>-30.606461</td>\n",
+       "      <td>60.628521</td>\n",
+       "      <td>62.027023</td>\n",
+       "      <td>-32.800556</td>\n",
+       "      <td>-37.021500</td>\n",
+       "      <td>...</td>\n",
+       "      <td>68.767868</td>\n",
+       "      <td>49.664017</td>\n",
+       "      <td>64.551498</td>\n",
+       "      <td>43.800747</td>\n",
+       "      <td>8.144480</td>\n",
+       "      <td>47.281460</td>\n",
+       "      <td>70.499649</td>\n",
+       "      <td>66.994400</td>\n",
+       "      <td>81.539246</td>\n",
+       "      <td>64.969772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>918</td>\n",
+       "      <td>-103.235825</td>\n",
+       "      <td>-98.744286</td>\n",
+       "      <td>-40.109543</td>\n",
+       "      <td>-44.907734</td>\n",
+       "      <td>12.109148</td>\n",
+       "      <td>1.621340</td>\n",
+       "      <td>33.765560</td>\n",
+       "      <td>55.356071</td>\n",
+       "      <td>-14.330512</td>\n",
+       "      <td>-17.224781</td>\n",
+       "      <td>...</td>\n",
+       "      <td>103.155251</td>\n",
+       "      <td>106.454849</td>\n",
+       "      <td>9.046827</td>\n",
+       "      <td>46.674419</td>\n",
+       "      <td>40.954796</td>\n",
+       "      <td>0.877180</td>\n",
+       "      <td>37.577152</td>\n",
+       "      <td>20.517746</td>\n",
+       "      <td>3.124434</td>\n",
+       "      <td>56.718388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>919</td>\n",
+       "      <td>-36.288868</td>\n",
+       "      <td>-10.762070</td>\n",
+       "      <td>-30.356262</td>\n",
+       "      <td>-23.319504</td>\n",
+       "      <td>14.252188</td>\n",
+       "      <td>-27.559860</td>\n",
+       "      <td>17.279512</td>\n",
+       "      <td>-19.060152</td>\n",
+       "      <td>26.558777</td>\n",
+       "      <td>21.377319</td>\n",
+       "      <td>...</td>\n",
+       "      <td>102.086304</td>\n",
+       "      <td>80.051140</td>\n",
+       "      <td>-2.642610</td>\n",
+       "      <td>-12.229620</td>\n",
+       "      <td>-6.596726</td>\n",
+       "      <td>17.665163</td>\n",
+       "      <td>16.153173</td>\n",
+       "      <td>8.300399</td>\n",
+       "      <td>33.687531</td>\n",
+       "      <td>17.960655</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 62 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "network           1                     2                     3             \\\n",
+       "node              1                     1                     1              \n",
+       "hemi             lh         rh         lh         rh         lh         rh   \n",
+       "915       -7.429513  -4.813219  -3.670537   1.442261 -19.680431 -29.109356   \n",
+       "916      -33.554138 -38.605621 -25.664803 -30.252352   5.562785  20.182186   \n",
+       "917      -78.539566 -74.197189 -54.041595 -39.970291 -14.277059 -30.606461   \n",
+       "918     -103.235825 -98.744286 -40.109543 -44.907734  12.109148   1.621340   \n",
+       "919      -36.288868 -10.762070 -30.356262 -23.319504  14.252188 -27.559860   \n",
+       "\n",
+       "network          4                     5             ...          16  \\\n",
+       "node             1                     1             ...           3   \n",
+       "hemi            lh         rh         lh         rh  ...          rh   \n",
+       "915      -3.376562  -8.764893   0.017912 -25.305094  ...   58.285793   \n",
+       "916      17.911247  24.653582 -32.935612 -21.783203  ...   59.864819   \n",
+       "917      60.628521  62.027023 -32.800556 -37.021500  ...   68.767868   \n",
+       "918      33.765560  55.356071 -14.330512 -17.224781  ...  103.155251   \n",
+       "919      17.279512 -19.060152  26.558777  21.377319  ...  102.086304   \n",
+       "\n",
+       "network                                17                                   \\\n",
+       "node              4                     1                     2              \n",
+       "hemi             lh         rh         lh         rh         lh         rh   \n",
+       "915       68.557411  22.833048  76.179489  51.934669  -6.614513  -6.690762   \n",
+       "916       23.004578   7.657463  50.962399  13.696922  63.503616  57.401176   \n",
+       "917       49.664017  64.551498  43.800747   8.144480  47.281460  70.499649   \n",
+       "918      106.454849   9.046827  46.674419  40.954796   0.877180  37.577152   \n",
+       "919       80.051140  -2.642610 -12.229620  -6.596726  17.665163  16.153173   \n",
+       "\n",
+       "network                                   \n",
+       "node             3                     4  \n",
+       "hemi            lh         rh         lh  \n",
+       "915      22.893030  48.274380  76.228455  \n",
+       "916      24.974548  51.972153  64.538788  \n",
+       "917      66.994400  81.539246  64.969772  \n",
+       "918      20.517746   3.124434  56.718388  \n",
+       "919       8.300399  33.687531  17.960655  \n",
+       "\n",
+       "[5 rows x 62 columns]"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 50,
@@ -1458,12 +2173,6 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "sns.set()\n",
-    "\n",
-    "# Load the brain networks example dataset\n",
-    "df = sns.load_dataset(\"brain_networks\", header=[0, 1, 2], index_col=0)\n",
-    "\n",
     "# Select a subset of the networks\n",
     "used_networks = [1, 5, 6, 7, 8, 12, 13, 17]\n",
     "used_columns = (df.columns.get_level_values(\"network\")\n",
diff --git a/Lecture 6 - Saving Object.ipynb b/lectures/Lecture 6 - Saving Object.ipynb
similarity index 100%
rename from Lecture 6 - Saving Object.ipynb
rename to lectures/Lecture 6 - Saving Object.ipynb
diff --git a/lectures/Lecture 6 - Saving objects.ipynb b/lectures/Lecture 6 - Saving objects.ipynb
index e5dc188..9b65ea3 100644
--- a/lectures/Lecture 6 - Saving objects.ipynb	
+++ b/lectures/Lecture 6 - Saving objects.ipynb	
@@ -15,31 +15,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "class Dog:\n",
-    "    \n",
     "    def __init__(self, name):\n",
     "        self.name = name\n",
     "        \n",
     "d = Dog(\"Rex\")\n",
     "\n",
-    "assert hasattr(d, \"name\"), \"this object doesn't have attribuet namee\""
+    "assert hasattr(d, \"name\"), \"this object doesn't have attribuet name!\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +39,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open('test_pickle_dog.pkl', 'wb') \n",
+    "pickle.dump(d, file)\n",
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open('test_pickle_dog.pkl', 'rb') \n",
+    "test = pickle.load(file)\n",
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Rex'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -77,7 +119,7 @@
        "['a', 'b']"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -88,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -106,10 +148,10 @@
      "evalue": "name 'test' is not defined",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-143-4e1243bd22c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'test' is not defined"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-16-4e1243bd22c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtest\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'test' is not defined"
      ]
     }
    ],
@@ -119,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -139,7 +181,7 @@
        "['a', 'b']"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -157,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -184,10 +226,10 @@
      "evalue": "name 'item' is not defined",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-154-8cd64e89cea0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'item' is not defined"
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-21-8cd64e89cea0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mitem\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'item' is not defined"
      ]
     }
    ],
@@ -198,12 +240,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "with open(\"test_pickle.pkl\", \"rb\") as file:\n",
-    "    item = pickle.load(file)"
+    "    item = pickle.load(file)\n",
+    "    file.close()"
    ]
   },
   {
@@ -230,10 +273,73 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h3 style=\"color:blue\">make a python object</h3>\n",
+    "<h3 style=\"color:blue\">make a python graph object</h3>\n",
     "<p style=\"color:blue\">- save the object</p>\n",
     "<p style=\"color:blue\">- delete the object from memory</p>\n",
-    "<p style=\"color:blue\">- reload it</p>"
+    "<p style=\"color:blue\">- reload it</p>\n",
+    "<p style=\"color:blue\">- plot it</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# http://hr.gs/msca37014\n",
+    "# password = msca37014"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "0\n",
+      "0\n",
+      "2\n",
+      "10\n",
+      "4\n",
+      "40\n",
+      "92\n",
+      "352\n",
+      "724\n",
+      "2680\n",
+      "14200\n",
+      "73712\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-29-3666e5ab0a52>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmsca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnqueens\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraf_nq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolutions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, size, show)\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolutions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36msolve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mpositions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolutions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     17\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mput_queen\u001b[1;34m(self, positions, target_row)\u001b[0m\n\u001b[0;32m     15\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_place\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m                     \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget_row\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mput_queen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_row\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\raves\\documents\\github\\python_for_analytics\\msca\\nqueens.py\u001b[0m in \u001b[0;36mcheck_place\u001b[1;34m(self, positions, ocuppied_rows, column)\u001b[0m\n\u001b[0;32m     22\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mocuppied_rows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32mor\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m                 \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mocuppied_rows\u001b[0m \u001b[1;32mor\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m                 \u001b[0mpositions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mocuppied_rows\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "for a in range(1,15):\n",
+    "    b = msca.nqueens.raf_nq(a)\n",
+    "    print(b, b.solutions)"
    ]
   },
   {
diff --git a/lectures/Lecture 7 - Logistic Regression, Feature Extraction and Transformation.ipynb b/lectures/Lecture 7 - Logistic Regression, Feature Extraction and Transformation.ipynb
index ea34382..0d7cc31 100644
--- a/lectures/Lecture 7 - Logistic Regression, Feature Extraction and Transformation.ipynb	
+++ b/lectures/Lecture 7 - Logistic Regression, Feature Extraction and Transformation.ipynb	
@@ -51,19 +51,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, classification_report"
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, classification_report\n",
+    "import msca"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'msca' from 'c:\\\\users\\\\raves\\\\documents\\\\github\\\\python_for_analytics\\\\msca\\\\__init__.py'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "msca"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -105,50 +126,86 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <td>145</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>146</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>147</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>148</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>149</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Virginica</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
-       "0           5.1          3.5           1.4          0.2  Setosa"
+       "     sepal.length  sepal.width  petal.length  petal.width    variety\n",
+       "145           6.7          3.0           5.2          2.3  Virginica\n",
+       "146           6.3          2.5           5.0          1.9  Virginica\n",
+       "147           6.5          3.0           5.2          2.0  Virginica\n",
+       "148           6.2          3.4           5.4          2.3  Virginica\n",
+       "149           5.9          3.0           5.1          1.8  Virginica"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head(1)"
+    "df.tail(5)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### we will make a binary classificaiton, where 1 is the Setosa class and 0 is all other classes"
+    "#### we will make a binary classification, where 1 is the Setosa class and 0 is all other classes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df[\"classification\"] = np.where(df[\"variety\"] == \"Setosa\",1, 0)"
+    "df[\"classification\"] = np.where(df[\"variety\"] == \"Setosa\",0, 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -182,48 +239,48 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>128</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>Virginica</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>77</td>\n",
-       "      <td>6.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>6.1</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.7</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>1.4</td>\n",
        "      <td>Versicolor</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4.7</td>\n",
+       "      <td>120</td>\n",
+       "      <td>6.9</td>\n",
        "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Setosa</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Virginica</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>58</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>Versicolor</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>136</td>\n",
+       "      <td>72</td>\n",
        "      <td>6.3</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>Virginica</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Versicolor</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Setosa</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -232,21 +289,21 @@
       ],
       "text/plain": [
        "     sepal.length  sepal.width  petal.length  petal.width     variety  \\\n",
-       "128           6.4          2.8           5.6          2.1   Virginica   \n",
-       "77            6.7          3.0           5.0          1.7  Versicolor   \n",
-       "2             4.7          3.2           1.3          0.2      Setosa   \n",
-       "58            6.6          2.9           4.6          1.3  Versicolor   \n",
-       "136           6.3          3.4           5.6          2.4   Virginica   \n",
+       "91            6.1          3.0           4.6          1.4  Versicolor   \n",
+       "120           6.9          3.2           5.7          2.3   Virginica   \n",
+       "7             5.0          3.4           1.5          0.2      Setosa   \n",
+       "72            6.3          2.5           4.9          1.5  Versicolor   \n",
+       "25            5.0          3.0           1.6          0.2      Setosa   \n",
        "\n",
        "     classification  \n",
-       "128               0  \n",
-       "77                0  \n",
-       "2                 1  \n",
-       "58                0  \n",
-       "136               0  "
+       "91                1  \n",
+       "120               1  \n",
+       "7                 0  \n",
+       "72                1  \n",
+       "25                0  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -303,17 +360,17 @@
       "149           5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]\n",
-      "0      1\n",
-      "1      1\n",
-      "2      1\n",
-      "3      1\n",
-      "4      1\n",
+      "0      0\n",
+      "1      0\n",
+      "2      0\n",
+      "3      0\n",
+      "4      0\n",
       "      ..\n",
-      "145    0\n",
-      "146    0\n",
-      "147    0\n",
-      "148    0\n",
-      "149    0\n",
+      "145    1\n",
+      "146    1\n",
+      "147    1\n",
+      "148    1\n",
+      "149    1\n",
       "Name: classification, Length: 150, dtype: int32\n"
      ]
     }
@@ -332,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -344,7 +401,7 @@
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)\n",
-      "<function sum at 0x000002A41897D828>\n"
+      "<function sum at 0x00000189962D1948>\n"
      ]
     }
    ],
@@ -365,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -386,7 +443,7 @@
        "                   warm_start=False)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,22 +471,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -440,22 +497,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -473,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -482,7 +539,7 @@
        "1.0"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -500,16 +557,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.41021713,  1.46416217, -2.26003266, -1.02103509]])"
+       "array([[-0.41021713, -1.46416217,  2.26003266,  1.02103509]])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -528,17 +585,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('sepal.length', 0.41021712519841536)\n",
+      "('sepal.length', 0.410217125198415)\n",
       "('sepal.width', 1.4641621652467662)\n",
-      "('petal.length', -2.260032661311069)\n",
-      "('petal.width', -1.0210350909174157)\n"
+      "('petal.length', -2.260032661311068)\n",
+      "('petal.width', -1.0210350909174162)\n"
      ]
     }
    ],
@@ -584,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -714,7 +771,7 @@
        "[150 rows x 4 columns]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -732,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -742,7 +799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -751,7 +808,7 @@
        "-6.315999999999999"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -763,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -772,7 +829,7 @@
        "3.9979999999999998"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -813,7 +870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -822,7 +879,7 @@
        "-433.2630461266474"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -833,7 +890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -842,44 +899,44 @@
        "array([[5.1, 3.5, 1.4, 0.2]])"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.array(x.head(1))\n",
-    "here = os.getcwd()"
+    "np.array(x.head(1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
     "def pred(features, coefs, intercept):\n",
-    "    return intercept + np.sum(np.multiply(features, coefs))"
+    "    a = intercept + np.sum(np.multiply(features, coefs))\n",
+    "    return a > 0 "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-6.287043672009523"
+       "True"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "p = pred(np.array(x.iloc[149,:]), reg.coef_[0], reg.intercept_[0])\n",
+    "p = pred(np.array(x.iloc[2,:]), reg.coef_[0], reg.intercept_[0])\n",
     "p"
    ]
   },
@@ -896,17 +953,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.01610102, 0.98389898],\n",
-       "       [0.03562213, 0.96437787]])"
+       "array([[0.98389898, 0.01610102],\n",
+       "       [0.96437787, 0.03562213]])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -924,7 +981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -957,38 +1014,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>2.092107</td>\n",
-       "      <td>5.124568</td>\n",
-       "      <td>-3.164046</td>\n",
-       "      <td>-0.204207</td>\n",
+       "      <td>-2.092107</td>\n",
+       "      <td>-5.124568</td>\n",
+       "      <td>3.164046</td>\n",
+       "      <td>0.204207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>2.010064</td>\n",
-       "      <td>4.392486</td>\n",
-       "      <td>-3.164046</td>\n",
-       "      <td>-0.204207</td>\n",
+       "      <td>-2.010064</td>\n",
+       "      <td>-4.392486</td>\n",
+       "      <td>3.164046</td>\n",
+       "      <td>0.204207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>1.928020</td>\n",
-       "      <td>4.685319</td>\n",
-       "      <td>-2.938042</td>\n",
-       "      <td>-0.204207</td>\n",
+       "      <td>-1.928020</td>\n",
+       "      <td>-4.685319</td>\n",
+       "      <td>2.938042</td>\n",
+       "      <td>0.204207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>1.886999</td>\n",
-       "      <td>4.538903</td>\n",
-       "      <td>-3.390049</td>\n",
-       "      <td>-0.204207</td>\n",
+       "      <td>-1.886999</td>\n",
+       "      <td>-4.538903</td>\n",
+       "      <td>3.390049</td>\n",
+       "      <td>0.204207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
-       "      <td>2.051086</td>\n",
-       "      <td>5.270984</td>\n",
-       "      <td>-3.164046</td>\n",
-       "      <td>-0.204207</td>\n",
+       "      <td>-2.051086</td>\n",
+       "      <td>-5.270984</td>\n",
+       "      <td>3.164046</td>\n",
+       "      <td>0.204207</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -996,14 +1053,14 @@
       ],
       "text/plain": [
        "   sepal.length  sepal.width  petal.length  petal.width\n",
-       "0      2.092107     5.124568     -3.164046    -0.204207\n",
-       "1      2.010064     4.392486     -3.164046    -0.204207\n",
-       "2      1.928020     4.685319     -2.938042    -0.204207\n",
-       "3      1.886999     4.538903     -3.390049    -0.204207\n",
-       "4      2.051086     5.270984     -3.164046    -0.204207"
+       "0     -2.092107    -5.124568      3.164046     0.204207\n",
+       "1     -2.010064    -4.392486      3.164046     0.204207\n",
+       "2     -1.928020    -4.685319      2.938042     0.204207\n",
+       "3     -1.886999    -4.538903      3.390049     0.204207\n",
+       "4     -2.051086    -5.270984      3.164046     0.204207"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1016,21 +1073,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0    3.848422\n",
-       "1    3.034298\n",
-       "2    3.471090\n",
-       "3    2.831645\n",
-       "4    3.953817\n",
+       "0   -3.848422\n",
+       "1   -3.034298\n",
+       "2   -3.471090\n",
+       "3   -2.831645\n",
+       "4   -3.953817\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1041,36 +1098,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import cv2"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0      4.112641\n",
-       "1      3.298516\n",
-       "2      3.735308\n",
-       "3      3.095864\n",
-       "4      4.218035\n",
+       "0     -4.112641\n",
+       "1     -3.298516\n",
+       "2     -3.735308\n",
+       "3     -3.095864\n",
+       "4     -4.218035\n",
        "         ...   \n",
-       "145   -6.695391\n",
-       "146   -6.731138\n",
-       "147   -6.471124\n",
-       "148   -6.766841\n",
-       "149   -6.287044\n",
+       "145    6.695391\n",
+       "146    6.731138\n",
+       "147    6.471124\n",
+       "148    6.766841\n",
+       "149    6.287044\n",
        "Length: 150, dtype: float64"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1081,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1091,7 +1146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -1100,7 +1155,7 @@
        "0.008149369173106819"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1112,7 +1167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -1121,7 +1176,7 @@
        "0.9918506308268932"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1132,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1142,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 83,
    "metadata": {
     "scrolled": true
    },
@@ -1175,53 +1230,53 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>0.983899</td>\n",
        "      <td>0.016101</td>\n",
+       "      <td>0.983899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>0.964378</td>\n",
        "      <td>0.035622</td>\n",
+       "      <td>0.964378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>0.976690</td>\n",
        "      <td>0.023310</td>\n",
+       "      <td>0.976690</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>0.956722</td>\n",
        "      <td>0.043278</td>\n",
+       "      <td>0.956722</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
-       "      <td>0.985486</td>\n",
        "      <td>0.014514</td>\n",
+       "      <td>0.985486</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>5</td>\n",
-       "      <td>0.980907</td>\n",
        "      <td>0.019093</td>\n",
+       "      <td>0.980907</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>6</td>\n",
-       "      <td>0.974889</td>\n",
        "      <td>0.025111</td>\n",
+       "      <td>0.974889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>7</td>\n",
-       "      <td>0.975854</td>\n",
        "      <td>0.024146</td>\n",
+       "      <td>0.975854</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>8</td>\n",
-       "      <td>0.950121</td>\n",
        "      <td>0.049879</td>\n",
+       "      <td>0.950121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>9</td>\n",
-       "      <td>0.965143</td>\n",
        "      <td>0.034857</td>\n",
+       "      <td>0.965143</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1229,19 +1284,19 @@
       ],
       "text/plain": [
        "   positive  negative\n",
-       "0  0.983899  0.016101\n",
-       "1  0.964378  0.035622\n",
-       "2  0.976690  0.023310\n",
-       "3  0.956722  0.043278\n",
-       "4  0.985486  0.014514\n",
-       "5  0.980907  0.019093\n",
-       "6  0.974889  0.025111\n",
-       "7  0.975854  0.024146\n",
-       "8  0.950121  0.049879\n",
-       "9  0.965143  0.034857"
+       "0  0.016101  0.983899\n",
+       "1  0.035622  0.964378\n",
+       "2  0.023310  0.976690\n",
+       "3  0.043278  0.956722\n",
+       "4  0.014514  0.985486\n",
+       "5  0.019093  0.980907\n",
+       "6  0.025111  0.974889\n",
+       "7  0.024146  0.975854\n",
+       "8  0.049879  0.950121\n",
+       "9  0.034857  0.965143"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1293,7 +1348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -1342,7 +1397,7 @@
        "0           5.1          3.5           1.4          0.2  Setosa"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1354,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -1367,7 +1422,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1387,7 +1442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1396,7 +1451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1405,7 +1460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -1452,7 +1507,7 @@
        "0           5.1          3.5           1.4          0.2"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1463,7 +1518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -1472,7 +1527,7 @@
        "StandardScaler(copy=True, with_mean=True, with_std=True)"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1484,114 +1539,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 94,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal.length</th>\n",
-       "      <th>sepal.width</th>\n",
-       "      <th>petal.length</th>\n",
-       "      <th>petal.width</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>count</td>\n",
-       "      <td>1.500000e+02</td>\n",
-       "      <td>1.500000e+02</td>\n",
-       "      <td>1.500000e+02</td>\n",
-       "      <td>1.500000e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>mean</td>\n",
-       "      <td>-2.775558e-16</td>\n",
-       "      <td>-9.695948e-16</td>\n",
-       "      <td>-8.652338e-16</td>\n",
-       "      <td>-4.662937e-16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>std</td>\n",
-       "      <td>1.003350e+00</td>\n",
-       "      <td>1.003350e+00</td>\n",
-       "      <td>1.003350e+00</td>\n",
-       "      <td>1.003350e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>min</td>\n",
-       "      <td>-1.870024e+00</td>\n",
-       "      <td>-2.433947e+00</td>\n",
-       "      <td>-1.567576e+00</td>\n",
-       "      <td>-1.447076e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>25%</td>\n",
-       "      <td>-9.006812e-01</td>\n",
-       "      <td>-5.923730e-01</td>\n",
-       "      <td>-1.226552e+00</td>\n",
-       "      <td>-1.183812e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>50%</td>\n",
-       "      <td>-5.250608e-02</td>\n",
-       "      <td>-1.319795e-01</td>\n",
-       "      <td>3.364776e-01</td>\n",
-       "      <td>1.325097e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>75%</td>\n",
-       "      <td>6.745011e-01</td>\n",
-       "      <td>5.586108e-01</td>\n",
-       "      <td>7.627583e-01</td>\n",
-       "      <td>7.906707e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>max</td>\n",
-       "      <td>2.492019e+00</td>\n",
-       "      <td>3.090775e+00</td>\n",
-       "      <td>1.785832e+00</td>\n",
-       "      <td>1.712096e+00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       sepal.length   sepal.width  petal.length   petal.width\n",
-       "count  1.500000e+02  1.500000e+02  1.500000e+02  1.500000e+02\n",
-       "mean  -2.775558e-16 -9.695948e-16 -8.652338e-16 -4.662937e-16\n",
-       "std    1.003350e+00  1.003350e+00  1.003350e+00  1.003350e+00\n",
-       "min   -1.870024e+00 -2.433947e+00 -1.567576e+00 -1.447076e+00\n",
-       "25%   -9.006812e-01 -5.923730e-01 -1.226552e+00 -1.183812e+00\n",
-       "50%   -5.250608e-02 -1.319795e-01  3.364776e-01  1.325097e-01\n",
-       "75%    6.745011e-01  5.586108e-01  7.627583e-01  7.906707e-01\n",
-       "max    2.492019e+00  3.090775e+00  1.785832e+00  1.712096e+00"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# note the transform returns a numpy array\n",
     "x_scaler = scaler.transform(x)\n",
@@ -1602,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -1732,7 +1682,7 @@
        "[150 rows x 4 columns]"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1751,7 +1701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1760,7 +1710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
@@ -1863,7 +1813,7 @@
        "max        1.000000     1.000000      1.000000     1.000000"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1937,7 +1887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1946,7 +1896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1960,7 +1910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1969,7 +1919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
@@ -1979,7 +1929,7 @@
        "\twith 21 stored elements in Compressed Sparse Row format>"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1991,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
@@ -2001,7 +1951,7 @@
        "\twith 21 stored elements in Compressed Sparse Row format>"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2012,7 +1962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -2024,7 +1974,7 @@
        "       [0, 1, 1, 1, 0, 0, 1, 0, 1]], dtype=int64)"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2035,7 +1985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2044,7 +1994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -2140,7 +2090,7 @@
        "3    0         1      1   1    0       0    1      0     1"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2151,7 +2101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2160,7 +2110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -2262,7 +2212,7 @@
        "3  0.000000  0.384085  "
       ]
      },
-     "execution_count": 107,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2284,7 +2234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2293,7 +2243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -2356,7 +2306,7 @@
        "2  0  1  1  3"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2369,7 +2319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -2472,7 +2422,7 @@
        "max    0.0  2.000000  4.000000  3.0"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2483,7 +2433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -2494,7 +2444,7 @@
        "       [1, 1]], dtype=int64)"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2507,7 +2457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -2516,7 +2466,7 @@
        "array([0.        , 0.22222222, 2.88888889, 0.        ])"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2527,7 +2477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -2536,7 +2486,7 @@
        "Int64Index([1, 2], dtype='int64')"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2545,6 +2495,292 @@
     "df.columns[selector.get_support()]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_df = pd.DataFrame(tst, columns = df.columns[selector.get_support()])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   1  2\n",
+       "0  2  0\n",
+       "1  1  4\n",
+       "2  1  1"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"../data/iris.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = df.corr().abs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal.length</th>\n",
+       "      <th>sepal.width</th>\n",
+       "      <th>petal.length</th>\n",
+       "      <th>petal.width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>sepal.length</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.117570</td>\n",
+       "      <td>0.871754</td>\n",
+       "      <td>0.817941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>sepal.width</td>\n",
+       "      <td>0.117570</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.428440</td>\n",
+       "      <td>0.366126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>petal.length</td>\n",
+       "      <td>0.871754</td>\n",
+       "      <td>0.428440</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.962865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>petal.width</td>\n",
+       "      <td>0.817941</td>\n",
+       "      <td>0.366126</td>\n",
+       "      <td>0.962865</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              sepal.length  sepal.width  petal.length  petal.width\n",
+       "sepal.length      1.000000     0.117570      0.871754     0.817941\n",
+       "sepal.width       0.117570     1.000000      0.428440     0.366126\n",
+       "petal.length      0.871754     0.428440      1.000000     0.962865\n",
+       "petal.width       0.817941     0.366126      0.962865     1.000000"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.87175378, 0.4284401 , 0.96286543, 0.96286543])"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sort(corr_matrix)[:,-2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_df = pd.DataFrame({\n",
+    "    \"val\":corr_matrix.columns,\n",
+    "    \"max_val\": np.sort(corr_matrix)[:,-2]\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>val</th>\n",
+       "      <th>max_val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>sepal.length</td>\n",
+       "      <td>0.871754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>sepal.width</td>\n",
+       "      <td>0.428440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>petal.length</td>\n",
+       "      <td>0.962865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>petal.width</td>\n",
+       "      <td>0.962865</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            val   max_val\n",
+       "0  sepal.length  0.871754\n",
+       "1   sepal.width  0.428440\n",
+       "2  petal.length  0.962865\n",
+       "3   petal.width  0.962865"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_df"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lectures/Lecture 8 - Decision Tree, Regression, Model Evaluation.ipynb b/lectures/Lecture 8 - Decision Tree, Regression, Model Evaluation.ipynb
index f94db49..6fb6c72 100644
--- a/lectures/Lecture 8 - Decision Tree, Regression, Model Evaluation.ipynb	
+++ b/lectures/Lecture 8 - Decision Tree, Regression, Model Evaluation.ipynb	
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,12 +38,14 @@
     "feature_columns = iris[\"feature_names\"]\n",
     "\n",
     "df = pd.DataFrame(data, columns = feature_columns)\n",
-    "df[\"label\"] = np.array([labels[x] for x in iris[\"target\"]])"
+    "df[\"label\"] = np.array([labels[x] for x in iris[\"target\"]])\n",
+    "x = df.drop(\"label\", 1)\n",
+    "y = df[\"label\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -95,7 +97,7 @@
        "0  setosa  "
       ]
      },
-     "execution_count": 157,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -106,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,6 +117,30 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -126,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +205,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = iris.target\n",
+    "a.reshape(1,-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -190,7 +243,7 @@
        "       [6.85      , 3.07368421, 5.74210526, 2.07105263]])"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -201,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -210,46 +263,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "could not convert string to float: 'setosa'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-126-494b6b06e49c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msilhouette_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkmeans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\cluster\\unsupervised.py\u001b[0m in \u001b[0;36msilhouette_score\u001b[1;34m(X, labels, metric, sample_size, random_state, **kwds)\u001b[0m\n\u001b[0;32m    115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 117\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msilhouette_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\cluster\\unsupervised.py\u001b[0m in \u001b[0;36msilhouette_samples\u001b[1;34m(X, labels, metric, **kwds)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m     \"\"\"\n\u001b[1;32m--> 212\u001b[1;33m     \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'csr'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m     \u001b[0mle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLabelEncoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    717\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 719\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    720\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    494\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m                 \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    497\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m     \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m    946\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    947\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"M8[ns]\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 948\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    949\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    950\u001b[0m     \u001b[1;31m# ----------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m     \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\numpy_.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m     \u001b[0m_HANDLED_TYPES\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m    536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m     \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'setosa'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "score = silhouette_score(y,kmeans.labels_)"
+    "score = silhouette_score(a.reshape(-1,1),kmeans.labels_)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.5528190123564102"
+       "0.7017242160053677"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -260,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,22 +305,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(2, 0.6810461692117465)\n",
-      "(3, 0.5528190123564102)\n",
-      "(4, 0.49805050499728803)\n",
-      "(5, 0.48874888709310654)\n",
-      "(6, 0.36598336415829236)\n",
-      "(7, 0.34521786463700094)\n",
-      "(8, 0.350060295968675)\n",
-      "(9, 0.32233069884845983)\n",
-      "(10, 0.30973766533913044)\n"
+      "(2, 0.681046169211746)\n",
+      "(3, 0.5528190123564091)\n",
+      "(4, 0.4980505049972867)\n",
+      "(5, 0.4887488870931048)\n",
+      "(6, 0.3648340039670018)\n",
+      "(7, 0.3563317372720714)\n",
+      "(8, 0.36179003359737993)\n",
+      "(9, 0.34161854494888316)\n",
+      "(10, 0.32859135923081095)\n"
      ]
     }
    ],
@@ -299,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -361,48 +393,138 @@
        "    <tr>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
        "      <td>4</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   observation  label\n",
-       "0            0      0\n",
-       "1            1      0\n",
-       "2            2      0\n",
-       "3            3      0\n",
-       "4            4      0"
+       "    observation  label\n",
+       "0             0      1\n",
+       "1             1      1\n",
+       "2             2      1\n",
+       "3             3      1\n",
+       "4             4      1\n",
+       "5             5      1\n",
+       "6             6      1\n",
+       "7             7      1\n",
+       "8             8      1\n",
+       "9             9      1\n",
+       "10           10      1\n",
+       "11           11      1\n",
+       "12           12      1\n",
+       "13           13      1\n",
+       "14           14      1\n",
+       "15           15      1\n",
+       "16           16      1\n",
+       "17           17      1\n",
+       "18           18      1\n",
+       "19           19      1"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "assign_df.head(5)"
+    "assign_df.head(20)"
    ]
   },
   {
@@ -414,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -423,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -447,7 +569,7 @@
        "       2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int64)"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -458,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -477,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -486,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -521,42 +643,42 @@
        "    <tr>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
        "      <td>3</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
        "      <td>4</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>...</td>\n",
@@ -577,10 +699,10 @@
        "    <tr>\n",
        "      <td>146</td>\n",
        "      <td>146</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>147</td>\n",
@@ -601,10 +723,10 @@
        "    <tr>\n",
        "      <td>149</td>\n",
        "      <td>149</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -612,23 +734,23 @@
        "</div>"
       ],
       "text/plain": [
-       "     observation  label  agglom_labels   diff  diff1\n",
-       "0              0      0              1  False  False\n",
-       "1              1      0              1  False  False\n",
-       "2              2      0              1  False  False\n",
-       "3              3      0              1  False  False\n",
-       "4              4      0              1  False  False\n",
-       "..           ...    ...            ...    ...    ...\n",
-       "145          145      2              2   True   True\n",
-       "146          146      1              0  False  False\n",
-       "147          147      2              2   True   True\n",
-       "148          148      2              2   True   True\n",
-       "149          149      1              0  False  False\n",
+       "     observation  label  agglom_labels  diff  diff1\n",
+       "0              0      1              1  True   True\n",
+       "1              1      1              1  True   True\n",
+       "2              2      1              1  True   True\n",
+       "3              3      1              1  True   True\n",
+       "4              4      1              1  True   True\n",
+       "..           ...    ...            ...   ...    ...\n",
+       "145          145      2              2  True   True\n",
+       "146          146      0              0  True   True\n",
+       "147          147      2              2  True   True\n",
+       "148          148      2              2  True   True\n",
+       "149          149      0              0  True   True\n",
        "\n",
        "[150 rows x 5 columns]"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -648,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -662,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -671,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -680,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -689,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -785,7 +907,7 @@
        "3    0         1      1   1    0       0    1      0     1"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -796,7 +918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -805,17 +927,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "('This is the first document.', 1)\n",
-      "('This document is the second document.', 1)\n",
-      "('And this is the third one.', 0)\n",
-      "('Is this the first document?', 1)\n"
+      "('This is the first document.', 0)\n",
+      "('This document is the second document.', 0)\n",
+      "('And this is the third one.', 1)\n",
+      "('Is this the first document?', 0)\n"
      ]
     }
    ],
@@ -845,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -861,7 +983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -878,7 +1000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -896,7 +1018,7 @@
        "      dtype=int64)"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -917,76 +1039,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.tree import DecisionTreeClassifier"
+    "\n",
+    "iris = load_iris()\n",
+    "data = iris[\"data\"]\n",
+    "labels = iris[\"target_names\"]\n",
+    "feature_columns = iris[\"feature_names\"]\n",
+    "\n",
+    "df = pd.DataFrame(data, columns = feature_columns)\n",
+    "df[\"label\"] = np.array([labels[x] for x in iris[\"target\"]])\n",
+    "x = df.drop(\"label\", 1)\n",
+    "y = df[\"label\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kmeans = KMeans(3).fit(x)"
+    "from sklearn.tree import DecisionTreeClassifier"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
-    "labels = kmeans.labels_"
+    "dt = DecisionTreeClassifier().fit(x,y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 83,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Number of labels=150 does not match number of samples=4",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-148-77853a8289f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    814\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    815\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 816\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m    817\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    248\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m             raise ValueError(\"Number of labels=%d does not match \"\n\u001b[1;32m--> 250\u001b[1;33m                              \"number of samples=%d\" % (len(y), n_samples))\n\u001b[0m\u001b[0;32m    251\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin_weight_fraction_leaf\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"min_weight_fraction_leaf must in [0, 0.5]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Number of labels=150 does not match number of samples=4"
-     ]
-    }
-   ],
-   "source": []
+   "outputs": [],
+   "source": [
+    "yhat = dt.predict([[1,2,3,4]])"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'dt' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-149-0db4274f6938>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0myhat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m: name 'dt' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "array(['versicolor'], dtype=object)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "yhat = dt.predict(x)"
+    "yhat"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -997,7 +1122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -1030,7 +1155,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <td>0</td>\n",
        "      <td>5.1</td>\n",
        "      <td>3.5</td>\n",
        "      <td>1.4</td>\n",
@@ -1038,7 +1163,7 @@
        "      <td>setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <td>1</td>\n",
        "      <td>4.9</td>\n",
        "      <td>3.0</td>\n",
        "      <td>1.4</td>\n",
@@ -1046,7 +1171,7 @@
        "      <td>setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <td>2</td>\n",
        "      <td>4.7</td>\n",
        "      <td>3.2</td>\n",
        "      <td>1.3</td>\n",
@@ -1054,7 +1179,7 @@
        "      <td>setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <td>3</td>\n",
        "      <td>4.6</td>\n",
        "      <td>3.1</td>\n",
        "      <td>1.5</td>\n",
@@ -1062,7 +1187,7 @@
        "      <td>setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <td>4</td>\n",
        "      <td>5.0</td>\n",
        "      <td>3.6</td>\n",
        "      <td>1.4</td>\n",
@@ -1089,7 +1214,7 @@
        "4  setosa  "
       ]
      },
-     "execution_count": 83,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1100,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1110,7 +1235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1119,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 70,
    "metadata": {
     "scrolled": true
    },
@@ -1154,7 +1279,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <td>0</td>\n",
        "      <td>5.1</td>\n",
        "      <td>3.5</td>\n",
        "      <td>1.4</td>\n",
@@ -1162,7 +1287,7 @@
        "      <td>setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <td>1</td>\n",
        "      <td>4.9</td>\n",
        "      <td>3.0</td>\n",
        "      <td>1.4</td>\n",
@@ -1170,7 +1295,7 @@
        "      <td>setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <td>2</td>\n",
        "      <td>4.7</td>\n",
        "      <td>3.2</td>\n",
        "      <td>1.3</td>\n",
@@ -1193,7 +1318,7 @@
        "2  setosa  "
       ]
      },
-     "execution_count": 90,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1204,7 +1329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -1237,7 +1362,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>147</th>\n",
+       "      <td>147</td>\n",
        "      <td>6.5</td>\n",
        "      <td>3.0</td>\n",
        "      <td>5.2</td>\n",
@@ -1245,7 +1370,7 @@
        "      <td>?</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>148</th>\n",
+       "      <td>148</td>\n",
        "      <td>6.2</td>\n",
        "      <td>3.4</td>\n",
        "      <td>5.4</td>\n",
@@ -1253,7 +1378,7 @@
        "      <td>?</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>149</th>\n",
+       "      <td>149</td>\n",
        "      <td>5.9</td>\n",
        "      <td>3.0</td>\n",
        "      <td>5.1</td>\n",
@@ -1276,7 +1401,7 @@
        "149     ?  "
       ]
      },
-     "execution_count": 91,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1301,7 +1426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1310,7 +1435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -1324,7 +1449,7 @@
        "                       random_state=None, splitter='best')"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1335,9 +1460,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 74,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NotFittedError",
+     "evalue": "This DecisionTreeClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-74-0db4274f6938>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0myhat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    427\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mpredict\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m         \"\"\"\n\u001b[1;32m--> 429\u001b[1;33m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'tree_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    430\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m         \u001b[0mproba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[1;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[0;32m    912\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    913\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mall_or_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mattr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mattributes\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 914\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNotFittedError\u001b[0m: This DecisionTreeClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this method."
+     ]
+    }
+   ],
    "source": [
     "yhat = dt.predict(x)"
    ]
@@ -1900,7 +2039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1910,7 +2049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1919,7 +2058,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1928,7 +2067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -1937,7 +2076,7 @@
        "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1948,7 +2087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
@@ -1972,7 +2111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 90,
    "metadata": {
     "scrolled": true
    },
@@ -2074,7 +2213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2083,7 +2222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2092,7 +2231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -2166,7 +2305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2179,7 +2318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2189,7 +2328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2203,7 +2342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -2271,16 +2410,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 122,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Series' object has no attribute 'aslist'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-122-ab7386fa4af2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maslist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5177\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5178\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5179\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5181\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'aslist'"
+     ]
+    }
+   ],
    "source": [
-    "from sklearn.model_selection import train_test_split"
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2289,7 +2443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
@@ -2303,7 +2457,7 @@
        "                       random_state=None, splitter='best')"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2315,7 +2469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -2329,7 +2483,7 @@
        "       'virginica', 'virginica', 'setosa', 'setosa'], dtype=object)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2347,7 +2501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2356,76 +2510,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
-    "yhat_test = clf.predict(x_test)"
+    "yhat_test = clf.predict(x_test)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "0.3333333333333333"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "accuracy_score(yhat_test, y_test)"
+    "accuracy_score(yhat_test, y[0:30])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\raves\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "0.1111111111111111"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "precision_score(yhat_test, y_test, average = \"weighted\")"
+    "precision_score(yhat_test, y[0:30], average = \"weighted\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "0.3333333333333333"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "recall_score(yhat_test, y_test, average = \"weighted\")"
+    "recall_score(yhat_test, y[0:30], average = \"weighted\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -2433,10 +2595,10 @@
       "text/plain": [
        "array([[10,  0,  0],\n",
        "       [ 0,  9,  0],\n",
-       "       [ 0,  0, 11]])"
+       "       [ 0,  0, 11]], dtype=int64)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2448,7 +2610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
@@ -2479,19 +2641,19 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>setosa</th>\n",
+       "      <td>setosa</td>\n",
        "      <td>10</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>versicolor</th>\n",
+       "      <td>versicolor</td>\n",
        "      <td>0</td>\n",
        "      <td>9</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>virginica</th>\n",
+       "      <td>virginica</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>11</td>\n",
@@ -2507,7 +2669,7 @@
        "virginica        0           0         11"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2518,7 +2680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
@@ -2527,19 +2689,19 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "      setosa       1.00      1.00      1.00        10\n",
-      "  versicolor       1.00      1.00      1.00         9\n",
-      "   virginica       1.00      1.00      1.00        11\n",
+      "      setosa       0.20      0.20      0.20        10\n",
+      "  versicolor       0.15      0.33      0.21         9\n",
+      "   virginica       0.00      0.00      0.00        11\n",
       "\n",
-      "    accuracy                           1.00        30\n",
-      "   macro avg       1.00      1.00      1.00        30\n",
-      "weighted avg       1.00      1.00      1.00        30\n",
+      "    accuracy                           0.17        30\n",
+      "   macro avg       0.12      0.18      0.14        30\n",
+      "weighted avg       0.11      0.17      0.13        30\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "print(classification_report(yhat_test, y_test))"
+    "print(classification_report(yhat_test, y[40:70]))"
    ]
   },
   {
@@ -2560,7 +2722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2583,7 +2745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2593,7 +2755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2602,7 +2764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [
     {
@@ -2673,7 +2835,7 @@
        "C             5           1             1          3"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2688,7 +2850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2697,7 +2859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [
     {
@@ -2708,7 +2870,7 @@
        "       [2.        , 3.60555128, 0.        ]])"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 156,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2719,7 +2881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2729,7 +2891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [
     {
@@ -2795,7 +2957,7 @@
        "C     2.0  3.605551  0.000000"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2813,7 +2975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [
     {
@@ -2879,7 +3041,7 @@
        "C     2  0  1"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2890,7 +3052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [
     {
@@ -2956,7 +3118,7 @@
        "C     C  A  B"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3338,6 +3500,44 @@
     "    * find a way to quantify the similarity"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 'aaaaabbbbbbcccccccdddddd'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "true\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(str(bool(re.search(Regex_Pattern, a))).lower())"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/lectures/Lecture 9 - Multiprocess and Threads.ipynb b/lectures/Lecture 9 - Multiprocess and Threads.ipynb
new file mode 100644
index 0000000..5699e3e
--- /dev/null
+++ b/lectures/Lecture 9 - Multiprocess and Threads.ipynb	
@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import multiprocessing as mp\n",
+    "import time\n",
+    "\n",
+    "def run(x):\n",
+    "  return x**2\n",
+    "\n",
+    "start = time.time()\n",
+    "\n",
+    "vect = list(range(2))\n",
+    "print(vect)\n",
+    "pool = mp.Pool(processes=1)\n",
+    "results = pool.map(run, vect)\n",
+    "print(results)\n",
+    "end = time.time()\n",
+    "print(\"Seconds:{}\".format(end - start))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}