From b25ae93537962d3246f70458294382dea803059b Mon Sep 17 00:00:00 2001
From: sgaruby <31140929+sgaruby@users.noreply.github.com>
Date: Thu, 26 Oct 2017 17:15:22 +0100
Subject: [PATCH 1/3] HiPy Competition - Adam Ruby

Adam Ruby - HiPy Competition .ipynb file.
---
 ...Registrations for 1st Nov Event FACT.ipynb | 433 ++++++++++++++++++
 1 file changed, 433 insertions(+)
 create mode 100644 hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb

diff --git a/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb b/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb
new file mode 100644
index 0000000..0f693ff
--- /dev/null
+++ b/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb	
@@ -0,0 +1,433 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adam Ruby - HiPy Nov 1st Registration Competition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Firstly, import modules: \n",
+    "- csv to upload the file. \n",
+    "- numpy for array manipulation.\n",
+    "- pandas for dataframe and plot creation.\n",
+    "\n",
+    "and allow viewing of the plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import csv\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Module CSV is used to import the registration data as a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with open('registration_data.csv', newline='') as f:\n",
+    "    reg_csv = csv.reader(f, delimiter=',', skipinitialspace=True)\n",
+    "    \n",
+    "    reg_list = [line for line in reg_csv]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inital list includes headings in first row. For just the data the first row is excluded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['Management School', 'Other'],\n",
+       " ['Other', 'Other'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Biology', 'Email']]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_list = reg_list[1:]\n",
+    "reg_list[:5]  #To view first five rows as example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lists are made for the variable names in order for it to be possible to count how people found out about HiPy and from what department.\n",
+    "- dep_list: List of department names\n",
+    "- found_list: List of ways people found out about HiPy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dep_list = ['Architecture','Astrophysics','Biology','Chemistry','Computer Science','EEE','Earth, Ocean, Ecology','External',\n",
+    "            'Institute of Infection /  Global Health','Management School','Mathematics','Other','Physics','Stephenson Institute']\n",
+    "found_list = ['Email','Other','Poster/Flyer','UoL video screen','Word of mouth']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initalised count to zero, this will represent the number of people from each department.\n",
+    "pop_list is a list used to store the values of count."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "count = 0\n",
+    "pop_list = np.empty([len(dep_list),len(found_list)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Loop is made so that for each cell containing a certain way of finding out about HiPy (eg 'Email'), the number of people who found out that particular way from each particular department is counted correctly.\n",
+    "\n",
+    "The names of the variables are iterated through using the lists dep_list and found_list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(0,len(dep_list)):\n",
+    "    for j in range(0,len(found_list)):\n",
+    "        count = 0\n",
+    "        for k in range(0,len(reg_list)):\n",
+    "            if reg_list[k][0] == dep_list[i]:\n",
+    "                if reg_list[k][1] == found_list[j]:\n",
+    "                    count = count + 1\n",
+    "        pop_list[i][j] = count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "dep_list variable 'Institute of Infection /  Global Health' updated to just 'Global Health' in order to make the graph\n",
+    "look better.\n",
+    "\n",
+    "A pandas data frame is also created which shows the number of attendees from each department based on how they found out about HiPy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Email</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>Poster/Flyer</th>\n",
+       "      <th>UoL video screen</th>\n",
+       "      <th>Word of mouth</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Architecture</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Astrophysics</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Biology</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chemistry</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Computer Science</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EEE</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Earth, Ocean, Ecology</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>External</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Global Health</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Management School</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mathematics</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Other</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Physics</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Stephenson Institute</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Email  Other  Poster/Flyer  UoL video screen  \\\n",
+       "Architecture             1.0    0.0           0.0               0.0   \n",
+       "Astrophysics             1.0    0.0           0.0               0.0   \n",
+       "Biology                 12.0    2.0           1.0               1.0   \n",
+       "Chemistry                2.0    0.0           0.0               0.0   \n",
+       "Computer Science         1.0    0.0           0.0               0.0   \n",
+       "EEE                      0.0    1.0           0.0               0.0   \n",
+       "Earth, Ocean, Ecology    1.0    0.0           1.0               0.0   \n",
+       "External                 2.0    0.0           0.0               0.0   \n",
+       "Global Health            1.0    0.0           0.0               0.0   \n",
+       "Management School        0.0    2.0           0.0               0.0   \n",
+       "Mathematics              0.0    2.0           0.0               1.0   \n",
+       "Other                    1.0    2.0           0.0               0.0   \n",
+       "Physics                  2.0    0.0           0.0               0.0   \n",
+       "Stephenson Institute     1.0    0.0           0.0               0.0   \n",
+       "\n",
+       "                       Word of mouth  \n",
+       "Architecture                     0.0  \n",
+       "Astrophysics                     0.0  \n",
+       "Biology                          1.0  \n",
+       "Chemistry                        1.0  \n",
+       "Computer Science                 1.0  \n",
+       "EEE                              0.0  \n",
+       "Earth, Ocean, Ecology            0.0  \n",
+       "External                         1.0  \n",
+       "Global Health                    0.0  \n",
+       "Management School                1.0  \n",
+       "Mathematics                      2.0  \n",
+       "Other                            2.0  \n",
+       "Physics                          0.0  \n",
+       "Stephenson Institute             0.0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dep_list[8] =  'Global Health'\n",
+    "\n",
+    "df = pd.DataFrame(pop_list, columns=found_list, index=dep_list)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A horizontal bar chat with stacked colours using Pandas is created and labels/title set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x20a3db53c88>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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8brnllrT2O+ussxg9enTx+6eeeoozzzxzkzoPPvggdevWZdasWdx4440UFhYC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\niKhZinFERETN/h8B65rlskwLOQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x20a3db4bcc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph = df.plot.barh(stacked=True, title='HiPy Registrations - 1 Nov 2017', width=0.85)\n",
+    "graph.set_xlabel(\"Number of People\")\n",
+    "graph.set_ylabel(\"Department\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 0e13c21c049e0db4d6701a4016e13d8eece02cfc Mon Sep 17 00:00:00 2001
From: sgaruby <31140929+sgaruby@users.noreply.github.com>
Date: Fri, 27 Oct 2017 16:48:05 +0100
Subject: [PATCH 2/3] Adam Ruby-Competition (Updated to add exposure %)

Updated to add HiPy successful exposure (eg Email) percentage in terms of total attendees.
---
 ...Registrations for 1st Nov Event FACT.ipynb | 339 ++++++++++++++----
 1 file changed, 274 insertions(+), 65 deletions(-)

diff --git a/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb b/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb
index 0f693ff..3eb0503 100644
--- a/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb	
+++ b/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb	
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 98,
    "metadata": {
     "collapsed": true
    },
@@ -43,16 +43,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['Department', 'How did you hear about HiPy?'],\n",
+       " ['Stephenson Institute', 'Email'],\n",
+       " ['Chemistry', 'Email'],\n",
+       " ['Biology', 'Poster/Flyer'],\n",
+       " ['Management School', 'Other'],\n",
+       " ['Other', 'Other'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Other', 'Word of mouth'],\n",
+       " ['Other', 'Word of mouth'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Computer Science', 'Word of mouth'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Biology', 'Word of mouth'],\n",
+       " ['Mathematics', 'UoL video screen'],\n",
+       " ['Earth, Ocean, Ecology', 'Email'],\n",
+       " ['Institute of Infection /  Global Health', 'Email'],\n",
+       " ['External', 'Email'],\n",
+       " ['Mathematics', 'Other'],\n",
+       " ['Physics', 'Email'],\n",
+       " ['Earth, Ocean, Ecology', 'Poster/Flyer'],\n",
+       " ['Biology', 'Other'],\n",
+       " ['Biology', 'UoL video screen'],\n",
+       " ['Mathematics', 'Word of mouth'],\n",
+       " ['Mathematics', 'Other'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Chemistry', 'Email'],\n",
+       " ['Management School', 'Other'],\n",
+       " ['Physics', ''],\n",
+       " ['Management School', 'Word of mouth'],\n",
+       " ['Computer Science', 'Email'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Biology', 'Other'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Astrophysics', 'Email'],\n",
+       " ['Chemistry', 'Word of mouth'],\n",
+       " ['Physics', 'Email'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['External', 'Word of mouth'],\n",
+       " ['Mathematics', 'Word of mouth'],\n",
+       " ['Stephenson Institute', ''],\n",
+       " ['Architecture', 'Email'],\n",
+       " ['Biology', 'Email'],\n",
+       " ['Physics', ''],\n",
+       " ['External', 'Email'],\n",
+       " ['Other', 'Email'],\n",
+       " ['EEE', 'Other'],\n",
+       " ['Other', 'Other']]"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "with open('registration_data.csv', newline='') as f:\n",
     "    reg_csv = csv.reader(f, delimiter=',', skipinitialspace=True)\n",
     "    \n",
-    "    reg_list = [line for line in reg_csv]"
+    "    reg_list = [line for line in reg_csv]\n",
+    "    \n",
+    "reg_list"
    ]
   },
   {
@@ -64,29 +125,121 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[['Management School', 'Other'],\n",
-       " ['Other', 'Other'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Biology', 'Email']]"
+       "[['Stephenson Institute', 'Email'],\n",
+       " ['Chemistry', 'Email'],\n",
+       " ['Biology', 'Poster/Flyer'],\n",
+       " ['Management School', 'Other'],\n",
+       " ['Other', 'Other']]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "\n",
+    "reg_labels = reg_list[0]\n",
+    "\n",
     "reg_list = reg_list[1:]\n",
     "reg_list[:5]  #To view first five rows as example"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To provide more information, it would be nice to include the percentage of the HiPy methods attendees were exposed to that lead to the successful registration. \n",
+    "\n",
+    "A pandas dataframe is utilised from the reg_list and by using the in-built 'groupby' function the frequency for each successful exposure methods is counted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Department</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>How did you hear about HiPy?</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Email</th>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Other</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poster/Flyer</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UoL video screen</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Word of mouth</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              Department\n",
+       "How did you hear about HiPy?            \n",
+       "Email                                 25\n",
+       "Other                                  9\n",
+       "Poster/Flyer                           2\n",
+       "UoL video screen                       2\n",
+       "Word of mouth                          9"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reg_df = pd.DataFrame.from_records(reg_list, columns=reg_labels)\n",
+    "reg_df.replace(r'^\\s*$', np.nan, regex=True, inplace = True)\n",
+    "freq = reg_df.groupby('How did you hear about HiPy?').count()\n",
+    "freq"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -98,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 102,
    "metadata": {
     "collapsed": true
    },
@@ -119,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 103,
    "metadata": {
     "collapsed": true
    },
@@ -140,8 +293,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 104,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "for i in range(0,len(dep_list)):\n",
@@ -154,19 +309,58 @@
     "        pop_list[i][j] = count"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To include the total percentage of how people were exposed to HiPy a for loop was required using the grouped pandas dataframe found early."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freq_list = []\n",
+    "total = 0\n",
+    "for g in range(0,len(freq['Department'])):\n",
+    "    freq_list.append(freq['Department'][g])\n",
+    "    total = total + freq_list[g]\n",
+    "\n",
+    "percentage_list = (freq_list/total)*100"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "dep_list variable 'Institute of Infection /  Global Health' updated to just 'Global Health' in order to make the graph\n",
-    "look better.\n",
+    "look better and also the method of exposure list (found_list) was updated to include the percentage of how affective the overall method of exposure was relative to all users."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dep_list[8] =  'Global Health'\n",
     "\n",
+    "for h in range(0, len(found_list)):\n",
+    "    found_list[h] = found_list[h] + \"   (\" + str(round(percentage_list[h],1))+ \"%)\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "A pandas data frame is also created which shows the number of attendees from each department based on how they found out about HiPy."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -190,11 +384,11 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Email</th>\n",
-       "      <th>Other</th>\n",
-       "      <th>Poster/Flyer</th>\n",
-       "      <th>UoL video screen</th>\n",
-       "      <th>Word of mouth</th>\n",
+       "      <th>Email   (53.2%)</th>\n",
+       "      <th>Other   (19.1%)</th>\n",
+       "      <th>Poster/Flyer   (4.3%)</th>\n",
+       "      <th>UoL video screen   (4.3%)</th>\n",
+       "      <th>Word of mouth   (19.1%)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -315,47 +509,61 @@
        "</div>"
       ],
       "text/plain": [
-       "                       Email  Other  Poster/Flyer  UoL video screen  \\\n",
-       "Architecture             1.0    0.0           0.0               0.0   \n",
-       "Astrophysics             1.0    0.0           0.0               0.0   \n",
-       "Biology                 12.0    2.0           1.0               1.0   \n",
-       "Chemistry                2.0    0.0           0.0               0.0   \n",
-       "Computer Science         1.0    0.0           0.0               0.0   \n",
-       "EEE                      0.0    1.0           0.0               0.0   \n",
-       "Earth, Ocean, Ecology    1.0    0.0           1.0               0.0   \n",
-       "External                 2.0    0.0           0.0               0.0   \n",
-       "Global Health            1.0    0.0           0.0               0.0   \n",
-       "Management School        0.0    2.0           0.0               0.0   \n",
-       "Mathematics              0.0    2.0           0.0               1.0   \n",
-       "Other                    1.0    2.0           0.0               0.0   \n",
-       "Physics                  2.0    0.0           0.0               0.0   \n",
-       "Stephenson Institute     1.0    0.0           0.0               0.0   \n",
+       "                       Email   (53.2%)  Other   (19.1%)  \\\n",
+       "Architecture                       1.0              0.0   \n",
+       "Astrophysics                       1.0              0.0   \n",
+       "Biology                           12.0              2.0   \n",
+       "Chemistry                          2.0              0.0   \n",
+       "Computer Science                   1.0              0.0   \n",
+       "EEE                                0.0              1.0   \n",
+       "Earth, Ocean, Ecology              1.0              0.0   \n",
+       "External                           2.0              0.0   \n",
+       "Global Health                      1.0              0.0   \n",
+       "Management School                  0.0              2.0   \n",
+       "Mathematics                        0.0              2.0   \n",
+       "Other                              1.0              2.0   \n",
+       "Physics                            2.0              0.0   \n",
+       "Stephenson Institute               1.0              0.0   \n",
+       "\n",
+       "                       Poster/Flyer   (4.3%)  UoL video screen   (4.3%)  \\\n",
+       "Architecture                             0.0                        0.0   \n",
+       "Astrophysics                             0.0                        0.0   \n",
+       "Biology                                  1.0                        1.0   \n",
+       "Chemistry                                0.0                        0.0   \n",
+       "Computer Science                         0.0                        0.0   \n",
+       "EEE                                      0.0                        0.0   \n",
+       "Earth, Ocean, Ecology                    1.0                        0.0   \n",
+       "External                                 0.0                        0.0   \n",
+       "Global Health                            0.0                        0.0   \n",
+       "Management School                        0.0                        0.0   \n",
+       "Mathematics                              0.0                        1.0   \n",
+       "Other                                    0.0                        0.0   \n",
+       "Physics                                  0.0                        0.0   \n",
+       "Stephenson Institute                     0.0                        0.0   \n",
        "\n",
-       "                       Word of mouth  \n",
-       "Architecture                     0.0  \n",
-       "Astrophysics                     0.0  \n",
-       "Biology                          1.0  \n",
-       "Chemistry                        1.0  \n",
-       "Computer Science                 1.0  \n",
-       "EEE                              0.0  \n",
-       "Earth, Ocean, Ecology            0.0  \n",
-       "External                         1.0  \n",
-       "Global Health                    0.0  \n",
-       "Management School                1.0  \n",
-       "Mathematics                      2.0  \n",
-       "Other                            2.0  \n",
-       "Physics                          0.0  \n",
-       "Stephenson Institute             0.0  "
+       "                       Word of mouth   (19.1%)  \n",
+       "Architecture                               0.0  \n",
+       "Astrophysics                               0.0  \n",
+       "Biology                                    1.0  \n",
+       "Chemistry                                  1.0  \n",
+       "Computer Science                           1.0  \n",
+       "EEE                                        0.0  \n",
+       "Earth, Ocean, Ecology                      0.0  \n",
+       "External                                   1.0  \n",
+       "Global Health                              0.0  \n",
+       "Management School                          1.0  \n",
+       "Mathematics                                2.0  \n",
+       "Other                                      2.0  \n",
+       "Physics                                    0.0  \n",
+       "Stephenson Institute                       0.0  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dep_list[8] =  'Global Health'\n",
-    "\n",
     "df = pd.DataFrame(pop_list, columns=found_list, index=dep_list)\n",
     "df"
    ]
@@ -369,24 +577,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x20a3db53c88>"
+       "<matplotlib.legend.Legend at 0x21b5baa6c18>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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8brnllrT2O+ussxg9enTx+6eeeoozzzxzkzoPPvggdevWZdasWdx4440UFhYC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\nLfDHwOy6swBI+h3gEOBCANuv2f6/elO9aSTwLkk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RF5fTn/irn4J4zjgIeF1ESmJFcXuwl0KBd4GvReSAvW/cG/hURIrY158Hdtvvi4jIOqwb\nioeyG1BVvxORmkCcvSx7GuiOFZG6wxf42F6CFqy91eMiMgaYISKJWCe+e+Vu6jniadwvgLki0h54\nIlObmcDbIpICNMDa1/4/ERkOrHOpl7mPQcB0ey6FsG40+mMwGPINT8vUrVq14vPPP2fgwIFs2bLF\nWe7j40Pnzlac0b17d+6//35OnDjB8ePHadq0KQC9evWiU6dOAISFhdGtWzc6dOhAhw4dcrSnUqVK\n1K9fH4Aff/yRHTt20LChdXbz3LlzNGjQwGPb+++/nzlz5rBu3TreeecdZ/myZcuIj4/ntttuA6wb\nkDJl3Mc6t9xyC+Hh1n1/ZGQkSUlJHueXubxHjx58/fXXOc7xaqUg9ozjAbfPu6rq68DrLp+/B27z\n0NV0VR2bqX3vTJ+DXN6/hnVoKTMhLnVecSlv5Ma+Y0B7N+VjMn12e8pAVYPtt0dyOe5uIMylyPUQ\n1zxgXqZrrisInvqAC6sLBoPhMpKens5PP/1E0aJFOXbsGDfffLPbejk9LvPll1+ycuVKFi9ezPjx\n49m+fTuFChUiPT3dWcf12dfAwEDne1WlVatWfPrpp3hDly5diIiIoFevXhn2mlWVXr168eKLL+bY\nR5EiRZzvfX19ncvU7lDVa+qxNSMUYTAYDJeZKVOmULNmTT799FP69OnD+fPnActJz507F4BPPvmE\nRo0aUaJECUqVKsWqVdY9+EcffUTTpk1JT09n7969NG/enEmTJnH8+HFOnz5NcHAwmzZtAmDTpk38\n9ttvbm2oX78+a9asYc+ePQAkJyeze/dut3UBKlasyIQJE3jssccylLdo0YK5c+dy6NAhAI4dO8bv\nv1s7j35+fs65ecLT/EqWLEmJEiVYvXo1gHOp+9/KVZkO0yXCNFwmQsuXYKMReDAYcoVjz9hB27Zt\n6dOnD++//z7r16+nWLFiNGnShJiYGMaOHUtgYCDbt28nMjKSEiVKOA9NzZo1i/79+5OcnEzlypWZ\nMWMGaWlpdO/enRMnTqCqDB48mJIlS/LAAw/w4YcfEh4ezm233Ub16lkWygAoXbo0M2fO5KGHHuKf\nf6zzpDExMR7rAzz66KNZymrVqkVMTAytW7cmPT0dPz8/pk+fTqVKlejXrx9hYWFEREQwYcIEj/26\nmx/AjBkz6NOnDwEBAbRp0ybnL/wqRlQ151qGa56oqCjduHFjQZthMGTgp59+ombNmgVtRr4RFBTk\n8USz4crH3b9HEYlX1aic2l6VkbHh8pOfEor5iZFjNBgM/wbMnrHBYDBcIZio+NrFOGODwWAwGAoY\n44wLEBFJs4UbttlCGAG2iMS2fOjbKbNoMBgMhisb44wLlhQ7FWUIcI58TLqhqm+r6oc51zQYDAZD\nQWOc8ZXDKsAhBembWV5RRKqIyCZHZRGpJiLx9vuJLrKLr9hlY0TkGft9VRFZKiJbxJKjrCIiZUVk\npUtk3vhyT9hgMBgMFuY09RWALQZxF/CNXVQNeEhV+4rIZ8ADqvqxiJwQkXBb7elhYKaIXAdEAzVU\nVe00o5mZDUxU1QUi4o91EzYA+FZVJ4iILxBwiadpMFxy8vvEvzen9X19fQkNDXV+7tKlC8OGDbvo\nse+44w7Wrl1LUlIS99xzD9u2eb97paq0aNGChQsXUrx4cYKDgylWrBi+vr4UKlQIx2OKI0eOZNGi\nRfj4+FCmTBlmzpxJuXLlMvSVkJDAgAEDOHnyJL6+vowYMcKZsrNbt25s3bqVe+65xymXOH78eMLC\nwmjf3kpWuGTJEjZs2ODMN21wj4mMC5aiIpKApUn8B+BQkvIkr/g+8LDtPDsDnwAngbPA+yJyP1bO\nbCciUgwor6oLAFT1rKomAxvsvsYAoZkVm+y2/URko4hsTEs+kV9zNhj+VTjyTzte+eGIAdauXZvn\ntl999RV16tShePHizrLly5eTkJCAa76AoUOHkpiYSEJCAvfccw/jxo3L0ldAQAAffvgh27dv55tv\nvuGpp57i+PHjJCYmApCYmMiqVas4ceIEBw4cYP369U5HDNCuXTsWL15McnJylr4NFzDOuGBx7BmH\nq+oTqnrOLvckrzgPK4K+B4hX1aOqmgrUs6914EJ07cBtcldVXQk0Af4EPnJ32MtIKBoMeSc4OJjh\nw4fToEEDoqKi2LRpE23atKFKlSpOhaXTp0/TokULIiIiCA0NZdGiRc72QUFBnrrOkdmzZ2dwiJ5w\nddZnzpxxmwu6evXqVKtmCe2VK1eOMmXKcPjwYfz8/EhJSSE9PZ1z587h6+vLqFGjsjh0EaFZs2Ys\nWbIkz/O5FjDO+CpCVc9iyUG+BcwAsGUhS6jqV8BTWJKUrm1OAvtEpINdv4h9arsScEhV38OKyCMu\n30wMhn8P7mQSHVSoUIG4uDgaN25M7969mTt3Lj/++COjRo0CwN/fnwULFrBp0yaWL1/OkCFDyI+s\niGvWrCEyMtL5WURo3bo1kZGRvPvuuxnqjhgxggoVKjB79my3kbEr69ev59y5c1SpUoWaNWtSsWJF\nIiIiePDBB9mzZw+qSt26dbO0i4qKcuaeNrjH7BlffcwG7sfSIwZLo3iRvRcswGA3bXoA74jIOOA8\n0AloDAwVkfNYkpLmMSiDIQ94kkkEuO+++wAIDQ3l9OnTFCtWjGLFiuHv78/x48cJDAxk+PDhrFy5\nEh8fH/78808OHjzITTfddFE2HTt2jGLFLsiXr1mzhnLlynHo0CFatWpFjRo1aNKkCQATJkxgwoQJ\nvPjii7zxxhse93YPHDhAjx49mDVrllO1aerUqc7r9957L++88w4TJkxgy5YttGrVyqnTXKZMGfbv\n339Rc/q3Y5xxAeIq8ehSloRneUWwJBY/UNU0+/oBrGXqzP2McXn/M3Bnpiq/ArPyaLrBYPACh2Sg\nj49PBvlAHx8fUlNTmT17NocPHyY+Ph4/Pz+Cg4MzSB7mFYeMosNpOg5llSlThujoaNavX+90xg66\ndu1Ku3bt3DrjkydP0q5dO2JiYpx6yK4sWrSIqKgozpw5w7Zt2/jss89o0qQJ3bp1IyAggLNnz1K0\naNGLnte/GbNMfRUhIguwIlh3uswGg+Eq48SJE5QpUwY/Pz+WL1/ulB68WG699VZ+/fVXwNoLPnXq\nlPP9d999R0iIdb//888/O9ssXryYGjVqZOnr3LlzREdH07NnTzp16pTl+vnz53nttdcYOnQoycnJ\nzn1nx14ywO7du51jGtxjIuOrCFWNLqixjYSi4WqgIIRD3MkkTpw40au23bp149577yUqKorw8HC3\nzjAvtGvXjhUrVlC1alUOHjxIdLT1pyM1NZWuXbvStm1bAIYNG8auXbvw8fGhUqVKzoNlGzdu5O23\n3+b999/ns88+Y+XKlRw9epSZM2cCMHPmTOecp0+fTq9evQgICCAsLAxVJTQ0lLvvvpuSJa0nLZcv\nX86LL76YL3P7t2IkFA1eYSQUDVci/zYJxfziwIED9OzZk//9738FbQoHDx6ka9euLFu2rKBNueQY\nCUXDJSc/JRST/LvmSz/5yfS/FhS0CW4Z+HbmrX6DIWfKli1L3759OXnyZIbHlwqCP/74g1dffbVA\nbbgaMM7YYDAY/oU8+OCDBW0CALfddltBm3BVYA5wGQwGg8FQwBhnfIUjIjeLyCIR+VlEfhGR10Sk\nsIiEi8jdLvWcwhAGg8FguLowzvgKRqxnBOYDC1W1GlAdCAImYGXaujub5rkdyze/+jIYDAZD7jDO\n+MrmTuCsqs4AsBN9DAb+A0wCOtsSiJ3t+rVEZIWI/CoigxydiEh3EVlv133H4XhF5LSIjBORdUCD\nyzozg8FgMDgxzvjKpjaWapMTO9d0EhADxNoiE45kuDWANlgZuUaLiJ+I1MRSeGqoquFYwhPd7PqB\nwDZVvV1VV1/y2RgMl5oxJfL35QX79u2jffv2VKtWjSpVqvDkk086k10kJCTw1VdfXTBvzBheeSVz\nUr1Lw8KFC525pleuXElERASFChVi7ty5Geo999xzhISEEBISkiGvtiue2u/atYvIyEjq1KlDXFwc\nYD3L3LJlywwqTV26dMmQYMSQFeOMr2wEcPcguKfyL1X1H1U9AhwCbgRaAJHABluusQVQ2a6fhqX2\n5H5wI6FoMGSLqnL//ffToUMHfv75Z3bv3s3p06cZMWIEkNUZXyxpaWle1500aRKPPfYYABUrVmTm\nzJl07ZrxscIvv/ySTZs2kZCQwLp163j55Zc5efJklr48tX/nnXeYOHEic+fOdd5kvPXWW/To0YOA\ngAsS6QMGDGDSpEle234tYpzxlc12IMPD4iJSHKiA5Ugz4056UYBZLlKNt7rkrT7ryHHtDiOhaDBk\nz/fff4+/vz8PP/wwAL6+vkyZMoUPPviAkydPMmrUKGJjYzOoOe3YsYNmzZpRuXJlpk2b5uzr448/\npl69eoSHh/Poo486HW9QUBCjRo3i9ttvd0afObF7926KFCnCDTfcAFhyjmFhYc5c1Q527NhB06ZN\nKVSoEIGBgdSpU4dvvsmswuq5vUNGMTk5GT8/P44fP84XX3xBz54ZdWcaN27M0qVLSU1N9cr+axHj\njK9slgEBDq1he6/3VWAmcBBLscmbPjqKSBm7j+ts+USDwXCRbN++PYNUIVgawRUrViQpKYlx48bR\nuXNnEhIS6NzZOtqxc+dOvv32W9avX8/YsWM5f/48P/30E7GxsaxZs4aEhAR8fX2ZPXs2YOWTDgkJ\nYd26dTRq1Mgru9asWUNERM6qqHXq1OHrr78mOTmZI0eOsHz5cvbu3ev1/AcOHMjkyZPp378/w4cP\nZ9y4cYwYMSKLLrKPjw9Vq1Zly5YtXvd9rWGSflzBqKqKSDTwpoiMxLp5+goYjrXfO8xeevaY9FVV\nd4jI88B3IuKDJaE4EMifjPQGwzWMqmZxPNmVg5U3ukiRIhQpUoQyZcpw8OBBli1bRnx8vDNBRkpK\nCmXKlAGsaPuBBx7IlV0HDhygdOnSOdZr3bo1GzZs4I477qB06dI0aNCAQoW8dwsVK1ZkxYoVAOzZ\ns4f9+/dTo0YNevTowblz5xg/fjzVq1cHLsgoZr55MVgYZ3yFo6p7gXvdXPoH8JjaRlVdZRhjgSwn\nM9xJOBoMBu+pXbs28+ZlPHZx8uRJ9u7dS5UqVYiPj8/SxlVK0dfXl9TUVFSVXr16uRVT8Pf3x9c3\nd08eFi1alBMnvDvnMWLECOced9euXalWrVquxnLtJyYmhmnTptGtWzeCg4MZO3asM8I3MorZY5ap\nDQaDIY+0aNGC5ORkPvzwQ8A6YDVkyBB69+5NQEAAxYoVc8oX5tTP3LlzOXToEADHjh27KDnFmjVr\nsmfPnhzrpaWlcfToUQASExNJTEykdevWuR7vhx9+oHz58lSrVo3k5GR8fHzw9fXNcKJ69+7d1K5d\nO9d9XzOoqnmZV46vyMhINRiuNHbs2FHQJugff/yh99xzj1atWlUrV66sjz/+uJ49e1ZVVY8ePapR\nUVFap04dnTNnjo4ePVpffvllZ9vatWvrb7/9pqqqc+bM0Tp16mhoaKhGRERoXFycqqoGBgbm2qYz\nZ85orVq1ND09XVVV169fr+XLl9eAgAC97rrrtFatWqqqmpKSojVr1tSaNWvq7bffrps3b3b2MXLk\nSF20aFG27VVV09PTtWXLlnrs2DFVtX4ndevW1dDQUF29erWqqv71119622235XoeVxvu/j0CG9WL\nv7FGQtHgFUZC0XAlYiQUPfPkk09y77330rJly4I2hSlTplC8eHEeeeSRgjblknIxEopmmdpgMBj+\nhQwfPjzDMnFBUrJkSXr16lXQZlzRmANcBu/Yv9nrjESXk5/mlMuXfr5vNj1f+slvjJ6xIa/ceOON\n3HfffQVtBoDzOWyDZ0xkbDAYDAZDAWOccSZEREXkI5fPhUTksIgsyaFdgUoaikiwiHR1+RwlItOy\na2MwGAyGKwPjjLNyBggREccDca2AP71ol6+ShnkgGHA6Y1XdqKqDPFc3GAwGw5WCccbu+RpoZ79/\nCPjUcUFE6onIWhHZbP+8VUQKA+PIH0nDl0QkXkSW2mM52t9n1wkWkVUissl+3WF3OxFobPc5WESa\nOaJ5EQnxRNMkAAAgAElEQVQSkRkislVEEkXkARHxFZGZIrLNLh98Kb9Qg8FgMHjGHOByzxxglO3M\nwoAPgMb2tZ1AE1VNFZGWwAuq+oCIjAKiVPVxsJapsSQNm2PlkN4lIm8BVbkgaXheRN7EkjT8ECvF\n5QpVfU5EFmDJJLYCagGzgMVYakytVPWsiFTDulGIAoYBz6jqPfb4zVzmMxI4oaqh9rVSWJF8ebUz\ndYlIyfz7+gyGgiF0Vmi+9re119Yc6/j6+hIaGkpqaio1a9Zk1qxZGRSLvGHq1Kn069cv1+0A4uLi\n+OCDD+jWrRvt27fnlltuAeCGG25g6dKljBkzhqCgIJ555rLtmnHgwAH69u3LkiUXdvf++OMPatWq\nxZgxY9za8sgjj7Bx40ZUlerVqzNz5kyCgoJ4/fXXeeedd6hYsSILFy6kcOHCrF69mvnz5zN58mQA\nDh8+TI8ePdyKXFwtmMjYDaqaiLXs+xBWLmhXSgCfi8g2YAqW5rAncitpeA5w/GvaCvygquft98F2\nuR/wnohsBT7HctQ50RJwHhdW1b+BX4HKIvK6iLQFsuimuUooHk42z6MbDO4oWrQoCQkJbNu2jcKF\nC/P222/nuo+pU6fm+jEkh6rTN998Q9u2bQFLHSkhIYGEhASWLl2aazuyIzeKS5MnT6Zv374ZygYP\nHsxdd93lsc2UKVPYsmULiYmJVKxYkTfeeAOA999/n8TEROrWrcu3336LqjJ+/HhGjhzpbFu6dGnK\nli3LmjVrcjmrKwfjjD2zGHgFlyVqm/HAcjuivBfwz6aP3EoantcLWVjSHe1VNZ0LqxiDsRSb6mBF\nxIW9mEsW/WPbIdcBVmAJR7yfuZG6SCiWDnCf9N5gMFygcePGzjSUkydPJiQkhJCQEKZOnQpYCkzt\n2rWjTp06hISEEBsby7Rp09i/fz/NmzenefPmAHz33Xc0aNCAiIgIOnXqxOnTpwFLynDcuHE0atSI\nzz//HIBly5Z5ldjjl19+yaDk9PPPPztFG+Lj42natCmRkZG0adOGAwcOANCsWTOGDx9O06ZNee21\n17z+HubNm+e8QQBYuHAhlStXzjYdZvHixQErK2RKSkoGoY3z5887ZRo/+ugj7r77bkqVKpWhfYcO\nHZx5sK9GjDP2zAfAOFXNvE5VggsHunq7lJ/i8kgalgAO2A66B+DIIJ/d+N8Bjzs+iEgpEbkB8FHV\neVjL2DnrrRkMBo+kpqby9ddfExoaSnx8PDNmzGDdunX8+OOPvPfee2zevJlvvvmGcuXKsWXLFrZt\n20bbtm0ZNGgQ5cqVY/ny5SxfvpwjR44QExPD0qVL2bRpE1FRUc7lWLCEI1avXk2XLl04cuQIfn5+\nlChh5QBYtWoV4eHhhIeHM2HChAz2ValShRIlSpCQkADAjBkz6N27N+fPn+eJJ55g7ty5xMfH06dP\nH6dwBMDx48f54YcfGDJkiFffw2+//UapUqWcghhnzpzhpZdeYvTo0Tm2ffjhh7npppvYuXMnTzzx\nBADPPPMM9evX5/DhwzRs2JBZs2bx2GOPZWkbFRXFqlWrvLLxSsQ4Yw+o6j5VdXcrOAl4UUTWcMER\nAizHOrDleoDLXb87AIekYSLwP6BsLkx7E+glIj8C1bFOfwMkAqkissXNYawYoJR9WGsL1j52eWCF\nvVQ+E/hvLmwwGAw2KSkphIeHExUVRcWKFXnkkUdYvXo10dHRBAYGEhQUxP3338+qVasIDQ1l6dKl\nPPfcc6xatcrpRF358ccf2bFjBw0bNiQ8PJxZs2ZlEI1w6CKDFUG7Cju4LlO7OlQH//nPf5gxYwZp\naWnExsbStWtXdu3axbZt22jVqhXh4eHExMSwb98+t+N5Q2b5xtGjRzN48GCCgnIWiZsxYwb79++n\nZs2axMZaQnM9evRg8+bNfPzxx0yePJlBgwbx9ddf07FjRwYPHkx6ejpwQaLxasUc4MqEupEVVNUV\nWMu5qGoclhN0MNIuP0Y+Shq6LF1nuKaqP2MdKnPwX7v8PNb+sysOm08D7nLRmWjYYLhIHHvGrnjK\n+V+9enXi4+P56quv+O9//0vr1q0ZNWpUlratWrXi008z75BZBAYGOt9//fXXPP30017b+sADDzB2\n7FjuvPNOIiMjuf7669m/fz+1a9cmLi4ux/G8oWjRopw9e9b5ed26dcydO5dnn32W48eP4+Pjg7+/\nP48//rjb9r6+vnTu3JmXX345Q+au/fv3s2HDBkaPHk29evWIi4tjxIgRLFu2jFatWl31Eo0mMjYY\nDIZ8pkmTJixcuJDk5GTOnDnDggULaNy4Mfv37ycgIIDu3bvzzDPPsGnTJoAMUov169dnzZo1zr3n\n5ORkdu/enWUMVSUxMZHw8HCv7fL396dNmzYMGDDA6ehuvfVWDh8+7HTG58+fZ/v27Xmee/Xq1UlK\nSnJ+XrVqFUlJSSQlJfHUU08xfPjwLI5YVZ3zVVW++OILatSokaHOyJEjGT9+PIBzT9nHx8d58G33\n7t2EhIRwtWIiY4N3lKsLY6481aaaY/Kpn/zpxlDAePMo0uUgIiKC3r17U69ePcBaHnacBh46dCg+\nPj74+fnx1ltvAdCvXz/uuusuypYty/Lly5k5cyYPPfQQ//xjnQGNiYmhevXqGcaIj4+nbt26GQ46\neUO3bt2YP3++c3m7cOHCzJ07l0GDBnHixAlSU1N56qmn8qw9HBgYSJUqVdizZw9Vq1bNtu7dd9/N\n+++/z0033USvXr04efIkqkqdOnWc3w3A5s2bAahbty5gPQYVGhpKhQoVnHvRy5cvp127dlkHuUow\nEooGrzASioYrkWtZQjEmJoaqVavSpUuXXLV75ZVXOHHihDPKvBQsWLCA+Ph4YmJiLtkYmWnSpAmL\nFi3Kcsr6cnIxEoomMjYYDIarkOeffz7XbaKjo/nll1/4/vvvL4FFGcc5evToJR3DlcOHD/P0008X\nqCO+WExkbPCKqHK+urFfzqchLzfT/1qQL/0YqcKrk2s5MjZceVxMZGwOcBkMBoPBUMAUmDPOq1Th\n1UxmmcNM13xEZJqLcMMGEbklm77yTaJRRE7nRz8Gg8FgyBsFGRnnVarwaiYYF5nDTHQGygFhtqBD\nNHD8MtllMBgMhgKkoJepcyVVaJf3FpH5IvKNiPwsIpNc2rxlCxtsF5GxLuV3i8hOEVltR58OacFA\nEfnAjkI3i0h7lzEWisgXIvKbiDwuIk/bdX4UkevselVsO+JtWcMadvlMe5y1tvxhR9uUDDKHmb6L\nslxIc+nIAPa33V9bWy5xi4gsc2njSaLxaTvC3iYiT+VUbjAYDIaCpaCd8Rygi4j4Y2WVWudyzSFV\nWBcYBbzgci0cK5IMxdIQrmCXj7A3ysOApiISZvf9DnCXqjYCSrv0MwL4XlVvw0oR+bKIONLNhGBF\nsfWACUCybUsc0NOu8y7whKpGAs9gpap0UBZoBNyD5YTBkjlcZQtETMn0XXwG3Gs76ldFpC6AiJQG\n3gMeUNU6QCeXNjWANraNo0XET0QigYeB24H6QF8RqeupHIPhX8RPNWrm6ysnkpKSsiSaGDNmDK+8\n8kq27Xr37s3cuXNzPb/9+/fTsWNHt9eaNWvGtfj4YceOHfn1118zlN13330eE4AsWrSIsLAwZwrT\n1atXA7Br1y4iIyOpU6eOMwFKamoqLVu2zKCo1aVLF37++ed8n0eBPtqkqokiEoxnqcJZtmavYkkH\nOlimqicARGQHUAnYCzwoIv2w5lUWS17QB/hVVX+z234K9LPftwbuc9l79Qcq2u+Xq+op4JSInAC+\nsMu3AmEiEgTcgSWn6LCriIuNC+0od4eI3OjFd7HPjv7vtF/LRKQTEACsdNhvp9108KWq/gP8IyIO\nicZGwAJVPWN/P/OxtJjFQ/lmTzbZ32U/gIoljGqTwVDQlCtXLk9O/HKQlpaGr69vzhXzke3bt5OW\nlkblypWdZfPnz882D3aLFi247777EBESExN58MEH2blzJ++88w4TJ04kODiYYcOGMW/ePN566y16\n9OiRQWd6wIABTJo0iffeey9f51LQkTHkTaowizShfdjpGaCFqoYBX9ptsvMighVxOuQMK6rqT27G\nSHf57JAz9AGOu7QNV1XXW2nX9l55Mlv7+GtVHYq1EtABN/KHHsZwlWh0R669qZFQNBgujoSEBOrX\nr09YWBjR0dH8/fffXrV77rnnePPNCwttY8aM4dVXX80QiaekpNClSxfCwsLo3LkzKSkpzvqeJBiX\nLVtG3bp1CQ0NpU+fPs4MX65MmzaNWrVqERYW5kwocvr0aR5++GFCQ0MJCwtj3rx5AAQFBTFq1Chu\nv/124uLiPEox/vLLL7Rt25bIyEgaN27Mzp07AWuFYNCgQdxxxx1Urlw51zcas2fPpn379s7Pp0+f\nZvLkydk+gx0UFOTMWnbmzBnnez8/P1JSUpxSjcePH+eLL76gZ8+eGdo3btyYpUuX5krf2RuuBGec\nW6lCTxTHOhR2wo5EHSrWO4HKdgQO1vK2g2+BJ8T+beRm2VZVTwK/2dErYlEnh2YeZQ5FJEJEytnv\nfbCW2n/HWhZv6jhZ7divzoaVQAcRCbCX3KOBVdmUGwyGS0TPnj156aWXSExMJDQ0lLFjx+bcCGsp\n1KFaBPDZZ5/RqVOnDHXeeustAgICSExMZMSIEcTHxwN4lGA8e/YsvXv3JjY2lq1bt5Kampoh5aSD\niRMnsnnzZhITE3n77bcBGD9+PCVKlGDr1q0kJiZy553Wc/lnzpwhJCSEdevWcfvtt3uUYuzXrx+v\nv/468fHxvPLKKxkkEA8cOMDq1atZsmQJw4YNy8W3C2vWrHFqMoOVv3rIkCEZIll3LFiwgBo1atCu\nXTs++OADAAYOHMjkyZPp378/w4cPZ9y4cYwYMSJLulEfHx+qVq3Kli1bcmVrThS4M86DVKGnfrZg\nLblux3Lwa+zyFOAx4BsRWQ0cBE7YzcZjLX8nisg2+3Nu6AY8IpYs4XagfQ71s5M5LAN8YduRCKQC\nb6jqYayl4vn2OFnUnlxR1U1Ykojrsfbg31fVzZ7KvZ6pwWDIQuY/1K7lJ06c4Pjx4zRt2hSAXr16\nsXLlSq/6rVu3LocOHWL//v1s2bKFUqVKUbFixQx1Vq5cSffu3QEICwsjLMwSc/Mkwbhr1y5uueUW\nZ45rT/aEhYXRrVs3Pv74YwoVsnYyly5dysCBA511HJmufH19eeCBBwA8SjGePn2atWvX0qlTJ8LD\nw3n00UedETNAhw4d8PHxoVatWhw8eNCr78eBq1xjQkICe/bsITo6Osd20dHR7Ny5k4ULFzJy5EgA\nKlasyIoVK4iLiyMgIID9+/dTo0YNevToQefOnTOIdVwKucYC2zO+CKnCmVhOxdHmHpf3vT0Mt1xV\na9gR8HRgo10/BXjUjR2Zxwh2d83ex23rpn3vTJ8d8ofuZA4ddb4BvvFw7Wusk+euZWMyfXaVaJwM\nTCYT2ZRfeam1DIargOuvvz7L0vOxY8e45RaPKQK8pmPHjsydO5e//vrLY/5pdzcDniQYM8s8euLL\nL79k5cqVLF68mPHjx7N9+3ZU1e1Y/v7+zn1iVXUrxXjy5ElKlizpcfwiRS4ctcltRkhXuUbHMnlw\ncDCpqakcOnSIZs2asWLFCo/tmzRpwi+//MKRI0e44YYbnOUjRowgJiaGadOm0a1bN4KDgxk7diyz\nZ88GuCRyjQUeGV8m+opIAlb0WgLrdLXBYDBcFEFBQZQtW5Zly6wnDo8dO8Y333xDo0aNKFGiBKVK\nlWLVKms36KOPPnJGyd7QpUsX5syZw9y5c92eoG7SpInTOWzbto3ExETAswRjjRo1SEpKcpa7syc9\nPZ29e/fSvHlzJk2axPHjxzl9+jStW7fmjTfecNZzt/ftSYqxePHi3HLLLXz++eeA5XDza4m3Zs2a\nzvkMGDCA/fv3k5SUxOrVq6levbpbR7xnzx6n09+0aRPnzp3j+uuvd17/4YcfKF++PNWqVSM5ORkf\nHx98fX0znKjevXt3nlWtPHFNCEXYjxFlfpTIYDD8y6i586ecK+UzH374IQMHDmTIkCEAjB49mipV\nqgAwa9Ys+vfvT3JyMpUrV2bGjBnOdo8++ihPPWU97l+hQoUsEWXt2rU5deoU5cuXp2zZslnGdWgS\nOx7Tccg1li5d2qME44wZM+jUqROpqancdttt9O/fP0OfaWlpdO/enRMnTqCqDB48mJIlS/L8888z\ncOBAQkJC8PX1ZfTo0dx///0Z2mYnxTh79mwGDBhATEwM58+fp0uXLtSpk9MRm5xp164dK1asoGXL\nltnWc+x99+/fn3nz5vHhhx/i5+dH0aJFiY2NdUb9qkpMTAyfffYZYO11d+vWLcP++sGDBylatKjb\n38nFYIQiDF5hJBQNVyJGKOLaJiUlhebNm7NmzZrL9ljVlClTKF68OI888kiWa0YowmAwGAzXHEWL\nFmXs2LH8+efly6RcsmRJevXqle/9erVMLSJF7OQS2ZYZ/r1s/fMEwcO+zJe+kia2y7mSwWAweEGb\nNm0u63gPP/zwJenX28g4zssyg8FgMBgMuSTbyFhEbgLKA0XthBiOs+3FsdI0GgwGg8FguEhyiozb\nYKWqvBnr+dRX7dfTwPBLa9rlQURuFJFPbOWjeBGJE5Fo+1ozyUFfWfKgKywe9IMzl9vqUW+4q+vF\nGE7b7fd3uFyb6aIkZTAYDIYCJtvIWFVnYYk1PKCq8y6TTZcNOwnIQmCWqna1yyoB9xWoYflPM+A0\nsLaA7TAYDAaDG7x9zniJiHQFgl3bqOq4S2HUZeRO4Jyqvu0oUNXfgdczV7RzQn8AVAaSgX6qmmhf\nriMi3wMVgEmq+p6t6rQIKIWVcvN5VV2UV0NtKcW3uaAq9ZSqrhGResBUoCiQAjysqrtc2gUD/YE0\nEekOPGFfaiIiTwM3Ac+q6pUpBWMw5ILp/b/P1/4Gvn1nttcHDx5MpUqVnM8Lt2nThgoVKvD+++8D\nMGTIEMqXL8/TTz+dp/HHjBlDUFAQzzzj3eLb4cOHueeeezh37hzTpk2jcePGeRo3N0ydOpV+/fo5\n80EHBQU5hSnywoEDB+jbty9Llizh6NGjdOzYkQ0bNtC7d+8MiUdiY2OZMGECaWlptGvXjkmTJmXp\ny1P7f/75h/bt27Nv3z4ee+wxZ67sfv36MWDAAOrWtWQK3njjDQIDAy/ZoS1XvD3AtQgr73IqlhiD\n43W1UxvY5GXdscBmWxFqOPChy7UwoB3QABhlCz6cBaJVNQJLK/lVhyBFNhS19YwT7Ixhrjc7rwFT\nbO3lB4D37fLsdJ9R1SQsJz7FVpZyiEO401vOgIj0E5GNIrIxLfmEuyoGwzXNHXfcwdq11oJTeno6\nR44cYfv27c7ra9eupWHDhl71lZaWdtH2LFu2jBo1arB58+bL4ojBcsau2akulsmTJ9O3b1/ASrc5\nfvz4LPrQR48eZejQoSxbtozt27dz8OBBZxY0Vzy1//bbb4mMjCQxMZF3330XgC1btpCenu50xAB9\n+vRh2rRp+Ta37PDWGd+sqp1VdZKqvup4XVLLCgARmW6LOGxwc7kR8BGAqn4PXC8iJexri1Q1RVWP\nAMuBeliH3V4QkURgKdZBuJx0jVNcJRmxnKuDlsAbtpNeDBQXkWJY6T0/twUmpmDdYHjDQlVNV9Ud\nnuxylVD0DSjhrorBcE3TsGFDpzPevn07ISEhFCtWjL///pt//vmHn376ibp166KqDB06lJCQEEJD\nQ52KTCtWrKB58+Z07dqV0NBQACZMmMCtt95Ky5Yt2bVrl9txf//9d1q0aEFYWBgtWrTgjz/+ICEh\ngWeffZavvvqK8PDwDJKKAMHBwQwfPpwGDRoQFRXFpk2baNOmDVWqVHFmqMrOznvuccoA8PjjjzNz\n5kymTZvG/v37ad68Oc2bN3deHzFiBHXq1KF+/fq5Fn+YN28ebdtaKf8DAwNp1KgR/v7+Ger8+uuv\nVK9e3SkS0bJlS6esoyue2jvkEl1lEEeOHMm4cRkXewMCAggODmb9+vW5mkNe8NYZrxWR0EtqScGw\nHYhwfFDVgVhCDqXd1HUX1Wqmn67l3ex+Im3HepCMmsy5xQdo4OKsy6vqKbLXfc6OXOstGwyGjJQr\nV45ChQrxxx9/sHbtWho0aODU9t24cSNhYWEULlyY+fPnk5CQwJYtW1i6dClDhw51KhetX7+eCRMm\nsGPHDuLj45kzZw6bN29m/vz5bNjgLi6wnGHPnj1JTEykW7duDBo0iPDwcMaNG0fnzp1JSEhwK2Tg\nSLvZuHFjevfuzdy5c/nxxx8ZNcq678/OTncMGjSIcuXKsXz5cpYvXw5Ysor169dny5YtNGnShPfe\ne8/r7/O3336jVKlSGcQj3FG1alV27txJUlISqampLFy4kL1793o9TqtWrfjrr7+4/fbbefbZZ1m8\neDGRkZGUK1cuS92oqChnfvFLibfOuBEQLyK7RCRRRLbaEd/VzveAv4gMcCnz9MjWSiwHi4g0A47Y\nmsYA7UXEX0SuxzostQErYj2kqudFpDlQ6SJt/Q543PFBRMLtt97oPnvUUTYYDBeHIzp2OOMGDRo4\nP99xh/UQw+rVq3nooYfw9fXlxhtvpGnTpk5HW69ePafK06pVq4iOjiYgIIDixYtz333uz5LGxcXR\ntWtXAHr06MHq1au9stXRX2hoKLfffjvFihWjdOnS+Pv7c/z48Wzt9JbChQs7o+jIyEiSkpK8busq\niZgdpUqV4q233qJz5840btyY4OBgp9yjNxQqVIhPPvmEzZs306lTJ6ZOncqQIUN4+umn6dixI4sX\nL3bWvRRyiW5t8rLeXZfUigJCVVVEOgBTRORZ4DDWXvhzbqqPAWbYNyHJgGs+tPXAl1iHq8ar6n4R\nmY2lT7wRSMDa270YBgHT7fELYd0c9MfSfZ5lH8bydHrlC2CuiLTnwgEug8GQDzj2jbdu3UpISAgV\nKlTg1VdfpXjx4vTp0wfIXhowMDAww+ecj5Zkxds2jojTx8cnQ/Tp4+NDamqqRzsLFSpEenq687ND\nttAdfn5+Tnt8fX0zLAXnhKskYk7ce++93HvvvQC8++67ec5N/eabb9KrVy/i4uIoXLgwsbGxNGjQ\nwHnjcinkEt3hVWRsnzCuANxpv0/2tu2VjqoeUNUuqnqLqtZT1eaqGmtfW+HQS1bVY6raXlXDVLW+\n4yS1qo5R1X6q2kJVq6nqe3b5EVVtYO+5/kdVa9qHqTzqB2cuV9WZqvq4S3+d7fFrqWp/uzxOVaur\nakNVHenQXs5k+267XbiqrlLV3q6np42escGQdxo2bMiSJUu47rrr8PX15brrruP48ePExcXRoEED\nwJI7jI2NJS0tjcOHD7Ny5UqnypIrTZo0YcGCBaSkpHDq1Cm++OILt2PecccdzJkzB4DZs2fTqFGj\nfJmLJzsrVarEjh07+Oeffzhx4kSGw1LFihXj1KlT+TJ+9erVvY6kDx06BFhyjm+++Sb/+c9/cj3e\n33//zZIlS+jZs6dTLlFEMtwQ7N69m5CQkGx6yR+8zU09GogCbgVmYD2q8zHg3TFBg8FguAzk9CjS\npSA0NJQjR444l40dZadPn3YK1kdHRxMXF0edOnUQESZNmsRNN93Ezp0ZF8wiIiLo3Lkz4eHhVKpU\nyeOJ6GnTptGnTx9efvllSpcunUGa8WLwZCfAgw8+SFhYGNWqVctw4rhfv37cddddlC1b1rlvnFcC\nAwOpUqUKe/bsoWrVqoB18OzkyZOcO3eOhQsX8t1331GrVi2efPJJpy7yqFGjqF69OgCLFy9m48aN\nzsNYntoDjBs3jueffx4RoU2bNkyfPp3Q0NAM0pJr1qxh9OjRFzUvb/BKQtE+wVsX2GQ/QoOIJNqP\n+RiuAYyEouFKxEgo/vtYsGAB8fHxxMTEFLQpbN68mcmTJ/PRRx95Vf9iJBS93TM+Z++vqt15YE4N\nDAaDwWDILdHR0Rw9erSgzQDgyJEjjB8//rKM5a0z/kxE3gFKikhfoA/g/Xl1w1VPfkoo5idDj+fP\nwYqCWN40GAzuycv+76WgVatWl20sr5yxqr4iIq2Ak1j7xqNU9X+X1DKDwWDwAlXN0wlkgyE/8WbL\nNzu8fjBLVf8nIuscbUTkOlU9dlGjGwwGw0Xg7+/P0aNHuf76641DNhQYqsrRo0ezZPrKDd6epn4U\nK09yCpCOlbFJsUQTDF4iImnAVpeiOarqNi+0XX+4qr7g6Xo+2tUMeMbxKJTBcLVw8803s2/fPg4f\nPlzQphiucfz9/bn55pvz3N7byPgZoLade9mQd1Ls1JjeMpxMwg85ISK+qnrxGecNhqsAPz8/Z/Yq\ng+FqxtvEHb9gJfow5DMiUsJOM3qr/flTEekrIhO5oOI0277WXUTW22XviIivXX5aRMbZ2wgNRCRJ\nRMaKyCY7dWkNu149EVkrIpvtn7cW1LwNBoPBcAFvI+P/YolFrMNFYEBVB10Sq/69FLWf2XbwoqrG\nisjjwEwReQ0o5cjiJSKPOyJpEakJdAYa2vmu38TKlf0hEAhsU9VRdl2wcmdHiMhjWCsb/+GC3GKq\niLTEirofuAzzNhgMBkM2eOuM38HKe7wVa8/YkDfcLlPbh+M6AdOBOh7atgAigQ22sy0KHLKvpQGZ\n9cPm2z/jgfvt9yWw8lhXw9rz98vOWBHpB/QD8C2ec/J2g8FgMOQNb51xqqo+fUktuYYRER+gJtYB\nueuAfe6qAbNU9b9urp11s0/sWMFI48Lv2SG3GC0iwcCK7OxS1XeBdwGKlK12cef2DQaDweARb/eM\nl4tIPxEpKyLXOV6X1LJri8HAT8BDwAci4ohYz7u8XwZ0FJEyYD1aJiK5lWX0Rm7RYDAYDJcZbyNj\nRwZ016jMPNqUezLvGX8DfIC1n1tPVU+JyErgeWA0VlSaKCKbVLWbiDwPfGdH0ueBgcDvuRjfG7lF\ng6QiiVQAAB5xSURBVMFgMFxmvBWK8FfVszmVGf69FClbTcv2mlrQZmTBpMM0GAxXMt4KRXi7TL3W\nyzKDwWAwGAy5JNtlahG5CSiPtbxaF+sQEUBxIOAS22a4gggtX4KNE9sVtBkGg8HwrySnPeM2WAd9\nbgZe5YIzPomVHcpgMBgMBsNFkuOesX1Y6CFVnX15TDJcieTnnnGxmsPypR+Arb225lzJYDAYCoh8\n2zNW1XTg0XyxymAwGAwGQxa8PcD1PxF5RkQqmOeMDQaDwWDIX7x1xn2wnmldiZVeMR7YmF0DEUmz\nBQ0cr1ytTYpIBxGp5fJ5hYjkGOpn6uNmEVkkIj+LyC8i8pqIFM5NH5cCW8hhq8t3My0PfQSLyLZL\nYZ/BYDAYLi9eJf1Q1bxolOVWLtCJiBQCOgBLgB157EOw8jO/partbYWjd4EJwNC89JnPNDeSlAaD\nwWAA7yNjRCRERB4UkZ6OV14GFJFRIrJBRLaJyLu203REvi+IyA/Ac8B9wMt25FjFbt7JlhDcLSKN\ncxjqTqyczTMA7NzNg4E+IhIgIr4i8oodoSaKyBO2HZEi8oOIxIvItyJS1i7va9u9RUTmiUiAXT5T\nRKbZkoS/ikjHvHwvdl9VRWSpPcYmEakiFi/b39dWEenspp2/iMywr28WkeZ2eYCIfGbPL1ZE1olI\nlIg8IiJTXNr3FZHJebXbYDAYDBeHV5GxiIwGmvH/7d15mFxVue/x788GmefJMAY5CQgJJJAgyBTG\n41EeBkUxRA3oBQdk8ooHRb0ch2O4KOhRMUKEACKcGAlyc1BACGFQCCFkIEBARoEoIDNCIMl7/1ir\nYKdSU3eqe1eS3+d58nT1qrXXfqvTT7+11t61XtgRuBb4N+A2Uvm+emqWCwR+GhHfzuNeBhwK/L/c\nZ/2I2C8/NwCYHBET8/cAq0TE7pI+RNou8qAG59+JtJz+toh4WdITwL8AewHbAkNzScEN8z7QPwEO\nj4hnc+L7HmmZ/qpCacPvAp/NfQH6AXsDOwDXABMbxFUxRVKluMMlEXEecDkwJiImSVqd9GbpI8AQ\nUjWnjUlVm26pGuvE/PoGK9Uuvl7SQOCLwAsRsbOkQUDl/+NK0jabX42It4Dj8E16ZmalaXVv6qNI\nyeCeiDhO0mbAuCbH1Fum3l/SV0mbhmwIzOWdZPzfTcYslgXs36SvSPtn12s/CBgbEQsBIuL5nLAG\nkW5YA+gC5ufjBuUkvD6wNnBdYcyr813n9+WfTSuWWKaWtA6wRURMyvG8kdv3Bq7IM/u/55WD4cDs\nwlh7k98YRMQDkh4HBub2H+f2eyXNzo9fk3QTcKik+4FVI2KpzwjJJRTNzPpEq8n49YhYLGmhpHVJ\ndXS7XSQiz/bOB4ZFxF8lnQWsXujyWpMhapUFrGcu8NGq868LbAU8TO1kLWBuROxZY7zxwBERMUvS\nsaSVguq4KmP0RL3jWhmvJ8eOI23c8gBwca0OLqFoZtY3Wr1mPF3S+sCFpFnpDGBaD85XSbzPSVqb\nNOOu5xVgnWYDStpC0o01nroRWLNybTvfwPVDYHxE/BO4Hvh8vlkMpY9qzQM2kbRnbltV0k55vHWA\n+Xkpe1SzuPLxD7TSD9ISOvCkpCPysavl69K3AEfna9ybAPuy9M/+lkpMeXl66/xabgM+ntt3BAYX\nzncn6Y3JMcAVrcZpZmbt11IyjogvRsSLETEWOBgYHRHHNTlsDS350aYxEfEiKaHPAa4G7mpw/JXA\n6fmGpO0a9OsHLKwRcwBHkm76egh4EHiDd7bxHAc8Qbp2Ogs4JiLeJL1BODu3zQQ+kPt/E7gTuIE0\nm2xI0sY0nplOKfxsKtfePwWcnJeT/wS8B5hEWpKeRSp7+NWI+FvVWOcDXZLmkJb6j42IBbl9kzze\nv+dxXiocNwG4PSJeaPZ6zMys97RUQhFA0kdI1yADuK1ybbNskr4EPBER15QdS5GkQ4H3RkS3P0Pc\nxhi6SNeD38hvaG4EBuY3HUiaDJwXEbVWFpbg7TDNzLpPLW6H2erd1OeT7kCuLGd+TtJBEXHiMsTY\nFhHx07JjqCUiJpcdA+kmuSl5aV3AFyLizXzJYRowq5VEbGZmvaulmbGkucCgvPRbKR4xJyJ2anyk\nrSiGDRsW06c33HTNzMyqtDozbvUGrnmkm4IqtmLJj9aYmZlZD7X60aaNgPslVe7iHQ78WdI1ABFx\nWG8EZx3k6XvgrPXaM9ZZLzXvY2a2Emk1GX+rV6MwMzNbibVaKGKqpG2AARHxR0lrkLamfKV3wzMz\nM1vxtXo39fGkbRE3BLYDtgTGAgf2XmjWqrzHdfEzPldGxBhJN5M+h/16bv9LRByVdz47Hni2cMyI\n/DlwMzPrY60uU58I7E7a9IKIeEjSpr0WlXVXo3KVoyKi1m3Q50XED3ozKDMza02rd1MvqGwUAW/X\nG/ZexWZmZm3QajKeKunrpC0uDwZ+wzuVlqx81VuPFmseX15oP6fQflqhfUpfB2xmZu9odZn6DFL9\n3jmkurfX0ryEovWdXlmmLpZQ3Hq9nhajMjOzZlq9m3qxpKtJdXufbXqArRCKJRSHbd7lyxJmZr2k\n4TK1krMkPUeqVDRP0rOS/LljMzOzNml2zfhUYC9geERsFBEbAu8H9pJ0Wq9HZ61aqlxl4bniNeM/\nFtpPqzqmf9+GbGZmFQ0LRUi6Bzg4Ip6rat8EuD4ihvZyfNYhhm3eFdNPWLs9g3k7TDNbSbSrUMSq\n1YkYIF83XrWnwZmZmdk7mt3A9WYPn7MVzeZD4SyXUDQz6w3NkvEukl6u0S5g9V6Ix8zMbKXTMBlH\nRFdfBWJmZrayanXTD1vJzXnqJfqf8T9tGev0F9doyzgAJ449oG1jmZmVpdXtMM3MzKyXLBfJWNJ7\nJF0p6WFJ90m6VtLAkmL5ehvG2EPSnfnzvffnkoaN+l8raf1lPa+ZmXWmjk/GkgRMAm6OiO0iYkfg\n68BmJYXU7WQsqfra+yXACXk/6UHAhEbHR8SHXGvYzGzF1fHJGNgfeCsixlYaImJmRNyat+s8R9K9\nkuZUqhVJGiFpqqQJkh6UNEbSKEnTcr/tcr/xksZKujX3OzS3Hyvpp5XzSZqcxxzDO7tdXZ6f+2Qe\nd6akX1QSr6RXJX1b0p3AnlWvaVNgfn4tiyLivnzM2pIuzjHOlvTR3P6YpI1bON/3JM2SdIekzXL7\nZpIm5fZZkj7QaBwzM+t7y0MyHgTcXee5jwBDgF2Ag4BzJPXLz+0CnAIMBj4FDIyI3UnVpk4qjNEf\n2A/4MDBWUt2PbEXEGeQKSRExStL7gKOBvfIsdxEwKndfC7g3It4fEbdVDXUeaZ/vSZI+VzjnN4GX\nImJwROwM3FQ8qIXz3RERuwC3AMfn9v8Cpub2XYG5TcYxM7M+trzfTb03cEVELAL+LmkqMBx4Gbgr\nIuYDSHoYuD4fM4c0266YEBGLgYckPQLs0I3zHwjsBtyVVtNZA3gmP7cI+G2tgyLi23lmfQhwDDAS\nGEF6Q/GJQr8XunG+N4HJ+fHdwMH58QHAp/N4i4CXJH2qwThvK5ZQ7Fp3kwY/BjMzWxbLQzKeCxxV\n57lGRXYXFB4vLny/mCVfd/Xm3AEsZMlVg3qzZQGXRMTXajz3Rk5+NUXEw8DPJV0IPCtpozxeo1KF\njc73Vryz0fgiGv/fNhqnGOPbJRRX6zfAJRTNzHrJ8rBMfROwmqTKsiuShkvaj7Qce7Skrly8Yl9g\nWjfH/5ikd+XryO8F5gGPAUNy+1bA7oX+b0mq7Mt9I3CUpE1zXBtK2qbZCSV9ON+YBjCAlDxfJM3e\nv1Tot0HVoT05343AF3L/Lknr9jRuMzPrHR2fjPNs70jg4PzRprnAWcDTpLusZwOzSEn7qxHxt26e\nYh4wFfg98PmIeAO4HXiUtKT9A2BGof8FwGxJl+cbr74BXC9pNnAD0I/mPkW6ZjwTuAwYlWfR3wU2\nyDekzWLJ5XR6eL5TgP0lzSEtX++0DHGbmVkvaFhCcUUnaTwwOSImlh1Lp1ut34DoN/pHbRnLO3CZ\n2cpCbSqhaGZmZr1spZ4ZW+uGDRsW06e7hKKZWXd4ZmxmZraccDI2MzMr2fLwOWPrAO0sodhO7boZ\nzDeCmVmZPDM2MzMrmZNxm6h2mccTJE1ufnSPzvenJs8vc6lHMzPrG07GbZB30+rTMo8R8YEmXWom\nYyX+fzcz6yD+o9weNcs8ArcCa0uaKOkBSZdXtsGUtJtSmce7JV1XqTYl6WZJ50m6RdL9eevPqyQ9\nJOm7lfElvZq/9st9Z+adu/ZRValHSf3zWOeTdhP7pqTzCmMdL+ncvvhBmZnZ0pyM26NRmcehwKnA\njqS9r/fKe1v/BDgqInYDLgK+VzjmzYjYFxgL/A44MZ/j2FxQougY4LpcCnEXYGZ1qcfcb3vg0ogY\nStri87DCHtvHARf38LWbmdky8t3UvW9aRDwJkPei7k8qCjEIuCFPlLuA+YVjrslf5wBzC6UgHwG2\nAv5R6HsXcFFOrFfnGXktj0fEHQAR8Zqkm4BDJd0PrBoRc6oPcAlFM7O+4Zlxe8wl1QeupVjKsVLa\nUKQkOyT/GxwRh9Q4ZjFLl4Jc4g1URNxCqlb1FHCZpE/XieO1qu/HAcfSYFYcERdExLCIGNa15np1\nhjUzs2XlZNweNcs8AvvV6T8P2ETSnrnvqpJ26smJc+nDZyLiQuCXwK75qWKpx6VExJ2kWfYxwBU9\nObeZmbWHk3EbNCnzWKv/m8BRwNm5VOJMoNnd0fWMAGZKugf4KPDj3P52qccGx04Abo+IF3p4bjMz\nawMXiliJ5c9AnxcRNzbr284Siu3kHbjMrJO5UITVJWl9SQ+S7rhumojNzKx3+W7qlVBEvAgMLDsO\nMzNLnIytJYO3WI/pYz5cdhhmZiskL1ObmZmVzDNja0mnllA0W1k8tvoxZYdQ0+Btt27bWBO+v7At\n49w04mdtGQf67uZOz4zNzMxK5mRsZmZWMifjEklalCsrzZI0Q9IHcvvmkiY2OXZEb9VKNjOzvuVr\nxuV6PVdbQtK/At8H9ouIp0k7dJmZ2UrAM+POsS7wAkCuP3xvfry6pIslzZF0j6T9qw+UtKGkqyXN\nlnSHpJ1z+yaSbsiz7l9IelzSxpK+I+mUwvHfk3RyH71OMzOr4mRcrjXyMvUDpCpK36nR50SAiBgM\njAQukbR6VZ//AO6JiJ2BrwOX5vb/A9wUEbsCk4DKbY+/BEYDSHoX8AlgqT2sJZ0gabqk6Yv++dIy\nvEwzM2vEy9TlKi5T7wlcKmlQVZ+9gZ8ARMQDkh5n6d2z9iYViSAibpK0kaT1cvuRuf0Pkl7Ijx+T\n9A9JQ4HNSIn8H1VjEhEXkApOsFq/Ad7E3MyslzgZd4iI+LOkjYFNqp5SC4fX6hNNjq3UM34PcFEr\nMZqZWe/wMnWHkLQD0AVUz1BvAUblPgNJS83zGvQZATwXES8DtwEfz+2HABsUjpkEfBAYDlzXxpdi\nZmbd5JlxudaQNDM/FjA6IhZJS0xozwfGSpoDLASOjYgFVX3OAi6WNBv4J/l6MOla8hWSjgamAvOB\nVyDVVJY0BXgxIhb1yqszM7OWOBmXKCK66rQ/BgzKj98gLSdX97kZuDk/fh44vMZQLwH/GhEL8zXp\n/SNiAbx949YewMeW8WWYmdkycjJesW0NTMiJ903geABJOwKTgUkR8VCJ8ZmZGaAI3yRrzQ0bNiym\nT59edhhmZssVSXdHxLBm/XwDl5mZWcm8TG0taWcJxcfGfLgt45iZrSg8MzYzMyuZk7GZmVnJnIzr\nkHSkpMibcTTq9/U2n/dmSU0v9jcZo2kJRjMz6xxOxvWNJO1g9Ykm/WomYyWl/Hwj4umIcAlGM7Pl\nhJNxDZLWBvYCPktOxpL6SbolV1m6V9I+ksbwTuWly3Ppw/slnQ/MALaSNDKXP7xX0tmFc7wq6Ye5\nvOGNkop7Un9M0jRJD0raJ/e/VdKQwvG3S9pZ0n75/DNzicV1qkowdkn6QY5htqSTcvsYSfflth/0\n8o/UzMwacDKu7QjgDxHxIPC8pF2BY4DrcpWlXYCZEXEGufJSRIzKx24PXBoRQ4G3gLOBA4AhwHBJ\nR+R+awEzcnnDqaRyhxWrRMTuwKmF9kphh8oe1atFxGzgK8CJOa59gNerXssJwLbA0Fxi8XJJG5Kq\nOe2U275b64fgEopmZn3Dybi2kcCV+fGV+fu7gOMknQUMjohX6hz7eETckR8PB26OiGcjYiGpZvC+\n+bnFwH/nx78ilTusuCp/vRvonx//BjhU0qrAZ4Dxuf124FxJJwPr5/MUHQSMrbTnrTNfBt4Axkn6\nCGk/66VExAURMSwihnWtuV6dl2tmZsvKybiKpI1IM9lxkh4DTgeOBm4lJdKngMskfbrOEK8Vh+vG\nqYtboS3IXxeRPwseEf8EbiDtQf1x4Ne5fQzwv4A1gDtq3HCmqrHJiXl34LfkVYBuxGlmZm3mZLy0\no0jLzNtERP+I2Ap4lJSIn4mIC4FfArvm/m/l2WotdwL7SdpYUhdphj01P/eufC5IS+C3tRDbOOC/\ngLvyDBdJ20XEnIg4G5gOVCfj64HPS1ol998wXxNfLyKuJS2FD8HMzErjHbiWNhIYU9X2W9Ky8GuS\n3gJeBSoz4wuA2ZJmAGcWD4qI+ZK+BkwhzVCvjYjf5adfA3aSdDeputLRzQKLiLslvQxcXGg+VdL+\npFn0fcDvgX6F58cBA3OMbwEX5tfzO0mr57hOa3ZuMzPrPS4UURJJr0bE2t08ZnNS2cQdImJxrwRW\nx2r9BkS/0T9qy1jeDtPMVhYuFLGCydeo7wTO7OtEbGZmvcszY2uJSyiamXWfZ8ZmZmbLCd/AZS1x\nCUUzs97jmbGZmVnJnIzNzMxK5mTcRKulFGscN17SUpWTiuUNJQ2R9KFliO1USWv29HgzM+sMTsbN\n1S2lmHfV6paq8oZDgB4nY9LuWd1KxpWduMzMrHM4GTdQp5TiCElTJP0amJPbPp1LEc6SdFlhiH0l\n/UnSI5VZcqW8oaR3A98Gjs7lD4+WtJakiyTdlcshHp6PWaoMYi4MsTkwRdKU3O/VQuxHSRqfH4+X\ndG7ud3a985iZWTk8S2rs7VKKkiqlFCEVWRgUEY9K2om0DeZeEfFcLk9Y0Y9UjWkH4BpgYuWJiHhT\n0reAYRHxJQBJ/wncFBGfkbQ+ME3SH0lbb1bKIC6UtGFEPC/py8D+EfFcC69lIHBQRCyqd56IeK3J\nGGZm1gs8M26sVilFgGkR8Wh+fAAwsZIQKwUcsqsjYnFE3Ads1sL5DgHOkDSTtO3l6sDW1C6D2F2/\niYhFTc6zBNczNjPrG54Z11EopThIUgBdpFKE17J0mcR625gtqOrX9LTARyNiXlUsjc5RVOyzetVz\n1TEvdZ6lBou4gFQIg9X6DfBWbWZmvcQz4/rqlVLcu6rfjcDHc/Kmapm6mVeAdQrfXweclJMvkobm\n9qXKINY5/u+S3ifpXcCRDc5b7zxmZlYCJ+P6RgKTqtp+S6o9/LaImAt8D5gqaRZwbjfOMQXYsXID\nF/AdYFVSucN78/eQyiA+kdtnFWK4APh95QYu4AxgMnATML/Beeudx8zMSuBCEdYSl1A0M+s+F4ow\nMzNbTvgGLmvJ4C3WY7pntGZmvcIzYzMzs5I5GZuZmZXMydjMzKxkvpvaWiLpFaDhJiEl2RhoZTvQ\nvtSJMUFnxtWJMUFnxtWJMUFnxtVJMW0TEZs06+QbuKxV81q5Pb+vSZreaXF1YkzQmXF1YkzQmXF1\nYkzQmXF1YkzNeJnazMysZE7GZmZmJXMytlZdUHYAdXRiXJ0YE3RmXJ0YE3RmXJ0YE3RmXJ0YU0O+\ngcvMzKxknhmbmZmVzMnYmpL0QUnzJP1F0hkdEM9WkqZIul/SXEmnlB1TkaQuSfdImlx2LACS1pc0\nUdID+We2Z9kxAUg6Lf//3SvpCknVNbj7Ko6LJD2TK5hV2jaUdIOkh/LXDTogpnPy/+FsSZMkrd+X\nMdWLq/DcVySFpI07ISZJJ+W/W3Ml/d++jKknnIytIUldwM+AfwN2BEZK2rHcqFgI/O+IeB+wB3Bi\nB8RUdApwf9lBFPwY+ENE7ADsQgfEJmkL4GRgWEQMArqAT5QUznjgg1VtZwA3RsQAUs3yvn4TWium\nG4BBEbEz8CDwtT6OCWrHhaStgINJpV772niqYpK0P3A4sHNE7AT8oIS4usXJ2JrZHfhLRDwSEW8C\nV5J+yUsTEfMjYkZ+/AopuWxRZkwVkrYEPkyqQV06SesC+wK/BIiINyPixXKjetsqwBqSVgHWBJ4u\nI4iIuAV4vqr5cOCS/PgS4IiyY4qI6yNiYf72DmDLvoypXlzZecBXgT6/CalOTF8AxkTEgtznmb6O\nq7ucjK2ZLYC/Fr5/kg5JfACS+gNDgTvLjeRtPyL9UVpcdiDZe4FngYvz0vk4SWuVHVREPEWarTwB\nzAdeiojry41qCZtFxHxIb/6ATUuOp9pngN+XHQSApMOApyJiVtmxFAwE9pF0p6SpkoaXHVAzTsbW\njGq0dcQt+JLWBn4LnBoRL3dAPIcCz0TE3WXHUrAKsCvw84gYCrxG3y+5LiVfgz0c2BbYHFhL0ifL\njWr5IOlM0qWayzsgljWBM4FvlR1LlVWADUiXsU4HJkiq9besYzgZWzNPAlsVvt+SkpYTiyStSkrE\nl0fEVWXHk+0FHCbpMdJy/gGSflVuSDwJPBkRlZWDiaTkXLaDgEcj4tmIeAu4CvhAyTEV/V1SP4D8\ntSOWOSWNBg4FRkVnfC51O9Ibqln5935LYIak95QaVfq9vyqSaaSVqj69say7nIytmbuAAZK2lfRu\n0k0215QZUH6H+0vg/og4t8xYiiLiaxGxZUT0J/2cboqIUmd7EfE34K+Sts9NBwL3lRhSxRPAHpLW\nzP+fB9IBN5YVXAOMzo9HA78rMRYgfaoB+HfgsIj4Z9nxAETEnIjYNCL659/7J4Fd8+9dma4GDgCQ\nNBB4N51TOKImJ2NrKN8w8iXgOtIfywkRMbfcqNgL+BRp5jkz//tQyTF1spOAyyXNBoYA/1lyPOSZ\n+kRgBjCH9LeolF2TJF0B/BnYXtKTkj4LjAEOlvQQ6S7hMR0Q00+BdYAb8u/82L6MqUFcpaoT00XA\ne/PHna4ERnfISkJd3oHLzMysZJ4Zm5mZlczJ2MzMrGROxmZmZiVzMjYzMyuZk7GZmVnJnIzNbCm5\n+s4PC99/RdJZbRp7vKSj2jFWk/N8LFepmlLV3l/S6/njQfdJGiuprX8LJd0saVg7x7QVm5OxmdWy\nAPhIX5fDayZXEWvVZ4EvRsT+NZ57OCKGADuTqpH1aSEIs2pOxmZWy0LSJhynVT9RPbOV9Gr+OiJv\nyj9B0oOSxkgaJWmapDmStisMc5CkW3O/Q/PxXblm7125Zu/nCuNOkfRr0gYh1fGMzOPfK+ns3PYt\nYG9grKRz6r3IvKnNn4B/ycedXjj/fxTO8eU8/r2STs1t/ZXqC1+S+0/MezVXx3eIpD9LmiHpN3lP\ndbMlOBmbWT0/A0ZJWq8bx+xCquc8mLRL2sCI2J1UUvKkQr/+wH6kcpNjJa1Omsm+FBHDgeHA8ZK2\nzf13B86MiCXqVkvaHDibtPXhEGC4pCMi4tvAdNIezqfXCzYnzwOBOZIOAQbkcw0BdpO0r6TdgOOA\n95MKDxwvaWgeYnvgglxj+GXgi1Xjbwx8AzgoInbNMX25yc/QVkJOxmZWU66EdSlwcjcOuyvXm14A\nPAxUyiLOISXgigkRsTgiHgIeAXYADgE+LWkmqSTmRqTkCDAtIh6tcb7hwM254ESlktG+LcS5XT7P\n7cD/RMTv8/kPAe4hbdO5Qz7/3sCkiHgtIl4lFbXYJ4/z14i4PT/+Ve5btAdpGfz2fL7RwDYtxGcr\nmVXKDsDMOtqPSInp4kLbQvIb+Vzk4d2F5xYUHi8ufL+YJf/eVO/DG6RynSdFxHXFJySNIJV+rKWn\nZfEq14yrx/p+RPyi6vynNhin1uuoHvOGiBjZszBtZeGZsZnVFRHPAxNIS8gVjwG75ceHA6v2YOiP\nSXpXvo78XmAeqRjJF3J5TCQNlLRWk3HuBPaTtHG+uWskMLUH8ZDP/5nKNV1JW0jaFLgFOCJXmFoL\nOBK4NR+ztaQ98+ORwG1VY94B7CWpck16zVxFyGwJnhmbWTM/JFXuqrgQ+J2kacCN1J+1NjKPlDQ3\nAz4fEW9IGkdayp6RZ9zP0uQu54iYL+lrwBTSLPTaiOhRucOIuF7S+4A/p9PzKvDJiJghaTwwLXcd\nFxH3SOpPqmQ2WtIvgIeAn1eN+aykY4ErJK2Wm78BPNiTGG3F5apNZmY9kJPx5IgYVHIotgLwMrWZ\nmVnJPDM2MzMrmWfGZmZmJXMyNjMzK5mTsZmZWcmcjM3MzErmZGxmZlYyJ2MzM7OS/X9DKUxTFgQ1\nPAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x20a3db4bcc0>"
+       "<matplotlib.figure.Figure at 0x21b5baacf28>"
       ]
      },
      "metadata": {},
@@ -394,9 +602,10 @@
     }
    ],
    "source": [
-    "graph = df.plot.barh(stacked=True, title='HiPy Registrations - 1 Nov 2017', width=0.85)\n",
+    "graph = df.plot.barh(stacked=True, title='HiPy Registrations - 1$^{st}$ Nov 2017', width=0.85)\n",
     "graph.set_xlabel(\"Number of People\")\n",
-    "graph.set_ylabel(\"Department\")"
+    "graph.set_ylabel(\"Department\")\n",
+    "graph.legend(title=\"Exposure Method\")"
    ]
   },
   {

From 1cda37d651b21dc374455648edbf4ab719da37aa Mon Sep 17 00:00:00 2001
From: sgaruby <31140929+sgaruby@users.noreply.github.com>
Date: Fri, 27 Oct 2017 16:51:51 +0100
Subject: [PATCH 3/3] Adam Ruby-Competition (UPDATED cleaned code)

Clean some of the code to give less vigorous outputs when viewing.
---
 ...Registrations for 1st Nov Event FACT.ipynb | 109 ++++--------------
 1 file changed, 25 insertions(+), 84 deletions(-)

diff --git a/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb b/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb
index 3eb0503..93529b6 100644
--- a/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb	
+++ b/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb	
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 112,
    "metadata": {
     "collapsed": true
    },
@@ -43,77 +43,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 114,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['Department', 'How did you hear about HiPy?'],\n",
-       " ['Stephenson Institute', 'Email'],\n",
-       " ['Chemistry', 'Email'],\n",
-       " ['Biology', 'Poster/Flyer'],\n",
-       " ['Management School', 'Other'],\n",
-       " ['Other', 'Other'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Other', 'Word of mouth'],\n",
-       " ['Other', 'Word of mouth'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Computer Science', 'Word of mouth'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Biology', 'Word of mouth'],\n",
-       " ['Mathematics', 'UoL video screen'],\n",
-       " ['Earth, Ocean, Ecology', 'Email'],\n",
-       " ['Institute of Infection /  Global Health', 'Email'],\n",
-       " ['External', 'Email'],\n",
-       " ['Mathematics', 'Other'],\n",
-       " ['Physics', 'Email'],\n",
-       " ['Earth, Ocean, Ecology', 'Poster/Flyer'],\n",
-       " ['Biology', 'Other'],\n",
-       " ['Biology', 'UoL video screen'],\n",
-       " ['Mathematics', 'Word of mouth'],\n",
-       " ['Mathematics', 'Other'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Chemistry', 'Email'],\n",
-       " ['Management School', 'Other'],\n",
-       " ['Physics', ''],\n",
-       " ['Management School', 'Word of mouth'],\n",
-       " ['Computer Science', 'Email'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Biology', 'Other'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Astrophysics', 'Email'],\n",
-       " ['Chemistry', 'Word of mouth'],\n",
-       " ['Physics', 'Email'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['External', 'Word of mouth'],\n",
-       " ['Mathematics', 'Word of mouth'],\n",
-       " ['Stephenson Institute', ''],\n",
-       " ['Architecture', 'Email'],\n",
-       " ['Biology', 'Email'],\n",
-       " ['Physics', ''],\n",
-       " ['External', 'Email'],\n",
-       " ['Other', 'Email'],\n",
-       " ['EEE', 'Other'],\n",
-       " ['Other', 'Other']]"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open('registration_data.csv', newline='') as f:\n",
     "    reg_csv = csv.reader(f, delimiter=',', skipinitialspace=True)\n",
     "    \n",
-    "    reg_list = [line for line in reg_csv]\n",
-    "    \n",
-    "reg_list"
+    "    reg_list = [line for line in reg_csv]"
    ]
   },
   {
@@ -125,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +75,7 @@
        " ['Other', 'Other']]"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -228,7 +165,7 @@
        "Word of mouth                          9"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -251,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 117,
    "metadata": {
     "collapsed": true
    },
@@ -272,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 118,
    "metadata": {
     "collapsed": true
    },
@@ -293,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 119,
    "metadata": {
     "collapsed": true
    },
@@ -318,8 +255,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
+   "execution_count": 120,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "freq_list = []\n",
@@ -341,8 +280,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
+   "execution_count": 121,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "dep_list[8] =  'Global Health'\n",
@@ -360,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -558,7 +499,7 @@
        "Stephenson Institute                       0.0  "
       ]
      },
-     "execution_count": 107,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -577,16 +518,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x21b5baa6c18>"
+       "<matplotlib.legend.Legend at 0x21b5bdada58>"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -594,7 +535,7 @@
      "data": {
       "image/png": 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RF5fTn/irn4J4zjgIeF1ESmJFcXuwl0KBd4GvReSAvW/cG/hURIrY158Hdtvvi4jIOqwb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\nPAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x21b5baacf28>"
+       "<matplotlib.figure.Figure at 0x21b5bd95dd8>"
       ]
      },
      "metadata": {},