diff --git a/.ipynb_checkpoints/My Notebook1-checkpoint.ipynb b/.ipynb_checkpoints/My Notebook1-checkpoint.ipynb new file mode 100644 index 0000000..2ef3584 --- /dev/null +++ b/.ipynb_checkpoints/My Notebook1-checkpoint.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 'test'" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.4 64-bit", + "language": "python", + "name": "python37464bit9d5bfe3b0c324d7da800e1dbfa603398" + }, + "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": 4 +} diff --git a/AI and ML Concepts/.ipynb_checkpoints/AI_ML_Concepts-checkpoint.ipynb b/AI and ML Concepts/.ipynb_checkpoints/AI_ML_Concepts-checkpoint.ipynb new file mode 100644 index 0000000..d03559e --- /dev/null +++ b/AI and ML Concepts/.ipynb_checkpoints/AI_ML_Concepts-checkpoint.ipynb @@ -0,0 +1,806 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "> **Jupyter slideshow:** This notebook can be displayed as slides. To view it as a slideshow in your browser, run the following cell:\n", + "\n", + "> `> jupyter nbconvert [this_notebook.ipynb] --to slides --post serve`\n", + " \n", + "> To toggle off the slideshow cell formatting, click the `CellToolbar` button, then `View > Cell Toolbar > None`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "outputs": [], + "source": [ + "#! jupyter nbconvert AI_ML_Concepts.ipynb --to slides --post serve" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "\n", + "\n", + "\n", + "# AI and ML Concepts\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "## Learning Objectives\n", + "*In this lesson, we will go over the folowing:*\n", + "\n", + "- Jupyter Notebooks\n", + "- Data science and the data science workflow.\n", + "- Test train split\n", + "- Over/underfitting concepts \n", + "- Data Science Applications\n", + "- AI ethics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### What Are Some Common Questions Asked in Data Science?\n", + "\n", + "**Machine learning more or less asks the following questions:**\n", + "\n", + "- Does X predict Y? \n", + "- Are there any distinct groups in our data?\n", + "- What are the key components of our data?\n", + "- Is one of our observations “weird”?\n", + "\n", + "**From a business perspective, Data Science can help us with use cases such as:**\n", + "\n", + "- What is the likelihood that a customer will buy this product?\n", + "- Is this a good or bad review?\n", + "- How much demand will there be for my service tomorrow?\n", + "- Is this the cheapest way to deliver my goods?\n", + "- Is there a better way to segment my marketing strategies?\n", + "- What groups of products are customers purchasing together?\n", + "- Can we automate this simple yes/no decision?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Are AI and Machine Learning different things?\n", + "\n", + "The AI onion \n", + "\n", + "- Artificial intelligence is an umbrella term that covers machine learning and deep learning \n", + "- Deep learning and neural networks are also types of machine learning algorithms \n", + "- What Data Science VS. (Machine Learning Engineer): \n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## When did this whole thing started? \n", + "---\n", + "AI is nothing new \n", + "> - it started in 1950's \n", + "> - followed by two winters\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Why now?\n", + "\n", + "---\n", + "\n", + "In the last few years there has been a lot of advancements in technologies that enable AI\n", + "> - Compute Power \n", + "> - Big Data\n", + "> - Powerful Algorithms \n", + "\n", + "\n", + "\n", + "Read more here: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai\n", + "\n", + "Can you mention examples of advancements in the above technologies? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data collected every minute\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why is AI powerful?\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Check the demo here:\n", + "http://www.r2d3.us/visual-intro-to-machine-learning-part-1/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Brain Vs. Computer \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "## Introduction: The Data Science Workflow\n", + "\n", + "---\n", + "- **Understand the Business Problem**: Develop a hypothesis-driven approach to your analysis.\n", + "- **Data Acquisition and Understanding**: Select, import, explore, and clean your data.\n", + "- **Build a Model**: engineer your data, build models, evaluate them and build the best model.\n", + "- **Deployment**: deploy your model in production and deliver ROI!\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# This is what data scientists do\n", + "\n", + "> They take care of the above lifecycle \n", + "\n", + "Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "\n", + "### Data scientist vs. machine learning engineer\n", + "While there’s some overlap, which is why some data scientists with software engineering backgrounds move into machine learning engineer roles, data scientists focus on analyzing data, providing business insights, and prototyping models, while machine learning engineers focus on coding and deploying complex, large-scale machine learning products." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What data engineers, analysts and architects do?\n", + "\n", + "ETL and Data Cleaning are the most time consuming steps\n", + "\n", + "> Data scientists work with machine learning engineers to move their models to production\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "** Times (week / month, .. ) mentioned in the chart are relative ** " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Let's Review the Data Science Lifecycle Step by Step" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Step 1. Business Understanding \n", + "\n", + "---\n", + "\n", + "- Identify the business/product objectives.\n", + "- Identify and hypothesize goals and criteria for success.\n", + "- Create a set of questions to help you identify the correct data set.\n", + "\n", + "## An Example Use Case\n", + "We work for a real estate company interested in using data science to determine the best properties to buy and resell. Specifically, your company would like to identify the characteristics of residential houses that estimate their sale price and the cost-effectiveness of doing renovations.\n", + "\n", + "> #### Identify the Business/Product Objectives\n", + "\n", + "The customer tells us their business goals are to accurately predict prices for houses (so that they can sell them for as large a profit as possible) and to identify which kinds of features in the housing market would be more likely to lead to foreclosure and other abnormal sales (which could represent more profitable sales for the company).\n", + "\n", + "> #### Identify and Hypothesize Goals and Criteria for Success\n", + "\n", + "Ultimately, the customer wants us to:\n", + "* Deliver a presentation to the real estate team.\n", + "* Write a business report discussing results, procedures used, and rationales.\n", + "* Build an API that provides estimated returns.\n", + "\n", + "> #### Create a Set of Questions to Help You Identify the Correct Data Set\n", + "\n", + "* Can you think of questions that would help this customer deliver on their business goals? \n", + "* What sort of features or columns would you want to see in the data?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "> **Instructor Note:** before going to data acquisition, you can ask questions such as \n", + "> * What would an ideal data set look like? \n", + "> * Describe the dataset that you think would be ideal for this use case" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Step 2. Data Acquisition\n", + "\n", + "** Ideal Data vs. Available Data** \n", + "\n", + "Oftentimes, we'll start by identifying the *ideal data* we would want for a project.\n", + "\n", + "Then, during the data acquisition phase, we'll learn about the limitations on the types of data actually available. We have to decide if these limitations will inhibit our ability to answer our question of interest or if we can work with what we have to find a reasonable and reliable answer.\n", + "\n", + "For example, we provide a set of housing data for Ames, Iowa, which [includes](./extra-materials/ames_data_documentation.txt):\n", + "\n", + "- 20 continuous variables indicating square footage.\n", + "- 14 discrete variables indicating number of each room type.\n", + "- 46 categorical variables containing 2–28 classes each, e.g., street type (gravel/paved) and neighborhood (city district name).\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "### ** Review the Dataset**\n", + "\n", + "Take a moment to look through the data description. How closely does the set match the ideal data that you envisioned? Would it be sufficient for our purposes? What limitations does it have?\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "This is possibly the hardest step in the data science workflow. At this stage, it's common to realize that the problem you're trying to solve may not be solvable with the information available. The data could be incomplete, non-existant, or unable to meet the criteria necessary to answer your question. \n", + "\n", + "That said, you now have a better feel for the data that's available and the information they could contain. You can now identify a new, answerable question that ultimately helps you solve or better understand your problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "> **Instructor Note**: During the **Framing** phases, guide students toward the following questions:\n", + "> - Where are the data coming from?\n", + "> - How do the data fit together?\n", + "> - Are there enough data?\n", + "> - Do our data appropriately align with the question/problem statement?\n", + "> - Can the data set be trusted? How was it collected?\n", + "> - Is this data set aggregated? Can we use the aggregation, or do we need to obtain it pre-aggregation?\n", + "> - What are necessary resources, requirements, assumptions, and constraints?\n", + "> - Can we import data from the web (Google Analytics, HTML, XML)?\n", + "> - Can we import data from a file (CSV, XML, TXT, JSON)?\n", + "> - Can we import data from a pre-existing database (SQL)?\n", + "> - Can we set up local or remote data structures?\n", + "> - What are the most appropriate tools for working with the data?\n", + "> - Do these tools align with the format and size of the data set?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 2.1 Data Wrangling & Cleaning\n", + "\n", + "This is by far the most time consuming step of Data Science Lifecyle\n", + "\n", + "For the Ames housing dataset we discussed,\n", + "- What if the data are in different databases and we have to consolidate them?\n", + "- What if the values for some columns in the dataset are missing or in wrong format? \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "** we will review and practice the data cleaning process as part of this course. **\n", + "\n", + "## AI ML algorithms are picky eaters \n", + "### They like coockis more than flour (Raw data)\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Step 3. Modeling\n", + "** What is a Model? **\n", + "\n", + "- Using Machine Learning algorithms we build a model from input data (image, text, ...)\n", + "> - In case of housing data set discussed above we can build a model that learns how to predict price of a house\n", + "- The resulted model is a representative of the data used for training \n", + "\n", + "\n", + "\n", + "> - The size of the output model can be alot smaller than the training data " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## There are many algorithms that can be used to build a model\n", + "\n", + "\n", + "\n", + "> - Depending on the use case, requirements and available data, a model will be selected!\n", + "\n", + "## Data scientists use one of these available algorithms and tune it for their use case\n", + "> - Most these algorithms are available in public and open source libraries \n", + "\n", + "> - Most data Scientists do no build their own algorithms, they just customize and tune an existing algorithm " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + " \n", + "## 3.1 Supervised vs. Unsupervised Learning \n", + "\n", + "There are two main categories of machine learning: supervised learning and unsupervised learning.\n", + "\n", + "**Supervised learning (a.k.a., “predictive modeling”):** \n", + "_Classification and regression_\n", + "- Predicts an outcome based on input data.\n", + " - Example: Predicts whether an email is spam or ham.\n", + "- Attempts to generalize.\n", + "- Requires past data on the element we want to predict (the target).\n", + "\n", + "**Unsupervised learning:** \n", + "_Clustering and dimensionality reduction_\n", + "- Extracts structure from data.\n", + " - Example: Segmenting grocery store shoppers into “clusters” that exhibit similar behaviors.\n", + "- Attempts to represent.\n", + "- **Does not require** past data on the element we want to predict.\n", + "\n", + "\n", + "\n", + "\n", + "Oftentimes, we may combine both types of machine learning in a project to reduce the cost of data collection by learning a better representation. This is referred to as transfer learning.\n", + "\n", + "Unsupervised learning tends to present more difficult problems because its goals are amorphous. Supervised learning has goals that are almost too clear and can lead people into the trap of optimizing metrics without considering business value." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 3.2 Feature Engineering \n", + "\n", + "#### Data Enrichment \n", + "\n", + "- Machine learning algorithms need the data to be engineered before they consume it\n", + "\n", + "\n", + "> - We need feature engineering to enrich the raw data \n", + "\n", + "Suppose, we want to predict the customers next purchase using a dataset looking like this:\n", + "\n", + "\n", + "How can we enrich this data?\n", + "\n", + "\n", + "Here, creating the new feature “Age” is an example of feature engineering.\n", + "\n", + "Now, the steps to do feature engineering are as follows:\n", + "\n", + "> - Brainstorm features.\n", + "> - Create features.\n", + "> - Check how the features work with the model.\n", + "> - Start again from first until the features work perfectly.\n", + "\n", + "\n", + "So here is another definition of feature engineering:\n", + "\n", + "### Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "> **Instructor Note**: What are other feature engineering ideas for above dataset:\n", + "> - Can we use dates to make an API call and get the interest rate for each point at time?\n", + "> - Can we use dates to calculate stock performance indexes and see if it affects the sale price of a house? \n", + "\n", + "You can get creative and creative/engineer new features that can make your model stand out!!!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 3.3 OverFitting and UnderFitting\n", + "\n", + "\n", + "What is a Good Model? \n", + "Arguably, Machine Learning models have one sole purpose; to generalize well.\n", + "\n", + "> Generalization is the model’s ability to give sensible outputs to sets of input that it has never seen before.\n", + "\n", + "### An Example\n", + "Let’s say we’re trying to build a Machine Learning model for the following data set.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### A fit model\n", + "Training the Linear Regression model in our example is all about minimizing the total distance (i.e. cost) between the line we’re trying to fit and the actual data points. This goes through multiple iterations until we find the relatively “optimal” configuration of our line within the data set. This is exactly where overfitting and underfitting occur.\n", + "\n", + "> In Linear Regression, we would like our model to follow a line similar to the following:\n", + "\n", + "\n", + ">The line above could give a very likely prediction for the new input, as, in terms of Machine Learning, the outputs are expected to follow the trend seen in the training set." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Overfitting\n", + "When we run our training algorithm on the data set, we allow the overall cost (i.e. distance from each point to the line) to become smaller with more iterations. Leaving this training algorithm run for long leads to minimal overall cost. However, this means that the line will be fit into all the points (including noise), catching secondary patterns that may not be needed for the generalizability of the model.\n", + "> Referring back to our example, if we leave the learning algorithm running for long, it cold end up fitting the line in the following manner:\n", + "\n", + "\n", + ">This looks good, right? Yes, but is it reliable? Well, not really.\n", + "\n", + "> If the model does not capture the dominant trend that we can all see (positively increasing, in our case), it can’t predict a likely output for an input that it has never seen before — defying the purpose of Machine Learning to begin with!\n", + "\n", + ">Overfitting is the case where the overall cost is really small, but the generalization of the model is unreliable. This is due to the model learning “too much” from the training data set.\n", + "\n", + ">> We always want to find the trend, not fit the line to all the data points." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Underfitting\n", + "We want the model to learn from the training data, but we don’t want it to learn too much (i.e. too many patterns). One solution could be to stop the training earlier. However, this could lead the model to not learn enough patterns from the training data, and possibly not even capture the dominant trend. This case is called underfitting.\n", + "\n", + ">Underfitting is the case where the model has “ not learned enough” from the training data, resulting in low generalization and unreliable predictions.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "### Bias-variance trade-off\n", + "\n", + "So what is the right measure? Depending on the model at hand, a performance that lies between overfitting and underfitting is more desirable. This trade-off is the most integral aspect of Machine Learning model training. As we discussed, Machine Learning models fulfill their purpose when they generalize well. Generalization is bound by the two undesirable outcomes — high bias and high variance. Detecting whether the model suffers from either one is the sole responsibility of the model developer." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 3.4 Test Train Split\n", + "\n", + "Should we use all the data for training a model? \n", + "\n", + "\n", + "> Data Scientists usually keep parts of the data for testing the model performance\n", + "\n", + "\n", + "> if we use all the data for training then we do not have any way of evaluating the model performance. \n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Cross Validation\n", + "\n", + "> why to have a fixed test and train split when we can use different combination of test and train data?\n", + "\n", + "\n", + "\n", + "Instead of using one fixed set of the data for test and train we can use cross validation. \n", + "> - In Cross Validation we use different parts of the data for test and training purposes to evaluate the model performance\n", + "\n", + "> - Then average performance of different test and train splits can be used as final performance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Step 4. Use Cases\n", + "** What are some of the use cases for AI/ML ? **\n", + "\n", + "- Nearly all occupations will be affected by automation\n", + "> - But only about 5 percent of occupations could be fully automated by currently demonstrated technologies.\n", + "- Many more occupations have portions of their constituent activities that are automatable: \n", + "> - we find that about 30 percent of the activities in 60 percent of all occupations could be automated. \n", + "\n", + "\n", + "\n", + "> - the size of the output model can be alot smaller than the training data " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 4.1 Example AI Use Cases \n", + "\n", + "> **Instructor Note**: This is a good section in which to provide your own work (or side project) experience as well! These are just a couple of options:\n", + "- [This Person is not real](https://thispersondoesnotexist.com/)\n", + "- [Google Quick Draw](https://quickdraw.withgoogle.com/)\n", + "- [Deep Dream Generator](https://deepdreamgenerator.com/)\n", + "- Add your own!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## AI Ethics\n", + "\n", + "As an engineer or some other non-philosopher it can be very easy to forget about ethics and simply build systems for the sake of building cool things. We must, however, be aware of the potential outcomes of our build decisions when it comes to highly complex, sophisticated, and potentially impactful systems.\n", + "\n", + "\n", + "### Data-Biasing\n", + "\n", + "The quality of your model is usually a direct result of the quality and quantity of your data. \n", + "\n", + "You can imagine a myriad of situations in which classification problems could go wrong because of bias in past data. From an ethical perspective, I think we can all agree that systems which discriminate against individuals on the basis of race, gender, age, ethnicity, etc. \n", + "\n", + "Some bad outcomes:\n", + "> Security systems trained to discriminate based on an individual’s race or gender.\n", + " \n", + "> An AI based resume review tool that values the gender of applicants\n", + "\n", + "> Facial recognition systems that lack a diverse training set, resulting in only detecting the race for which they are trained\n", + " \n", + "> Court systems (AI judges/juries) with past biased rulings against certain races as the training data\n", + "\n", + "\n", + "\n", + "### How to Avoid Bias?\n", + "Ultimately, the majority of these issues can be solved by some human-centered approaches to acquiring, cleaning, labeling, and annotating data. But this can be especially difficult. \n", + "\n", + "> Our AI in many ways are mirrors of the people who train them.\n", + "\n", + "\n", + "We need to develop some approach for identifying AI that are not performing within our ethical framework and are producing net bad outcomes for society." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Next Steps\n", + "\n", + "- Install Anaconda (https://www.anaconda.com/distribution/)\n", + "\n", + "> Make sure to install the latest (python 3.7) version \n", + "\n", + "\n", + "\n", + "- We will go over the Python Programming language in next session" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI and ML Concepts/AI_ML_Concepts.ipynb b/AI and ML Concepts/AI_ML_Concepts.ipynb index d03559e..09c1db4 100644 --- a/AI and ML Concepts/AI_ML_Concepts.ipynb +++ b/AI and ML Concepts/AI_ML_Concepts.ipynb @@ -798,9 +798,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.8.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Data Manipulation and Exploration/.ipynb_checkpoints/Data Manipulation-checkpoint.ipynb b/Data Manipulation and Exploration/.ipynb_checkpoints/Data Manipulation-checkpoint.ipynb new file mode 100644 index 0000000..ee3a856 --- /dev/null +++ b/Data Manipulation and Exploration/.ipynb_checkpoints/Data Manipulation-checkpoint.ipynb @@ -0,0 +1,17479 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **Jupyter slideshow:** This notebook can be displayed as slides. To view it as a slideshow in your browser, run the following cell:\n", + "\n", + "> `> jupyter nbconvert [this_notebook.ipynb] --to slides --post serve`\n", + " \n", + "> To toggle off the slideshow cell formatting, click the `CellToolbar` button, then `View > Cell Toolbar > None`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "# Data Manipulation with Python" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# ! jupyter nbconvert Python_intro.ipynb --to slides --post serve" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "*In this lesson, we will go over the following:*\n", + "\n", + "- Define what Pandas is and how it relates to data science.\n", + "- Manipulate Pandas `DataFrames` and `Series`.\n", + "- Filter and sort data using Pandas.\n", + "- Manipulate `DataFrame` columns.\n", + "- Know how to handle null and missing values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson Guide\n", + "\n", + "- [What Is Pandas?](#pandas)\n", + "- [Reading Files, Selecting Columns, and Summarizing](#reading-files)\n", + " - [Exercise 1](#exercise-one)\n", + " \n", + " \n", + "- [Filtering and Sorting](#filtering-and-sorting)\n", + " - [Exercise 2](#exercise-two)\n", + " \n", + " \n", + "- [Renaming, Adding, and Removing Columns](#columns)\n", + "- [Handling Missing Values](#missing-values)\n", + " - [Exercise 3](#exercise-three)\n", + " \n", + " \n", + "- [Split-Apply-Combine](#split-apply-combine)\n", + " - [Exercise 4](#exercise-four)\n", + " \n", + " \n", + "- [Selecting Multiple Columns and Filtering Rows](#multiple-columns)\n", + "- [Joining (Merging) DataFrames](#joining-dataframes)\n", + "- [OPTIONAL: Other Commonly Used Features](#other-features)\n", + "- [OPTIONAL: Other Less Used Features of Pandas](#uncommon-features)\n", + "- [Summary](#summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## What Is Pandas?\n", + "\n", + "- **Objective:** Define what Pandas is and how it relates to data science.\n", + "\n", + "Pandas is a Python library that primarily adds two new datatypes to Python: `DataFrame` and `Series`.\n", + "\n", + "- A `Series` is a sequence of items, where each item has a unique label (called an `index`).\n", + "- A `DataFrame` is a table of data. Each row has a unique label (the `row index`), and each column has a unique label (the `column index`).\n", + "- Note that each column in a `DataFrame` can be considered a `Series` (`Series` index).\n", + "\n", + "> Behind the scenes, these datatypes use the NumPy (\"Numerical Python\") library. NumPy primarily adds the `ndarray` (n-dimensional array) datatype to Pandas. An `ndarray` is similar to a Python list — it stores ordered data. However, it differs in three respects:\n", + "> - Each element has the same datatype (typically fixed-size, e.g., a 32-bit integer).\n", + "> - Elements are stored contiguously (immediately after each other) in memory for fast retrieval.\n", + "> - The total size of an `ndarray` is fixed.\n", + "\n", + "> Storing `Series` and `DataFrame` data in `ndarray`s makes Pandas faster and uses less memory than standard Python datatypes. Many libraries (such as scikit-learn) accept `ndarray`s as input rather than Pandas datatypes, so we will frequently convert between them.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why Pandas is so popular ?\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Pandas\n", + "\n", + "Pandas is frequently used in data science because it offers a large set of commonly used functions, is relatively fast, and has a large community. Because many data science libraries also use NumPy to manipulate data, you can easily transfer data between libraries (as we will often do in this class!).\n", + "\n", + "Pandas is a large library that typically takes a lot of practice to learn. It heavily overrides Python operators, resulting in odd-looking syntax. For example, given a `DataFrame` called `cars` which contains a column `mpg`, we might want to view all cars with mpg over 35. To do this, we might write: `cars[cars['mpg'] > 35]`. In standard Python, this would most likely give a syntax error. (**Challenge:** Using only built-in datatypes, can you define `cars` and `mpg` to make this expression valid?)\n", + "\n", + "Pandas also highly favors certain patterns of use. For example, looping through a `DataFrame` row by row is highly discouraged. Instead, Pandas favors using **vectorized functions** that operate column by column. (This is because each column is stored separately as an `ndarray`, and NumPy is optimized for operating on `ndarray`s.)\n", + "\n", + "Do not be discouraged if Pandas feels overwhelming. Gradually, as you use it, you will become familiar with which methods to use and the \"Pandas way\" of thinking about and manipulating data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Class Methods and Attributes\n", + "\n", + "Pandas `DataFrame`s are Pandas class objects and therefore come with attributes and methods. To access these, follow the variable name with a dot. For example, given a `DataFrame` called `users`:\n", + "\n", + "```\n", + "- users.index # accesses the `index` attribute -- note there are no parentheses. attributes are not callable\n", + "- users.head() # calls the `head` method (since there are open/closed parentheses)\n", + "- users.head(10) # calls the `head` method with parameter `10`, indicating the first 10 rows. this is the same as:\n", + "- users.head(n=10) # calls the `head` method, setting the named parameter `n` to have a value of `10`.\n", + "```\n", + "\n", + "We know that the `head` method accepts one parameter with an optional name of `n` because it is in the documentation for that method. Let's see how to view the documentation next." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## First lets import Pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Pandas into Python\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Reading Files, Selecting Columns, and Summarizing" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# using the read_csv method of pandas we read file \n", + "users = pd.read_csv('./data/user.tbl', sep='|')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Examine the users data.**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "Pandas `DataFrame`s have structural similarities with Python-style lists and dictionaries. \n", + "In the example below, we select a column of data using the name of the column in a similar manner to how we select a dictionary value with the dictionary key." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 M\n", + "1 F\n", + "2 M\n", + "3 M\n", + "4 F\n", + "5 M\n", + "6 M\n", + "7 M\n", + "8 M\n", + "9 M\n", + "10 F\n", + "11 F\n", + "12 M\n", + "13 M\n", + "14 F\n", + "15 M\n", + "16 M\n", + "17 F\n", + "18 M\n", + "19 F\n", + "20 M\n", + "21 M\n", + "22 F\n", + "23 F\n", + "24 M\n", + "25 M\n", + "26 F\n", + "27 M\n", + "28 M\n", + "29 M\n", + " ..\n", + "913 F\n", + "914 M\n", + "915 M\n", + "916 F\n", + "917 M\n", + "918 M\n", + "919 F\n", + "920 F\n", + "921 F\n", + "922 M\n", + "923 M\n", + "924 F\n", + "925 M\n", + "926 M\n", + "927 M\n", + "928 M\n", + "929 F\n", + "930 M\n", + "931 M\n", + "932 M\n", + "933 M\n", + "934 M\n", + "935 M\n", + "936 M\n", + "937 F\n", + "938 F\n", + "939 M\n", + "940 M\n", + "941 F\n", + "942 M\n", + "Name: gender, Length: 943, dtype: object" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select a column — returns a Pandas Series (essentially an ndarray with an index)\n", + "users['gender']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# DataFrame columns are Pandas Series.\n", + "type(users['age'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 M\n", + "1 F\n", + "2 M\n", + "3 M\n", + "4 F\n", + "5 M\n", + "6 M\n", + "7 M\n", + "8 M\n", + "9 M\n", + "10 F\n", + "11 F\n", + "12 M\n", + "13 M\n", + "14 F\n", + "15 M\n", + "16 M\n", + "17 F\n", + "18 M\n", + "19 F\n", + "20 M\n", + "21 M\n", + "22 F\n", + "23 F\n", + "24 M\n", + "25 M\n", + "26 F\n", + "27 M\n", + "28 M\n", + "29 M\n", + " ..\n", + "913 F\n", + "914 M\n", + "915 M\n", + "916 F\n", + "917 M\n", + "918 M\n", + "919 F\n", + "920 F\n", + "921 F\n", + "922 M\n", + "923 M\n", + "924 F\n", + "925 M\n", + "926 M\n", + "927 M\n", + "928 M\n", + "929 F\n", + "930 M\n", + "931 M\n", + "932 M\n", + "933 M\n", + "934 M\n", + "935 M\n", + "936 M\n", + "937 F\n", + "938 F\n", + "939 M\n", + "940 M\n", + "941 F\n", + "942 M\n", + "Name: gender, Length: 943, dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select one column using the DataFrame attribute.\n", + "users.gender\n", + "# While a useful shorthand, these attributes only exist\n", + "# if the column name has no punctuations or spaces." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Summarize (describe) the data.**
\n", + "Pandas has a bunch of built-in methods to quickly summarize your data and provide you with a quick general understanding." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idage
count943.000000943.000000
mean472.00000034.051962
std272.36495112.192740
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" + ], + "text/plain": [ + " user_id age\n", + "count 943.000000 943.000000\n", + "mean 472.000000 34.051962\n", + "std 272.364951 12.192740\n", + "min 1.000000 7.000000\n", + "25% 236.500000 25.000000\n", + "50% 472.000000 31.000000\n", + "75% 707.500000 43.000000\n", + "max 943.000000 73.000000" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe all numeric columns.\n", + "users.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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count943.000000943.000000943943943
uniqueNaNNaN221795
topNaNNaNMstudent55414
freqNaNNaN6701969
mean472.00000034.051962NaNNaNNaN
std272.36495112.192740NaNNaNNaN
min1.0000007.000000NaNNaNNaN
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" + ], + "text/plain": [ + " user_id age gender occupation zip_code\n", + "count 943.000000 943.000000 943 943 943\n", + "unique NaN NaN 2 21 795\n", + "top NaN NaN M student 55414\n", + "freq NaN NaN 670 196 9\n", + "mean 472.000000 34.051962 NaN NaN NaN\n", + "std 272.364951 12.192740 NaN NaN NaN\n", + "min 1.000000 7.000000 NaN NaN NaN\n", + "25% 236.500000 25.000000 NaN NaN NaN\n", + "50% 472.000000 31.000000 NaN NaN NaN\n", + "75% 707.500000 43.000000 NaN NaN NaN\n", + "max 943.000000 73.000000 NaN NaN NaN" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe all columns, including non-numeric.\n", + "users.describe(include='all')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 943\n", + "unique 2\n", + "top M\n", + "freq 670\n", + "Name: gender, dtype: object" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Describe a single column — recall that \"users.gender\" refers to a Series.\n", + "users.gender.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34.05196182396607" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate the mean of the ages.\n", + "users.age.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Draw a histogram of a column (the distribution of ages).\n", + "users.age.hist(bins=5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Count the number of occurrences of each value.**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "M 670\n", + "F 273\n", + "Name: gender, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.gender.value_counts() \n", + "\n", + "# Most useful for categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "55414 9\n", + "55105 6\n", + "55337 5\n", + "20009 5\n", + "10003 5\n", + "27514 4\n", + "55408 4\n", + "55454 4\n", + "55409 3\n", + "63108 3\n", + "02215 3\n", + "94043 3\n", + "60201 3\n", + "61820 3\n", + "97301 3\n", + "55113 3\n", + "48103 3\n", + "10021 3\n", + "61801 3\n", + "11217 3\n", + "14216 3\n", + "55106 3\n", + "22903 3\n", + "55108 3\n", + "55104 3\n", + "22902 3\n", + "80525 3\n", + "62901 3\n", + "80123 2\n", + "90034 2\n", + " ..\n", + "98133 1\n", + "77841 1\n", + "V0R2M 1\n", + "59717 1\n", + "55125 1\n", + "60615 1\n", + "95662 1\n", + "03755 1\n", + "40503 1\n", + "78155 1\n", + "06811 1\n", + "79070 1\n", + "80913 1\n", + "97408 1\n", + "63146 1\n", + "25652 1\n", + "15213 1\n", + "53713 1\n", + "85281 1\n", + "99835 1\n", + "55320 1\n", + "75094 1\n", + "48823 1\n", + "92660 1\n", + "97203 1\n", + "53188 1\n", + "31909 1\n", + "01940 1\n", + "90254 1\n", + "94403 1\n", + "Name: zip_code, Length: 795, dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# How many of each zip codes do we have? \n", + "users.zip_code.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "M 670\n", + "F 273\n", + "Name: gender, dtype: int64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.gender.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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F34EW8NexFPKbgTuA+6fZk1RVF0y7By3xiv5VLsndwK8DDwCfrKqvT7klSa8wBv2rXJKfsvTPY3jpOqj/PJYEGPSS1D0fmJKkzhn0ktQ5g16SOmfQS1Ln/g8Y3oofMUpQ2gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "users.gender.value_counts().plot(kind='bar') # Quick plot by category" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30 39\n", + "25 38\n", + "22 37\n", + "28 36\n", + "27 35\n", + "26 34\n", + "24 33\n", + "29 32\n", + "20 32\n", + "32 28\n", + "23 28\n", + "35 27\n", + "21 27\n", + "33 26\n", + "31 25\n", + "19 23\n", + "44 23\n", + "39 22\n", + "40 21\n", + "36 21\n", + "42 21\n", + "51 20\n", + "50 20\n", + "48 20\n", + "49 19\n", + "37 19\n", + "18 18\n", + "34 17\n", + "38 17\n", + "45 15\n", + " ..\n", + "47 14\n", + "43 13\n", + "46 12\n", + "53 12\n", + "55 11\n", + "41 10\n", + "57 9\n", + "60 9\n", + "52 6\n", + "56 6\n", + "15 6\n", + "13 5\n", + "16 5\n", + "54 4\n", + "63 3\n", + "14 3\n", + "65 3\n", + "70 3\n", + "61 3\n", + "59 3\n", + "58 3\n", + "64 2\n", + "68 2\n", + "69 2\n", + "62 2\n", + "11 1\n", + "10 1\n", + "73 1\n", + "66 1\n", + "7 1\n", + "Name: age, Length: 61, dtype: int64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Can also be used with numeric variables\n", + "# Try .sort_index() to sort by indices or .sort_values() to sort by counts.\n", + "users.age.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7 1\n", + "10 1\n", + "11 1\n", + "13 5\n", + "14 3\n", + "15 6\n", + "16 5\n", + "17 14\n", + "18 18\n", + "19 23\n", + "20 32\n", + "21 27\n", + "22 37\n", + "23 28\n", + "24 33\n", + "25 38\n", + "26 34\n", + "27 35\n", + "28 36\n", + "29 32\n", + "30 39\n", + "31 25\n", + "32 28\n", + "33 26\n", + "34 17\n", + "35 27\n", + "36 21\n", + "37 19\n", + "38 17\n", + "39 22\n", + " ..\n", + "41 10\n", + "42 21\n", + "43 13\n", + "44 23\n", + "45 15\n", + "46 12\n", + "47 14\n", + "48 20\n", + "49 19\n", + "50 20\n", + "51 20\n", + "52 6\n", + "53 12\n", + "54 4\n", + "55 11\n", + "56 6\n", + "57 9\n", + "58 3\n", + "59 3\n", + "60 9\n", + "61 3\n", + "62 2\n", + "63 3\n", + "64 2\n", + "65 3\n", + "66 1\n", + "68 2\n", + "69 2\n", + "70 3\n", + "73 1\n", + "Name: age, Length: 61, dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.age.value_counts().sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "users.age.value_counts().sort_index().plot(kind='bar', figsize=(16,5)); # Bigger plot by increasing age\n", + "plt.xlabel('Age');\n", + "plt.ylabel('Number of users');\n", + "plt.title('Number of users per age');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Excercise 1 \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "2 Algeria 25 0 14 \n", + "3 Andorra 245 138 312 \n", + "4 Angola 217 57 45 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.0 AS \n", + "1 4.9 EU \n", + "2 0.7 AF \n", + "3 12.4 EU \n", + "4 5.9 AF " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read drinks.csv into a DataFrame called \"drinks\".\n", + "import pandas as pd\n", + "drinks = pd.read_csv('./data/drinks.csv')\n", + "drinks.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(193, 6)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the head and the tail.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Examine the default index, datatypes, and shape.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the beer_servings Series.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the average beer_servings for the entire data set.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Count the number of occurrences of each \"continent\" value and see if it looks correct.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Filtering and Sorting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "- **Objective:** Filter and sort data using Pandas.\n", + "\n", + "We can use simple operator comparisons on columns to extract relevant or drop irrelevant information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Logical filtering: Only show users with age < 20.**" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 False\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 True\n", + " ... \n", + "913 False\n", + "914 False\n", + "915 False\n", + "916 False\n", + "917 False\n", + "918 False\n", + "919 False\n", + "920 False\n", + "921 False\n", + "922 False\n", + "923 False\n", + "924 True\n", + "925 False\n", + "926 False\n", + "927 False\n", + "928 False\n", + "929 False\n", + "930 False\n", + "931 False\n", + "932 False\n", + "933 False\n", + "934 False\n", + "935 False\n", + "936 False\n", + "937 False\n", + "938 False\n", + "939 False\n", + "940 False\n", + "941 False\n", + "942 False\n", + "Name: age, Length: 943, dtype: bool" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a Series of Booleans…\n", + "# In Pandas, this comparison is performed element-wise on each row of data.\n", + "young_bool = users.age < 20\n", + "young_bool" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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........................
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943 rows × 7 columns

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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.0 AS \n", + "1 4.9 EU " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter DataFrame to only include European countries.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter DataFrame to only include European countries with wine_servings > 300.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the average beer_servings for all of Europe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Determine which 10 countries have the highest total_litres_of_pure_alcohol.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Renaming, Adding, and Removing Columns\n", + "\n", + "\n", + "\n", + "- **Objective:** Manipulate `DataFrame` columns." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwinetotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
5Antigua & Barbuda102128454.9NaN
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
11Bahamas122176516.3NaN
12Bahrain426372.0AS
13Bangladesh0000.0AS
14Barbados143173366.3NaN
15Belarus1423734214.4EU
16Belgium2958421210.5EU
17Belize26311486.8NaN
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
.....................
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
174Trinidad & Tobago19715676.4NaN
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
184USA249158848.7NaN
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
\n", + "

193 rows × 6 columns

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" + ], + "text/plain": [ + " country beer spirit wine total_litres_of_pure_alcohol \\\n", + "0 Afghanistan 0 0 0 0.0 \n", + "1 Albania 89 132 54 4.9 \n", + "2 Algeria 25 0 14 0.7 \n", + "3 Andorra 245 138 312 12.4 \n", + "4 Angola 217 57 45 5.9 \n", + "5 Antigua & Barbuda 102 128 45 4.9 \n", + "6 Argentina 193 25 221 8.3 \n", + "7 Armenia 21 179 11 3.8 \n", + "8 Australia 261 72 212 10.4 \n", + "9 Austria 279 75 191 9.7 \n", + "10 Azerbaijan 21 46 5 1.3 \n", + "11 Bahamas 122 176 51 6.3 \n", + "12 Bahrain 42 63 7 2.0 \n", + "13 Bangladesh 0 0 0 0.0 \n", + "14 Barbados 143 173 36 6.3 \n", + "15 Belarus 142 373 42 14.4 \n", + "16 Belgium 295 84 212 10.5 \n", + "17 Belize 263 114 8 6.8 \n", + "18 Benin 34 4 13 1.1 \n", + "19 Bhutan 23 0 0 0.4 \n", + "20 Bolivia 167 41 8 3.8 \n", + "21 Bosnia-Herzegovina 76 173 8 4.6 \n", + "22 Botswana 173 35 35 5.4 \n", + "23 Brazil 245 145 16 7.2 \n", + "24 Brunei 31 2 1 0.6 \n", + "25 Bulgaria 231 252 94 10.3 \n", + "26 Burkina Faso 25 7 7 4.3 \n", + "27 Burundi 88 0 0 6.3 \n", + "28 Cote d'Ivoire 37 1 7 4.0 \n", + "29 Cabo Verde 144 56 16 4.0 \n", + ".. ... ... ... ... ... \n", + "163 Suriname 128 178 7 5.6 \n", + "164 Swaziland 90 2 2 4.7 \n", + "165 Sweden 152 60 186 7.2 \n", + "166 Switzerland 185 100 280 10.2 \n", + "167 Syria 5 35 16 1.0 \n", + "168 Tajikistan 2 15 0 0.3 \n", + "169 Thailand 99 258 1 6.4 \n", + "170 Macedonia 106 27 86 3.9 \n", + "171 Timor-Leste 1 1 4 0.1 \n", + "172 Togo 36 2 19 1.3 \n", + "173 Tonga 36 21 5 1.1 \n", + "174 Trinidad & Tobago 197 156 7 6.4 \n", + "175 Tunisia 51 3 20 1.3 \n", + "176 Turkey 51 22 7 1.4 \n", + "177 Turkmenistan 19 71 32 2.2 \n", + "178 Tuvalu 6 41 9 1.0 \n", + "179 Uganda 45 9 0 8.3 \n", + "180 Ukraine 206 237 45 8.9 \n", + "181 United Arab Emirates 16 135 5 2.8 \n", + "182 United Kingdom 219 126 195 10.4 \n", + "183 Tanzania 36 6 1 5.7 \n", + "184 USA 249 158 84 8.7 \n", + "185 Uruguay 115 35 220 6.6 \n", + "186 Uzbekistan 25 101 8 2.4 \n", + "187 Vanuatu 21 18 11 0.9 \n", + "188 Venezuela 333 100 3 7.7 \n", + "189 Vietnam 111 2 1 2.0 \n", + "190 Yemen 6 0 0 0.1 \n", + "191 Zambia 32 19 4 2.5 \n", + "192 Zimbabwe 64 18 4 4.7 \n", + "\n", + " continent \n", + "0 AS \n", + "1 EU \n", + "2 AF \n", + "3 EU \n", + "4 AF \n", + "5 NaN \n", + "6 SA \n", + "7 EU \n", + "8 OC \n", + "9 EU \n", + "10 EU \n", + "11 NaN \n", + "12 AS \n", + "13 AS \n", + "14 NaN \n", + "15 EU \n", + "16 EU \n", + "17 NaN \n", + "18 AF \n", + "19 AS \n", + "20 SA \n", + "21 EU \n", + "22 AF \n", + "23 SA \n", + "24 AS \n", + "25 EU \n", + "26 AF \n", + "27 AF \n", + "28 AF \n", + "29 AF \n", + ".. ... \n", + "163 SA \n", + "164 AF \n", + "165 EU \n", + "166 EU \n", + "167 AS \n", + "168 AS \n", + "169 AS \n", + "170 EU \n", + "171 AS \n", + "172 AF \n", + "173 OC \n", + "174 NaN \n", + "175 AF \n", + "176 AS \n", + "177 AS \n", + "178 OC \n", + "179 AF \n", + "180 EU \n", + "181 AS \n", + "182 EU \n", + "183 AF \n", + "184 NaN \n", + "185 SA \n", + "186 AS \n", + "187 OC \n", + "188 SA \n", + "189 AS \n", + "190 AS \n", + "191 AF \n", + "192 AF \n", + "\n", + "[193 rows x 6 columns]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rename one or more columns in a single output using value mapping.\n", + "drinks.rename(columns={'beer_servings':'beer', 'wine_servings':'wine','spirit_servings':'spirit'})" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.0 AS \n", + "1 4.9 EU " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename one or more columns in the original DataFrame.\n", + "drinks.rename(columns={'beer_servings':'beer', 'wine_servings':'wine'\n", + " ,'spirit_servings':'spirit','total_litres_of_pure_alcohol':'liters'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
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" + ], + "text/plain": [ + " country beer spirit wine liters continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Easy Column Operations**
\n", + "Rather than having to reference indexes and create for loops to do column-wise operations, Pandas is smart and knows that when we add columns together we want to add the values in each row together." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinentservingsmL
0Afghanistan0000.0AS00.0
1Albania89132544.9EU2754900.0
2Algeria250140.7AF39700.0
3Andorra24513831212.4EU69512400.0
4Angola21757455.9AF3195900.0
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" + ], + "text/plain": [ + " country beer spirit wine liters continent servings mL\n", + "0 Afghanistan 0 0 0 0.0 AS 0 0.0\n", + "1 Albania 89 132 54 4.9 EU 275 4900.0\n", + "2 Algeria 25 0 14 0.7 AF 39 700.0\n", + "3 Andorra 245 138 312 12.4 EU 695 12400.0\n", + "4 Angola 217 57 45 5.9 AF 319 5900.0" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add a new column as a function of existing columns.\n", + "drinks['servings'] = drinks.beer + drinks.spirit + drinks.wine\n", + "drinks['mL'] = drinks.liters * 1000\n", + "\n", + "drinks.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Removing Columns**" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
5Antigua & Barbuda102128454.9NaN
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
11Bahamas122176516.3NaN
12Bahrain426372.0AS
13Bangladesh0000.0AS
14Barbados143173366.3NaN
15Belarus1423734214.4EU
16Belgium2958421210.5EU
17Belize26311486.8NaN
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
.....................
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
174Trinidad & Tobago19715676.4NaN
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
184USA249158848.7NaN
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
\n", + "

193 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " country beer spirit wine liters continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU\n", + "2 Algeria 25 0 14 0.7 AF\n", + "3 Andorra 245 138 312 12.4 EU\n", + "4 Angola 217 57 45 5.9 AF\n", + "5 Antigua & Barbuda 102 128 45 4.9 NaN\n", + "6 Argentina 193 25 221 8.3 SA\n", + "7 Armenia 21 179 11 3.8 EU\n", + "8 Australia 261 72 212 10.4 OC\n", + "9 Austria 279 75 191 9.7 EU\n", + "10 Azerbaijan 21 46 5 1.3 EU\n", + "11 Bahamas 122 176 51 6.3 NaN\n", + "12 Bahrain 42 63 7 2.0 AS\n", + "13 Bangladesh 0 0 0 0.0 AS\n", + "14 Barbados 143 173 36 6.3 NaN\n", + "15 Belarus 142 373 42 14.4 EU\n", + "16 Belgium 295 84 212 10.5 EU\n", + "17 Belize 263 114 8 6.8 NaN\n", + "18 Benin 34 4 13 1.1 AF\n", + "19 Bhutan 23 0 0 0.4 AS\n", + "20 Bolivia 167 41 8 3.8 SA\n", + "21 Bosnia-Herzegovina 76 173 8 4.6 EU\n", + "22 Botswana 173 35 35 5.4 AF\n", + "23 Brazil 245 145 16 7.2 SA\n", + "24 Brunei 31 2 1 0.6 AS\n", + "25 Bulgaria 231 252 94 10.3 EU\n", + "26 Burkina Faso 25 7 7 4.3 AF\n", + "27 Burundi 88 0 0 6.3 AF\n", + "28 Cote d'Ivoire 37 1 7 4.0 AF\n", + "29 Cabo Verde 144 56 16 4.0 AF\n", + ".. ... ... ... ... ... ...\n", + "163 Suriname 128 178 7 5.6 SA\n", + "164 Swaziland 90 2 2 4.7 AF\n", + "165 Sweden 152 60 186 7.2 EU\n", + "166 Switzerland 185 100 280 10.2 EU\n", + "167 Syria 5 35 16 1.0 AS\n", + "168 Tajikistan 2 15 0 0.3 AS\n", + "169 Thailand 99 258 1 6.4 AS\n", + "170 Macedonia 106 27 86 3.9 EU\n", + "171 Timor-Leste 1 1 4 0.1 AS\n", + "172 Togo 36 2 19 1.3 AF\n", + "173 Tonga 36 21 5 1.1 OC\n", + "174 Trinidad & Tobago 197 156 7 6.4 NaN\n", + "175 Tunisia 51 3 20 1.3 AF\n", + "176 Turkey 51 22 7 1.4 AS\n", + "177 Turkmenistan 19 71 32 2.2 AS\n", + "178 Tuvalu 6 41 9 1.0 OC\n", + "179 Uganda 45 9 0 8.3 AF\n", + "180 Ukraine 206 237 45 8.9 EU\n", + "181 United Arab Emirates 16 135 5 2.8 AS\n", + "182 United Kingdom 219 126 195 10.4 EU\n", + "183 Tanzania 36 6 1 5.7 AF\n", + "184 USA 249 158 84 8.7 NaN\n", + "185 Uruguay 115 35 220 6.6 SA\n", + "186 Uzbekistan 25 101 8 2.4 AS\n", + "187 Vanuatu 21 18 11 0.9 OC\n", + "188 Venezuela 333 100 3 7.7 SA\n", + "189 Vietnam 111 2 1 2.0 AS\n", + "190 Yemen 6 0 0 0.1 AS\n", + "191 Zambia 32 19 4 2.5 AF\n", + "192 Zimbabwe 64 18 4 4.7 AF\n", + "\n", + "[193 rows x 6 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop multiple columns.\n", + "drinks.drop(['mL', 'servings'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinentservingsmL
0Afghanistan0000.0AS00.0
1Albania89132544.9EU2754900.0
2Algeria250140.7AF39700.0
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" + ], + "text/plain": [ + " country beer spirit wine liters continent servings mL\n", + "0 Afghanistan 0 0 0 0.0 AS 0 0.0\n", + "1 Albania 89 132 54 4.9 EU 275 4900.0\n", + "2 Algeria 25 0 14 0.7 AF 39 700.0" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop on the original DataFrame rather than returning a new one.\n", + "drinks.drop(['mL', 'servings'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
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" + ], + "text/plain": [ + " country beer spirit wine liters continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU\n", + "2 Algeria 25 0 14 0.7 AF" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Handling Missing Values\n", + "\n", + "\n", + "\n", + "\n", + "- **Objective:** Know how to handle null and missing values.\n", + "\n", + "Sometimes, values will be missing from the source data or as a byproduct of manipulations. It is very important to detect missing data. Missing data can:\n", + "\n", + "- Make the entire row ineligible to be training data for a model.\n", + "- Hint at data-collection errors.\n", + "- Indicate improper conversion or manipulation.\n", + "- Actually not be missing — it sometimes means \"zero,\" \"false,\" \"not applicable,\" or \"entered an empty string.\"\n", + "\n", + "For example, a `.csv` file might have a missing value in some data fields:\n", + "\n", + "```\n", + "tool_name,material,cost\n", + "hammer,wood,8\n", + "chainsaw,,\n", + "wrench,metal,5\n", + "```\n", + "\n", + "When this data is imported, \"null\" values will be stored in the second row (in the \"material\" and \"cost\" columns).\n", + "\n", + "> In Pandas, a \"null\" value is either `None` or `np.NaN` (Not a Number). Many fixed-size numeric datatypes (such as integers) do not have a way of representing `np.NaN`. So, numeric columns will be promoted to floating-point datatypes that do support it. For example, when importing the `.csv` file above:\n", + "\n", + "> - **For the second row:** `None` will be stored in the \"material\" column and `np.NaN` will be stored in the \"cost\" column. The entire \"cost\" column (stored as a single `ndarray`) must be stored as floating-point values to accommodate the `np.NaN`, even though an integer `8` is in the first row." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Missing values are usually excluded in calculations by default.\n", + "drinks.continent.value_counts() # Excludes missing values in the calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "NaN 23\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Includes missing values\n", + "drinks.continent.value_counts(dropna=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 True\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 True\n", + "12 False\n", + "13 False\n", + "14 True\n", + "15 False\n", + "16 False\n", + "17 True\n", + "18 False\n", + "19 False\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + " ... \n", + "163 False\n", + "164 False\n", + "165 False\n", + "166 False\n", + "167 False\n", + "168 False\n", + "169 False\n", + "170 False\n", + "171 False\n", + "172 False\n", + "173 False\n", + "174 True\n", + "175 False\n", + "176 False\n", + "177 False\n", + "178 False\n", + "179 False\n", + "180 False\n", + "181 False\n", + "182 False\n", + "183 False\n", + "184 True\n", + "185 False\n", + "186 False\n", + "187 False\n", + "188 False\n", + "189 False\n", + "190 False\n", + "191 False\n", + "192 False\n", + "Name: continent, Length: 193, dtype: bool" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count the missing values — sum() works because True is 1 and False is 0.\n", + "drinks.continent.isnull()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "23" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find missing values in a Series.\n", + "# True if missing, False if not missing\n", + "drinks.continent.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinent
5Antigua & Barbuda102128454.9NaN
11Bahamas122176516.3NaN
14Barbados143173366.3NaN
17Belize26311486.8NaN
32Canada2401221008.2NaN
41Costa Rica14987114.4NaN
43Cuba9313754.2NaN
50Dominica52286266.6NaN
51Dominican Republic19314796.2NaN
54El Salvador526922.2NaN
68Grenada1994382811.9NaN
69Guatemala536922.2NaN
73Haiti132615.9NaN
74Honduras699823.0NaN
84Jamaica829793.4NaN
109Mexico2386855.5NaN
122Nicaragua7811813.5NaN
130Panama285104187.2NaN
143St. Kitts & Nevis194205327.7NaN
144St. Lucia1713157110.1NaN
145St. Vincent & the Grenadines120221116.3NaN
174Trinidad & Tobago19715676.4NaN
184USA249158848.7NaN
\n", + "
" + ], + "text/plain": [ + " country beer spirit wine liters continent\n", + "5 Antigua & Barbuda 102 128 45 4.9 NaN\n", + "11 Bahamas 122 176 51 6.3 NaN\n", + "14 Barbados 143 173 36 6.3 NaN\n", + "17 Belize 263 114 8 6.8 NaN\n", + "32 Canada 240 122 100 8.2 NaN\n", + "41 Costa Rica 149 87 11 4.4 NaN\n", + "43 Cuba 93 137 5 4.2 NaN\n", + "50 Dominica 52 286 26 6.6 NaN\n", + "51 Dominican Republic 193 147 9 6.2 NaN\n", + "54 El Salvador 52 69 2 2.2 NaN\n", + "68 Grenada 199 438 28 11.9 NaN\n", + "69 Guatemala 53 69 2 2.2 NaN\n", + "73 Haiti 1 326 1 5.9 NaN\n", + "74 Honduras 69 98 2 3.0 NaN\n", + "84 Jamaica 82 97 9 3.4 NaN\n", + "109 Mexico 238 68 5 5.5 NaN\n", + "122 Nicaragua 78 118 1 3.5 NaN\n", + "130 Panama 285 104 18 7.2 NaN\n", + "143 St. Kitts & Nevis 194 205 32 7.7 NaN\n", + "144 St. Lucia 171 315 71 10.1 NaN\n", + "145 St. Vincent & the Grenadines 120 221 11 6.3 NaN\n", + "174 Trinidad & Tobago 197 156 7 6.4 NaN\n", + "184 USA 249 158 84 8.7 NaN" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only show rows where continent is not missing.\n", + "drinks[drinks.continent.isnull()]\n", + "#drinks[drinks.continent.notnull()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Find missing values in a `DataFrame`.**" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "country 0\n", + "beer 0\n", + "spirit 0\n", + "wine 0\n", + "liters 0\n", + "continent 23\n", + "dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
12Bahrain426372.0AS
13Bangladesh0000.0AS
15Belarus1423734214.4EU
16Belgium2958421210.5EU
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
30Cambodia576512.2AS
31Cameroon147145.8AF
33Central African Republic17211.8AF
34Chad15110.4AF
.....................
161Sri Lanka1610402.2AS
162Sudan81301.7AF
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
\n", + "

170 rows × 6 columns

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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
5Antigua & Barbuda102128454.9NaN
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
11Bahamas122176516.3NaN
12Bahrain426372.0AS
13Bangladesh0000.0AS
14Barbados143173366.3NaN
15Belarus1423734214.4EU
16Belgium2958421210.5EU
17Belize26311486.8NaN
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
.....................
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
174Trinidad & Tobago19715676.4NaN
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
184USA249158848.7NaN
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
\n", + "

193 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " country beer spirit wine liters continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU\n", + "2 Algeria 25 0 14 0.7 AF\n", + "3 Andorra 245 138 312 12.4 EU\n", + "4 Angola 217 57 45 5.9 AF\n", + "5 Antigua & Barbuda 102 128 45 4.9 NaN\n", + "6 Argentina 193 25 221 8.3 SA\n", + "7 Armenia 21 179 11 3.8 EU\n", + "8 Australia 261 72 212 10.4 OC\n", + "9 Austria 279 75 191 9.7 EU\n", + "10 Azerbaijan 21 46 5 1.3 EU\n", + "11 Bahamas 122 176 51 6.3 NaN\n", + "12 Bahrain 42 63 7 2.0 AS\n", + "13 Bangladesh 0 0 0 0.0 AS\n", + "14 Barbados 143 173 36 6.3 NaN\n", + "15 Belarus 142 373 42 14.4 EU\n", + "16 Belgium 295 84 212 10.5 EU\n", + "17 Belize 263 114 8 6.8 NaN\n", + "18 Benin 34 4 13 1.1 AF\n", + "19 Bhutan 23 0 0 0.4 AS\n", + "20 Bolivia 167 41 8 3.8 SA\n", + "21 Bosnia-Herzegovina 76 173 8 4.6 EU\n", + "22 Botswana 173 35 35 5.4 AF\n", + "23 Brazil 245 145 16 7.2 SA\n", + "24 Brunei 31 2 1 0.6 AS\n", + "25 Bulgaria 231 252 94 10.3 EU\n", + "26 Burkina Faso 25 7 7 4.3 AF\n", + "27 Burundi 88 0 0 6.3 AF\n", + "28 Cote d'Ivoire 37 1 7 4.0 AF\n", + "29 Cabo Verde 144 56 16 4.0 AF\n", + ".. ... ... ... ... ... ...\n", + "163 Suriname 128 178 7 5.6 SA\n", + "164 Swaziland 90 2 2 4.7 AF\n", + "165 Sweden 152 60 186 7.2 EU\n", + "166 Switzerland 185 100 280 10.2 EU\n", + "167 Syria 5 35 16 1.0 AS\n", + "168 Tajikistan 2 15 0 0.3 AS\n", + "169 Thailand 99 258 1 6.4 AS\n", + "170 Macedonia 106 27 86 3.9 EU\n", + "171 Timor-Leste 1 1 4 0.1 AS\n", + "172 Togo 36 2 19 1.3 AF\n", + "173 Tonga 36 21 5 1.1 OC\n", + "174 Trinidad & Tobago 197 156 7 6.4 NaN\n", + "175 Tunisia 51 3 20 1.3 AF\n", + "176 Turkey 51 22 7 1.4 AS\n", + "177 Turkmenistan 19 71 32 2.2 AS\n", + "178 Tuvalu 6 41 9 1.0 OC\n", + "179 Uganda 45 9 0 8.3 AF\n", + "180 Ukraine 206 237 45 8.9 EU\n", + "181 United Arab Emirates 16 135 5 2.8 AS\n", + "182 United Kingdom 219 126 195 10.4 EU\n", + "183 Tanzania 36 6 1 5.7 AF\n", + "184 USA 249 158 84 8.7 NaN\n", + "185 Uruguay 115 35 220 6.6 SA\n", + "186 Uzbekistan 25 101 8 2.4 AS\n", + "187 Vanuatu 21 18 11 0.9 OC\n", + "188 Venezuela 333 100 3 7.7 SA\n", + "189 Vietnam 111 2 1 2.0 AS\n", + "190 Yemen 6 0 0 0.1 AS\n", + "191 Zambia 32 19 4 2.5 AF\n", + "192 Zimbabwe 64 18 4 4.7 AF\n", + "\n", + "[193 rows x 6 columns]" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop a row only if ALL values are missing.\n", + "drinks.dropna(how='all')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Filling Missing Values**
\n", + "You may have noticed that the continent North America (NA) does not appear in the `continent` column. Pandas read in the original data and saw \"NA\", thought it was a missing value, and converted it to a `NaN`, missing value." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 AS\n", + "1 EU\n", + "2 AF\n", + "3 EU\n", + "4 AF\n", + "5 NA\n", + "6 SA\n", + "7 EU\n", + "8 OC\n", + "9 EU\n", + "10 EU\n", + "11 NA\n", + "12 AS\n", + "13 AS\n", + "14 NA\n", + "15 EU\n", + "16 EU\n", + "17 NA\n", + "18 AF\n", + "19 AS\n", + "20 SA\n", + "21 EU\n", + "22 AF\n", + "23 SA\n", + "24 AS\n", + "25 EU\n", + "26 AF\n", + "27 AF\n", + "28 AF\n", + "29 AF\n", + " ..\n", + "163 SA\n", + "164 AF\n", + "165 EU\n", + "166 EU\n", + "167 AS\n", + "168 AS\n", + "169 AS\n", + "170 EU\n", + "171 AS\n", + "172 AF\n", + "173 OC\n", + "174 NA\n", + "175 AF\n", + "176 AS\n", + "177 AS\n", + "178 OC\n", + "179 AF\n", + "180 EU\n", + "181 AS\n", + "182 EU\n", + "183 AF\n", + "184 NA\n", + "185 SA\n", + "186 AS\n", + "187 OC\n", + "188 SA\n", + "189 AS\n", + "190 AS\n", + "191 AF\n", + "192 AF\n", + "Name: continent, Length: 193, dtype: object" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fill in missing values with \"NA\" — this is dangerous to do without manually verifying them!\n", + "drinks.continent.fillna(value='NA')" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# Modifies \"drinks\" in-place\n", + "drinks.continent.fillna(value='NA', inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 3\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State Time\n", + "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", + "1 Willingboro NaN OTHER NJ 6/30/1930 20:00" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read ufo.csv into a DataFrame called \"ufo\".\n", + "ufo= pd.read_csv('./data/ufo.csv')\n", + "ufo.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(80543, 5)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the shape of the DataFrame.\n", + "ufo.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# What are the three most common colors reported?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename any columns with spaces so that they don't contain spaces.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# For reports in VA, what's the most common city?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Print a DataFrame containing only reports from Arlington, VA.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Count the number of missing values in each column.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the original size: 80543\n" + ] + }, + { + "data": { + "text/plain": [ + "15510" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# How many rows remain if you drop all rows with any missing values?\n", + "print('the original size:', ufo.shape[0])\n", + "ufo.dropna().shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Split-Apply-Combine\n", + "\n", + "\n", + "\n", + "Split-apply-combine is a pattern for analyzing data. Suppose we want to find mean beer consumption per country. Then:\n", + "\n", + "- **Split:** We group data by continent.\n", + "- **Apply:** For each group, we apply the `mean()` function to find the average beer consumption.\n", + "- **Combine:** We now combine the continent names with the `mean()`s to produce a summary of our findings." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
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" + ], + "text/plain": [ + " country beer spirit wine liters continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU\n", + "2 Algeria 25 0 14 0.7 AF" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "continent\n", + "AF 61.471698\n", + "AS 37.045455\n", + "EU 193.777778\n", + "NA 145.434783\n", + "OC 89.687500\n", + "SA 175.083333\n", + "Name: beer, dtype: float64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For each continent, calculate the mean beer servings.\n", + "drinks.groupby('continent').beer.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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beerspiritwineliters
continent
AF61.47169816.33962316.2641513.007547
AS37.04545560.8409099.0681822.170455
EU193.777778132.555556142.2222228.617778
NA145.434783165.73913024.5217395.995652
OC89.68750058.43750035.6250003.381250
SA175.083333114.75000062.4166676.308333
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" + ], + "text/plain": [ + " beer spirit wine liters\n", + "continent \n", + "AF 61.471698 16.339623 16.264151 3.007547\n", + "AS 37.045455 60.840909 9.068182 2.170455\n", + "EU 193.777778 132.555556 142.222222 8.617778\n", + "NA 145.434783 165.739130 24.521739 5.995652\n", + "OC 89.687500 58.437500 35.625000 3.381250\n", + "SA 175.083333 114.750000 62.416667 6.308333" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For each continent, calculate the mean of all numeric columns.\n", + "drinks.groupby('continent').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
continent
AF53.061.47169880.5578160.015.0032.076.00376.0
AS44.037.04545549.4697250.04.2517.560.50247.0
EU45.0193.77777899.6315690.0127.00219.0270.00361.0
NA23.0145.43478379.6211631.080.00143.0198.00285.0
OC16.089.68750096.6414120.021.0052.5125.75306.0
SA12.0175.08333365.24284593.0129.50162.5198.00333.0
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" + ], + "text/plain": [ + " count mean std min 25% 50% 75% max\n", + "continent \n", + "AF 53.0 61.471698 80.557816 0.0 15.00 32.0 76.00 376.0\n", + "AS 44.0 37.045455 49.469725 0.0 4.25 17.5 60.50 247.0\n", + "EU 45.0 193.777778 99.631569 0.0 127.00 219.0 270.00 361.0\n", + "NA 23.0 145.434783 79.621163 1.0 80.00 143.0 198.00 285.0\n", + "OC 16.0 89.687500 96.641412 0.0 21.00 52.5 125.75 306.0\n", + "SA 12.0 175.083333 65.242845 93.0 129.50 162.5 198.00 333.0" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For each continent, describe beer servings.\n", + "drinks.groupby('continent').beer.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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beerspirit...wineliters
countmeanstdmin25%50%75%maxcountmean...75%maxcountmeanstdmin25%50%75%max
continent
AF53.061.47169880.5578160.015.0032.076.00376.053.016.339623...13.00233.053.03.0075472.6475570.00.702.304.7009.1
AS44.037.04545549.4697250.04.2517.560.50247.044.060.840909...8.00123.044.02.1704552.7702390.00.101.202.42511.5
EU45.0193.77777899.6315690.0127.00219.0270.00361.045.0132.555556...195.00370.045.08.6177783.3584550.06.6010.0010.90014.4
NA23.0145.43478379.6211631.080.00143.0198.00285.023.0165.739130...34.00100.023.05.9956522.4093532.24.306.307.00011.9
OC16.089.68750096.6414120.021.0052.5125.75306.016.058.437500...23.25212.016.03.3812503.3456880.01.001.756.15010.4
SA12.0175.08333365.24284593.0129.50162.5198.00333.012.0114.750000...98.50221.012.06.3083331.5311663.85.256.857.3758.3
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6 rows × 32 columns

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" + ], + "text/plain": [ + " beer \\\n", + " count mean std min 25% 50% 75% max \n", + "continent \n", + "AF 53.0 61.471698 80.557816 0.0 15.00 32.0 76.00 376.0 \n", + "AS 44.0 37.045455 49.469725 0.0 4.25 17.5 60.50 247.0 \n", + "EU 45.0 193.777778 99.631569 0.0 127.00 219.0 270.00 361.0 \n", + "NA 23.0 145.434783 79.621163 1.0 80.00 143.0 198.00 285.0 \n", + "OC 16.0 89.687500 96.641412 0.0 21.00 52.5 125.75 306.0 \n", + "SA 12.0 175.083333 65.242845 93.0 129.50 162.5 198.00 333.0 \n", + "\n", + " spirit ... wine liters \\\n", + " count mean ... 75% max count mean std \n", + "continent ... \n", + "AF 53.0 16.339623 ... 13.00 233.0 53.0 3.007547 2.647557 \n", + "AS 44.0 60.840909 ... 8.00 123.0 44.0 2.170455 2.770239 \n", + "EU 45.0 132.555556 ... 195.00 370.0 45.0 8.617778 3.358455 \n", + "NA 23.0 165.739130 ... 34.00 100.0 23.0 5.995652 2.409353 \n", + "OC 16.0 58.437500 ... 23.25 212.0 16.0 3.381250 3.345688 \n", + "SA 12.0 114.750000 ... 98.50 221.0 12.0 6.308333 1.531166 \n", + "\n", + " \n", + " min 25% 50% 75% max \n", + "continent \n", + "AF 0.0 0.70 2.30 4.700 9.1 \n", + "AS 0.0 0.10 1.20 2.425 11.5 \n", + "EU 0.0 6.60 10.00 10.900 14.4 \n", + "NA 2.2 4.30 6.30 7.000 11.9 \n", + "OC 0.0 1.00 1.75 6.150 10.4 \n", + "SA 3.8 5.25 6.85 7.375 8.3 \n", + "\n", + "[6 rows x 32 columns]" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For each continent, describe all numeric columns.\n", + "drinks.groupby('continent').describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "NA 23\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64\n" + ] + } + ], + "source": [ + "# For each continent, count the number of rows.\n", + "print((drinks.continent.value_counts())) # should be the same" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 4 : TAKE HOME\n", + "\n", + "\n", + "\n", + "Use the \"users\" `DataFrame` or \"users\" file in the Data folder to complete the following." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idagegenderoccupationzip_codeboolageis_young
0124Mtechnician85711True0
1253Fother94043False0
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" + ], + "text/plain": [ + " user_id age gender occupation zip_code boolage is_young\n", + "0 1 24 M technician 85711 True 0\n", + "1 2 53 F other 94043 False 0" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "# For each occupation in \"users\", count the number of occurrences.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "# For each occupation, calculate the mean age.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# For each occupation, calculate the minimum and maximum ages.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "# For each combination of occupation and gender, calculate the mean age.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Selecting Multiple Columns and Filtering Rows" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
3AbileneNaNDISKKS6/1/1931 13:00
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State Time\n", + "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", + "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", + "2 Holyoke NaN OVAL CO 2/15/1931 14:00\n", + "3 Abilene NaN DISK KS 6/1/1931 13:00\n", + "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityState
0IthacaNY
1WillingboroNJ
2HolyokeCO
3AbileneKS
4New York Worlds FairNY
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" + ], + "text/plain": [ + " City State\n", + "0 Ithaca NY\n", + "1 Willingboro NJ\n", + "2 Holyoke CO\n", + "3 Abilene KS\n", + "4 New York Worlds Fair NY" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Or, combine into a single step (this is a Python list inside of the Python index operator!).\n", + "ufo[['City', 'State']].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Use `loc` to select columns by name.**" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Ithaca\n", + "1 Willingboro\n", + "2 Holyoke\n", + "3 Abilene\n", + "4 New York Worlds Fair\n", + "5 Valley City\n", + "6 Crater Lake\n", + "7 Alma\n", + "8 Eklutna\n", + "9 Hubbard\n", + "10 Fontana\n", + "11 Waterloo\n", + "12 Belton\n", + "13 Keokuk\n", + "14 Ludington\n", + "15 Forest Home\n", + "16 Los Angeles\n", + "17 Hapeville\n", + "18 Oneida\n", + "19 Bering Sea\n", + "20 Nebraska\n", + "21 NaN\n", + "22 NaN\n", + "23 Owensboro\n", + "24 Wilderness\n", + "25 San Diego\n", + "26 Wilderness\n", + "27 Clovis\n", + "28 Los Alamos\n", + "29 Ft. Duschene\n", + " ... \n", + "80513 Manahawkin\n", + "80514 New Bedford\n", + "80515 Woodbridge\n", + "80516 Glendale\n", + "80517 Laconia\n", + "80518 Langhorne\n", + "80519 Glen Ellyn\n", + "80520 Waynesburg\n", + "80521 Canal Winchester\n", + "80522 Lawrence\n", + "80523 Ellicott City\n", + "80524 Olympia\n", + "80525 Iowa City\n", + "80526 Marshfield\n", + "80527 Seattle\n", + "80528 North Royalton\n", + "80529 Deer Park\n", + "80530 Glen St. Mary\n", + "80531 Abingdon\n", + "80532 Lawrence\n", + "80533 Melbourne\n", + "80534 Burleson\n", + "80535 Elizabethtown\n", + "80536 Wyoming\n", + "80537 Neligh\n", + "80538 Neligh\n", + "80539 Uhrichsville\n", + "80540 Tucson\n", + "80541 Orland park\n", + "80542 Loughman\n", + "Name: City, Length: 80543, dtype: object" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# \"loc\" locates the values from the first parameter (colon means \"all rows\"), and the column \"City\".\n", + "ufo.loc[:, 'City'] " + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityState
0IthacaNY
1WillingboroNJ
2HolyokeCO
3AbileneKS
4New York Worlds FairNY
5Valley CityND
6Crater LakeCA
7AlmaMI
8EklutnaAK
9HubbardOR
10FontanaCA
11WaterlooAL
12BeltonSC
13KeokukIA
14LudingtonMI
15Forest HomeCA
16Los AngelesCA
17HapevilleGA
18OneidaTN
19Bering SeaAK
20NebraskaNE
21NaNLA
22NaNLA
23OwensboroKY
24WildernessWV
25San DiegoCA
26WildernessWV
27ClovisNM
28Los AlamosNM
29Ft. DuscheneUT
.........
80513ManahawkinNJ
80514New BedfordMA
80515WoodbridgeVA
80516GlendaleCA
80517LaconiaNH
80518LanghornePA
80519Glen EllynIL
80520WaynesburgPA
80521Canal WinchesterOH
80522LawrenceMA
80523Ellicott CityMD
80524OlympiaWA
80525Iowa CityIA
80526MarshfieldMA
80527SeattleWA
80528North RoyaltonOH
80529Deer ParkWA
80530Glen St. MaryFL
80531AbingdonVA
80532LawrenceMA
80533MelbourneIA
80534BurlesonTX
80535ElizabethtownKY
80536WyomingPA
80537NelighNE
80538NelighNE
80539UhrichsvilleOH
80540TucsonAZ
80541Orland parkIL
80542LoughmanFL
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80543 rows × 2 columns

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" + ], + "text/plain": [ + " City State\n", + "0 Ithaca NY\n", + "1 Willingboro NJ\n", + "2 Holyoke CO\n", + "3 Abilene KS\n", + "4 New York Worlds Fair NY\n", + "5 Valley City ND\n", + "6 Crater Lake CA\n", + "7 Alma MI\n", + "8 Eklutna AK\n", + "9 Hubbard OR\n", + "10 Fontana CA\n", + "11 Waterloo AL\n", + "12 Belton SC\n", + "13 Keokuk IA\n", + "14 Ludington MI\n", + "15 Forest Home CA\n", + "16 Los Angeles CA\n", + "17 Hapeville GA\n", + "18 Oneida TN\n", + "19 Bering Sea AK\n", + "20 Nebraska NE\n", + "21 NaN LA\n", + "22 NaN LA\n", + "23 Owensboro KY\n", + "24 Wilderness WV\n", + "25 San Diego CA\n", + "26 Wilderness WV\n", + "27 Clovis NM\n", + "28 Los Alamos NM\n", + "29 Ft. Duschene UT\n", + "... ... ...\n", + "80513 Manahawkin NJ\n", + "80514 New Bedford MA\n", + "80515 Woodbridge VA\n", + "80516 Glendale CA\n", + "80517 Laconia NH\n", + "80518 Langhorne PA\n", + "80519 Glen Ellyn IL\n", + "80520 Waynesburg PA\n", + "80521 Canal Winchester OH\n", + "80522 Lawrence MA\n", + "80523 Ellicott City MD\n", + "80524 Olympia WA\n", + "80525 Iowa City IA\n", + "80526 Marshfield MA\n", + "80527 Seattle WA\n", + "80528 North Royalton OH\n", + "80529 Deer Park WA\n", + "80530 Glen St. Mary FL\n", + "80531 Abingdon VA\n", + "80532 Lawrence MA\n", + "80533 Melbourne IA\n", + "80534 Burleson TX\n", + "80535 Elizabethtown KY\n", + "80536 Wyoming PA\n", + "80537 Neligh NE\n", + "80538 Neligh NE\n", + "80539 Uhrichsville OH\n", + "80540 Tucson AZ\n", + "80541 Orland park IL\n", + "80542 Loughman FL\n", + "\n", + "[80543 rows x 2 columns]" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select two columns.\n", + "ufo.loc[:, ['City', 'State']]" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedState
0IthacaNaNTRIANGLENY
1WillingboroNaNOTHERNJ
2HolyokeNaNOVALCO
3AbileneNaNDISKKS
4New York Worlds FairNaNLIGHTNY
5Valley CityNaNDISKND
6Crater LakeNaNCIRCLECA
7AlmaNaNDISKMI
8EklutnaNaNCIGARAK
9HubbardNaNCYLINDEROR
10FontanaNaNLIGHTCA
11WaterlooNaNFIREBALLAL
12BeltonREDSPHERESC
13KeokukNaNOVALIA
14LudingtonNaNDISKMI
15Forest HomeNaNCIRCLECA
16Los AngelesNaNNaNCA
17HapevilleNaNNaNGA
18OneidaNaNRECTANGLETN
19Bering SeaREDOTHERAK
20NebraskaNaNDISKNE
21NaNNaNNaNLA
22NaNNaNLIGHTLA
23OwensboroNaNRECTANGLEKY
24WildernessNaNDISKWV
25San DiegoNaNCIGARCA
26WildernessNaNDISKWV
27ClovisNaNDISKNM
28Los AlamosNaNDISKNM
29Ft. DuscheneNaNDISKUT
...............
80513ManahawkinNaNCIRCLENJ
80514New BedfordNaNLIGHTMA
80515WoodbridgeNaNTRIANGLEVA
80516GlendaleNaNCYLINDERCA
80517LaconiaNaNCIGARNH
80518LanghorneNaNOTHERPA
80519Glen EllynREDCIRCLEIL
80520WaynesburgNaNFLASHPA
80521Canal WinchesterNaNTRIANGLEOH
80522LawrenceORANGELIGHTMA
80523Ellicott CityNaNLIGHTMD
80524OlympiaREDLIGHTWA
80525Iowa CityBLUELIGHTIA
80526MarshfieldNaNLIGHTMA
80527SeattleNaNDIAMONDWA
80528North RoyaltonREDTRIANGLEOH
80529Deer ParkNaNLIGHTWA
80530Glen St. MaryNaNDISKFL
80531AbingdonNaNCIRCLEVA
80532LawrenceNaNLIGHTMA
80533MelbourneNaNOVALIA
80534BurlesonNaNLIGHTTX
80535ElizabethtownNaNCIRCLEKY
80536WyomingREDDISKPA
80537NelighNaNCIRCLENE
80538NelighNaNCIRCLENE
80539UhrichsvilleNaNLIGHTOH
80540TucsonRED BLUENaNAZ
80541Orland parkREDLIGHTIL
80542LoughmanNaNLIGHTFL
\n", + "

80543 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " City Colors Reported Shape Reported State\n", + "0 Ithaca NaN TRIANGLE NY\n", + "1 Willingboro NaN OTHER NJ\n", + "2 Holyoke NaN OVAL CO\n", + "3 Abilene NaN DISK KS\n", + "4 New York Worlds Fair NaN LIGHT NY\n", + "5 Valley City NaN DISK ND\n", + "6 Crater Lake NaN CIRCLE CA\n", + "7 Alma NaN DISK MI\n", + "8 Eklutna NaN CIGAR AK\n", + "9 Hubbard NaN CYLINDER OR\n", + "10 Fontana NaN LIGHT CA\n", + "11 Waterloo NaN FIREBALL AL\n", + "12 Belton RED SPHERE SC\n", + "13 Keokuk NaN OVAL IA\n", + "14 Ludington NaN DISK MI\n", + "15 Forest Home NaN CIRCLE CA\n", + "16 Los Angeles NaN NaN CA\n", + "17 Hapeville NaN NaN GA\n", + "18 Oneida NaN RECTANGLE TN\n", + "19 Bering Sea RED OTHER AK\n", + "20 Nebraska NaN DISK NE\n", + "21 NaN NaN NaN LA\n", + "22 NaN NaN LIGHT LA\n", + "23 Owensboro NaN RECTANGLE KY\n", + "24 Wilderness NaN DISK WV\n", + "25 San Diego NaN CIGAR CA\n", + "26 Wilderness NaN DISK WV\n", + "27 Clovis NaN DISK NM\n", + "28 Los Alamos NaN DISK NM\n", + "29 Ft. Duschene NaN DISK UT\n", + "... ... ... ... ...\n", + "80513 Manahawkin NaN CIRCLE NJ\n", + "80514 New Bedford NaN LIGHT MA\n", + "80515 Woodbridge NaN TRIANGLE VA\n", + "80516 Glendale NaN CYLINDER CA\n", + "80517 Laconia NaN CIGAR NH\n", + "80518 Langhorne NaN OTHER PA\n", + "80519 Glen Ellyn RED CIRCLE IL\n", + "80520 Waynesburg NaN FLASH PA\n", + "80521 Canal Winchester NaN TRIANGLE OH\n", + "80522 Lawrence ORANGE LIGHT MA\n", + "80523 Ellicott City NaN LIGHT MD\n", + "80524 Olympia RED LIGHT WA\n", + "80525 Iowa City BLUE LIGHT IA\n", + "80526 Marshfield NaN LIGHT MA\n", + "80527 Seattle NaN DIAMOND WA\n", + "80528 North Royalton RED TRIANGLE OH\n", + "80529 Deer Park NaN LIGHT WA\n", + "80530 Glen St. Mary NaN DISK FL\n", + "80531 Abingdon NaN CIRCLE VA\n", + "80532 Lawrence NaN LIGHT MA\n", + "80533 Melbourne NaN OVAL IA\n", + "80534 Burleson NaN LIGHT TX\n", + "80535 Elizabethtown NaN CIRCLE KY\n", + "80536 Wyoming RED DISK PA\n", + "80537 Neligh NaN CIRCLE NE\n", + "80538 Neligh NaN CIRCLE NE\n", + "80539 Uhrichsville NaN LIGHT OH\n", + "80540 Tucson RED BLUE NaN AZ\n", + "80541 Orland park RED LIGHT IL\n", + "80542 Loughman NaN LIGHT FL\n", + "\n", + "[80543 rows x 4 columns]" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select a range of columns — unlike Python ranges, Pandas index ranges INCLUDE the final column in the range.\n", + "ufo.loc[:, 'City':'State']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "City Ithaca\n", + "Colors Reported NaN\n", + "Shape Reported TRIANGLE\n", + "State NY\n", + "Time 6/1/1930 22:00\n", + "Name: 0, dtype: object" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# \"loc\" can also filter rows by \"name\" (the index).\n", + "# Row 0, all columns\n", + "ufo.loc[0, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State Time\n", + "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", + "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", + "2 Holyoke NaN OVAL CO 2/15/1931 14:00" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rows 0/1/2, all columns\n", + "ufo.loc[0:2, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedState
0IthacaNaNTRIANGLENY
1WillingboroNaNOTHERNJ
2HolyokeNaNOVALCO
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State\n", + "0 Ithaca NaN TRIANGLE NY\n", + "1 Willingboro NaN OTHER NJ\n", + "2 Holyoke NaN OVAL CO" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rows 0/1/2, range of columns\n", + "ufo.loc[0:2, 'City':'State'] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Joining (Merging) `DataFrames`\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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movie_idtitle
01Toy Story (1995)
12GoldenEye (1995)
23Four Rooms (1995)
34Get Shorty (1995)
45Copycat (1995)
\n", + "
" + ], + "text/plain": [ + " movie_id title\n", + "0 1 Toy Story (1995)\n", + "1 2 GoldenEye (1995)\n", + "2 3 Four Rooms (1995)\n", + "3 4 Get Shorty (1995)\n", + "4 5 Copycat (1995)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "movie_cols = ['movie_id', 'title']\n", + "u_item = './data/movies.tbl'\n", + "movies = pd.read_csv(u_item, sep='|', header=None, names=movie_cols, usecols=[0, 1], encoding='latin-1')\n", + "movies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idmovie_idratingtimestamp
01962423881250949
11863023891717742
2223771878887116
3244512880606923
41663461886397596
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" + ], + "text/plain": [ + " user_id movie_id rating timestamp\n", + "0 196 242 3 881250949\n", + "1 186 302 3 891717742\n", + "2 22 377 1 878887116\n", + "3 244 51 2 880606923\n", + "4 166 346 1 886397596" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rating_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings_filename = './data/movie_ratings.tsv'\n", + "\n", + "ratings = pd.read_csv(ratings_filename, sep='\\t', header=None, names=rating_cols)\n", + "ratings.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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movie_idtitleuser_idratingtimestamp
01Toy Story (1995)3084887736532
11Toy Story (1995)2875875334088
21Toy Story (1995)1484877019411
31Toy Story (1995)2804891700426
41Toy Story (1995)663883601324
\n", + "
" + ], + "text/plain": [ + " movie_id title user_id rating timestamp\n", + "0 1 Toy Story (1995) 308 4 887736532\n", + "1 1 Toy Story (1995) 287 5 875334088\n", + "2 1 Toy Story (1995) 148 4 877019411\n", + "3 1 Toy Story (1995) 280 4 891700426\n", + "4 1 Toy Story (1995) 66 3 883601324" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Merge \"movies\" and \"ratings\" (inner join on \"movie_id\").\n", + "movie_ratings = pd.merge(movies, ratings)\n", + "movie_ratings.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1682, 2)\n", + "(100000, 4)\n", + "(100000, 5)\n" + ] + } + ], + "source": [ + "print(movies.shape)\n", + "print(ratings.shape)\n", + "print(movie_ratings.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to export your file?" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "movie_ratings.to_csv('new_movies.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More Details about Merging \n", + "https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas CheatSheet\n", + "https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### OPTIONAL: Other Commonly Used Features" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply an arbitrary function to each value of a Pandas column, storing the result in a new column.\n", + "users['under30'] = users.age.apply(lambda age: age < 30)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply an arbitrary function to each row of a DataFrame, storing the result in a new column.\n", + "# (Remember that, by default, axis=0. Since we want to go row by row, we set axis=1.)\n", + "users['under30male'] = users.apply(lambda row: row.age < 30 and row.gender == 'M', axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "# Map existing values to a different set of values.\n", + "users['is_male'] = users.gender.map({'F':0, 'M':1})" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "# Replace all instances of a value in a column (must match entire value).\n", + "ufo.State.replace('Fl', 'FL', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 True\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 True\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + " ... \n", + "80513 False\n", + "80514 False\n", + "80515 False\n", + "80516 False\n", + "80517 False\n", + "80518 False\n", + "80519 True\n", + "80520 False\n", + "80521 False\n", + "80522 False\n", + "80523 False\n", + "80524 True\n", + "80525 False\n", + "80526 False\n", + "80527 False\n", + "80528 True\n", + "80529 False\n", + "80530 False\n", + "80531 False\n", + "80532 False\n", + "80533 False\n", + "80534 False\n", + "80535 False\n", + "80536 True\n", + "80537 False\n", + "80538 False\n", + "80539 False\n", + "80540 True\n", + "80541 True\n", + "80542 False\n", + "Name: Colors Reported, Length: 80543, dtype: object" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# String methods are accessed via \"str\".\n", + "ufo.State.str.upper() # Converts to upper case\n", + "# checks for a substring\n", + "ufo['Colors Reported'].str.contains('RED', na='False') " + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30776" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert a string to the datetime format (this is often slow — consider doing it in the \"read_csv()\" method.)\n", + "ufo['Time'] = pd.to_datetime(ufo.Time)\n", + "ufo.Time.dt.hour # Datetime format exposes convenient attributes\n", + "(ufo.Time.max() - ufo.Time.min()).days # Also allows you to do datetime \"math\"" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "# Set and then remove an index.\n", + "ufo.set_index('Time', inplace=True)\n", + "ufo.reset_index(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "# Change the datatype of a column.\n", + "drinks['beer'] = drinks.beer.astype('float')" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "# Create dummy variables for \"continent\" and exclude first dummy column.\n", + "continent_dummies = pd.get_dummies(drinks.continent, prefix='cont').iloc[:, 1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "# Concatenate two DataFrames (axis=0 for rows, axis=1 for columns).\n", + "drinks = pd.concat([drinks, continent_dummies], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### OPTIONAL: Other Less-Used Features of Pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Detecting duplicate rows\n", + "users.duplicated() # True if a row is identical to a previous row\n", + "users.duplicated().sum() # Count of duplicates\n", + "users[users.duplicated()] # Only show duplicates\n", + "users.drop_duplicates() # Drop duplicate rows\n", + "users.age.duplicated() # Check a single column for duplicates\n", + "users.duplicated(['age', 'gender', 'zip_code']).sum() # Specify columns for finding duplicates" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert a range of values into descriptive groups.\n", + "drinks['beer_level'] = 'low' # Initially set all values to \"low\"\n", + "drinks.loc[drinks.beer.between(101, 200), 'beer_level'] = 'med' # Change 101-200 to \"med\"\n", + "drinks.loc[drinks.beer.between(201, 400), 'beer_level'] = 'high' # Change 201-400 to \"high\"" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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102Malaysia13.0400.3AS10000low
103Maldives0.0000.0AS10000low
104Mali5.0110.6AF00000low
106Marshall Islands0.0000.0OC00010low
107Mauritania0.0000.0AF00000low
108Mauritius98.031182.6AF00000low
101Malawi8.01111.5AF00000low
110Micronesia62.050182.3OC00010low
112Mongolia77.018984.9AS10000low
113Montenegro31.01141284.9EU01000low
114Morocco12.06100.5AF00000low
115Mozambique47.01851.3AF00000low
116Myanmar5.0100.1AS10000low
118Nauru49.0081.0OC00010low
111Monaco0.0000.0EU01000low
100Madagascar26.01540.8AF00000low
97Libya0.0000.0AF00000low
191Zambia32.01942.5AF00000low
78Indonesia5.0100.1AS10000low
79Iran0.0000.0AS10000low
80Iraq9.0300.2AS10000low
82Israel63.06992.5AS10000low
83Italy85.0422376.5EU01000low
84Jamaica82.09793.4NA00100low
85Japan77.0202167.0AS10000low
86Jordan6.02110.5AS10000low
88Kenya58.02221.8AF00000low
89Kiribati21.03411.0OC00010low
90Kuwait0.0000.0AS10000low
.......................................
3Andorra245.013831212.4EU01000high
76Iceland233.061786.6EU01000high
188Venezuela333.010037.7SA00001high
99Luxembourg236.013327111.4EU01000high
75Hungary234.021518511.3EU01000high
109Mexico238.06855.5NA00100high
57Estonia224.0194599.5EU01000high
140Romania297.012216710.4EU01000high
93Latvia281.02166210.5EU01000high
141Russian Federation247.03267311.5AS10000high
135Poland343.02155610.9EU01000high
62Gabon347.098598.9AF00000high
132Paraguay213.0117747.3SA00001high
25Bulgaria231.02529410.3EU01000high
65Germany346.011717511.3EU01000high
151Serbia283.01311279.6EU01000high
130Panama285.0104187.2NA00100high
23Brazil245.0145167.2SA00001high
129Palau306.063236.9OC00010high
32Canada240.01221008.2NA00100high
156Slovenia270.05127610.6EU01000high
60Finland263.01339710.0EU01000high
159South Africa225.076818.2AF00000high
160Spain284.015711210.0EU01000high
81Ireland313.011816511.4EU01000high
17Belize263.011486.8NA00100high
121New Zealand203.0791759.3OC00010high
16Belgium295.08421210.5EU01000high
117Namibia376.0316.8AF00000high
120Netherlands251.0881909.4EU01000high
\n", + "

193 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " country beer spirit wine liters continent cont_AS \\\n", + "0 Afghanistan 0.0 0 0 0.0 AS 1 \n", + "102 Malaysia 13.0 4 0 0.3 AS 1 \n", + "103 Maldives 0.0 0 0 0.0 AS 1 \n", + "104 Mali 5.0 1 1 0.6 AF 0 \n", + "106 Marshall Islands 0.0 0 0 0.0 OC 0 \n", + "107 Mauritania 0.0 0 0 0.0 AF 0 \n", + "108 Mauritius 98.0 31 18 2.6 AF 0 \n", + "101 Malawi 8.0 11 1 1.5 AF 0 \n", + "110 Micronesia 62.0 50 18 2.3 OC 0 \n", + "112 Mongolia 77.0 189 8 4.9 AS 1 \n", + "113 Montenegro 31.0 114 128 4.9 EU 0 \n", + "114 Morocco 12.0 6 10 0.5 AF 0 \n", + "115 Mozambique 47.0 18 5 1.3 AF 0 \n", + "116 Myanmar 5.0 1 0 0.1 AS 1 \n", + "118 Nauru 49.0 0 8 1.0 OC 0 \n", + "111 Monaco 0.0 0 0 0.0 EU 0 \n", + "100 Madagascar 26.0 15 4 0.8 AF 0 \n", + "97 Libya 0.0 0 0 0.0 AF 0 \n", + "191 Zambia 32.0 19 4 2.5 AF 0 \n", + "78 Indonesia 5.0 1 0 0.1 AS 1 \n", + "79 Iran 0.0 0 0 0.0 AS 1 \n", + "80 Iraq 9.0 3 0 0.2 AS 1 \n", + "82 Israel 63.0 69 9 2.5 AS 1 \n", + "83 Italy 85.0 42 237 6.5 EU 0 \n", + "84 Jamaica 82.0 97 9 3.4 NA 0 \n", + "85 Japan 77.0 202 16 7.0 AS 1 \n", + "86 Jordan 6.0 21 1 0.5 AS 1 \n", + "88 Kenya 58.0 22 2 1.8 AF 0 \n", + "89 Kiribati 21.0 34 1 1.0 OC 0 \n", + "90 Kuwait 0.0 0 0 0.0 AS 1 \n", + ".. ... ... ... ... ... ... ... \n", + "3 Andorra 245.0 138 312 12.4 EU 0 \n", + "76 Iceland 233.0 61 78 6.6 EU 0 \n", + "188 Venezuela 333.0 100 3 7.7 SA 0 \n", + "99 Luxembourg 236.0 133 271 11.4 EU 0 \n", + "75 Hungary 234.0 215 185 11.3 EU 0 \n", + "109 Mexico 238.0 68 5 5.5 NA 0 \n", + "57 Estonia 224.0 194 59 9.5 EU 0 \n", + "140 Romania 297.0 122 167 10.4 EU 0 \n", + "93 Latvia 281.0 216 62 10.5 EU 0 \n", + "141 Russian Federation 247.0 326 73 11.5 AS 1 \n", + "135 Poland 343.0 215 56 10.9 EU 0 \n", + "62 Gabon 347.0 98 59 8.9 AF 0 \n", + "132 Paraguay 213.0 117 74 7.3 SA 0 \n", + "25 Bulgaria 231.0 252 94 10.3 EU 0 \n", + "65 Germany 346.0 117 175 11.3 EU 0 \n", + "151 Serbia 283.0 131 127 9.6 EU 0 \n", + "130 Panama 285.0 104 18 7.2 NA 0 \n", + "23 Brazil 245.0 145 16 7.2 SA 0 \n", + "129 Palau 306.0 63 23 6.9 OC 0 \n", + "32 Canada 240.0 122 100 8.2 NA 0 \n", + "156 Slovenia 270.0 51 276 10.6 EU 0 \n", + "60 Finland 263.0 133 97 10.0 EU 0 \n", + "159 South Africa 225.0 76 81 8.2 AF 0 \n", + "160 Spain 284.0 157 112 10.0 EU 0 \n", + "81 Ireland 313.0 118 165 11.4 EU 0 \n", + "17 Belize 263.0 114 8 6.8 NA 0 \n", + "121 New Zealand 203.0 79 175 9.3 OC 0 \n", + "16 Belgium 295.0 84 212 10.5 EU 0 \n", + "117 Namibia 376.0 3 1 6.8 AF 0 \n", + "120 Netherlands 251.0 88 190 9.4 EU 0 \n", + "\n", + " cont_EU cont_NA cont_OC cont_SA beer_level \n", + "0 0 0 0 0 low \n", + "102 0 0 0 0 low \n", + "103 0 0 0 0 low \n", + "104 0 0 0 0 low \n", + "106 0 0 1 0 low \n", + "107 0 0 0 0 low \n", + "108 0 0 0 0 low \n", + "101 0 0 0 0 low \n", + "110 0 0 1 0 low \n", + "112 0 0 0 0 low \n", + "113 1 0 0 0 low \n", + "114 0 0 0 0 low \n", + "115 0 0 0 0 low \n", + "116 0 0 0 0 low \n", + "118 0 0 1 0 low \n", + "111 1 0 0 0 low \n", + "100 0 0 0 0 low \n", + "97 0 0 0 0 low \n", + "191 0 0 0 0 low \n", + "78 0 0 0 0 low \n", + "79 0 0 0 0 low \n", + "80 0 0 0 0 low \n", + "82 0 0 0 0 low \n", + "83 1 0 0 0 low \n", + "84 0 1 0 0 low \n", + "85 0 0 0 0 low \n", + "86 0 0 0 0 low \n", + "88 0 0 0 0 low \n", + "89 0 0 1 0 low \n", + "90 0 0 0 0 low \n", + ".. ... ... ... ... ... \n", + "3 1 0 0 0 high \n", + "76 1 0 0 0 high \n", + "188 0 0 0 1 high \n", + "99 1 0 0 0 high \n", + "75 1 0 0 0 high \n", + "109 0 1 0 0 high \n", + "57 1 0 0 0 high \n", + "140 1 0 0 0 high \n", + "93 1 0 0 0 high \n", + "141 0 0 0 0 high \n", + "135 1 0 0 0 high \n", + "62 0 0 0 0 high \n", + "132 0 0 0 1 high \n", + "25 1 0 0 0 high \n", + "65 1 0 0 0 high \n", + "151 1 0 0 0 high \n", + "130 0 1 0 0 high \n", + "23 0 0 0 1 high \n", + "129 0 0 1 0 high \n", + "32 0 1 0 0 high \n", + "156 1 0 0 0 high \n", + "60 1 0 0 0 high \n", + "159 0 0 0 0 high \n", + "160 1 0 0 0 high \n", + "81 1 0 0 0 high \n", + "17 0 1 0 0 high \n", + "121 0 0 1 0 high \n", + "16 1 0 0 0 high \n", + "117 0 0 0 0 high \n", + "120 1 0 0 0 high \n", + "\n", + "[193 rows x 12 columns]" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert \"beer_level\" into the \"category\" datatype.\n", + "drinks['beer_level'] = pd.Categorical(drinks.beer_level, categories=['low', 'med', 'high'])\n", + "drinks.sort_values('beer_level') # Sorts by the categorical ordering (low to high)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Algeria250140.7AF
1Andorra24513831212.4EU
2Angola21757455.9AF
3Antigua & Barbuda102128454.9NaN
4Argentina193252218.3SA
5Armenia21179113.8EU
6Australia2617221210.4OC
7Austria279751919.7EU
8Azerbaijan214651.3EU
9Bahamas122176516.3NaN
10Bahrain426372.0AS
11Bangladesh0000.0AS
12Barbados143173366.3NaN
13Belarus1423734214.4EU
14Belgium2958421210.5EU
15Belize26311486.8NaN
16Benin344131.1AF
17Bhutan23000.4AS
18Bolivia1674183.8SA
19Bosnia-Herzegovina7617384.6EU
20Botswana17335355.4AF
21Brazil245145167.2SA
22Brunei31210.6AS
23Bulgaria2312529410.3EU
24Burkina Faso25774.3AF
25Burundi88006.3AF
26Cote d'Ivoire37174.0AF
27Cabo Verde14456164.0AF
28Cambodia576512.2AS
29Cameroon147145.8AF
.....................
161Suriname12817875.6SA
162Swaziland90224.7AF
163Sweden152601867.2EU
164Switzerland18510028010.2EU
165Syria535161.0AS
166Tajikistan21500.3AS
167Thailand9925816.4AS
168Macedonia10627863.9EU
169Timor-Leste1140.1AS
170Togo362191.3AF
171Tonga362151.1OC
172Trinidad & Tobago19715676.4NaN
173Tunisia513201.3AF
174Turkey512271.4AS
175Turkmenistan1971322.2AS
176Tuvalu64191.0OC
177Uganda45908.3AF
178Ukraine206237458.9EU
179United Arab Emirates1613552.8AS
180United Kingdom21912619510.4EU
181Tanzania36615.7AF
182USA249158848.7NaN
183Uruguay115352206.6SA
184Uzbekistan2510182.4AS
185Vanuatu2118110.9OC
186Venezuela33310037.7SA
187Vietnam111212.0AS
188Yemen6000.1AS
189Zambia321942.5AF
190Zimbabwe641844.7AF
\n", + "

191 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "0 Algeria 25 0 14 \n", + "1 Andorra 245 138 312 \n", + "2 Angola 217 57 45 \n", + "3 Antigua & Barbuda 102 128 45 \n", + "4 Argentina 193 25 221 \n", + "5 Armenia 21 179 11 \n", + "6 Australia 261 72 212 \n", + "7 Austria 279 75 191 \n", + "8 Azerbaijan 21 46 5 \n", + "9 Bahamas 122 176 51 \n", + "10 Bahrain 42 63 7 \n", + "11 Bangladesh 0 0 0 \n", + "12 Barbados 143 173 36 \n", + "13 Belarus 142 373 42 \n", + "14 Belgium 295 84 212 \n", + "15 Belize 263 114 8 \n", + "16 Benin 34 4 13 \n", + "17 Bhutan 23 0 0 \n", + "18 Bolivia 167 41 8 \n", + "19 Bosnia-Herzegovina 76 173 8 \n", + "20 Botswana 173 35 35 \n", + "21 Brazil 245 145 16 \n", + "22 Brunei 31 2 1 \n", + "23 Bulgaria 231 252 94 \n", + "24 Burkina Faso 25 7 7 \n", + "25 Burundi 88 0 0 \n", + "26 Cote d'Ivoire 37 1 7 \n", + "27 Cabo Verde 144 56 16 \n", + "28 Cambodia 57 65 1 \n", + "29 Cameroon 147 1 4 \n", + ".. ... ... ... ... \n", + "161 Suriname 128 178 7 \n", + "162 Swaziland 90 2 2 \n", + "163 Sweden 152 60 186 \n", + "164 Switzerland 185 100 280 \n", + "165 Syria 5 35 16 \n", + "166 Tajikistan 2 15 0 \n", + "167 Thailand 99 258 1 \n", + "168 Macedonia 106 27 86 \n", + "169 Timor-Leste 1 1 4 \n", + "170 Togo 36 2 19 \n", + "171 Tonga 36 21 5 \n", + "172 Trinidad & Tobago 197 156 7 \n", + "173 Tunisia 51 3 20 \n", + "174 Turkey 51 22 7 \n", + "175 Turkmenistan 19 71 32 \n", + "176 Tuvalu 6 41 9 \n", + "177 Uganda 45 9 0 \n", + "178 Ukraine 206 237 45 \n", + "179 United Arab Emirates 16 135 5 \n", + "180 United Kingdom 219 126 195 \n", + "181 Tanzania 36 6 1 \n", + "182 USA 249 158 84 \n", + "183 Uruguay 115 35 220 \n", + "184 Uzbekistan 25 101 8 \n", + "185 Vanuatu 21 18 11 \n", + "186 Venezuela 333 100 3 \n", + "187 Vietnam 111 2 1 \n", + "188 Yemen 6 0 0 \n", + "189 Zambia 32 19 4 \n", + "190 Zimbabwe 64 18 4 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.7 AF \n", + "1 12.4 EU \n", + "2 5.9 AF \n", + "3 4.9 NaN \n", + "4 8.3 SA \n", + "5 3.8 EU \n", + "6 10.4 OC \n", + "7 9.7 EU \n", + "8 1.3 EU \n", + "9 6.3 NaN \n", + "10 2.0 AS \n", + "11 0.0 AS \n", + "12 6.3 NaN \n", + "13 14.4 EU \n", + "14 10.5 EU \n", + "15 6.8 NaN \n", + "16 1.1 AF \n", + "17 0.4 AS \n", + "18 3.8 SA \n", + "19 4.6 EU \n", + "20 5.4 AF \n", + "21 7.2 SA \n", + "22 0.6 AS \n", + "23 10.3 EU \n", + "24 4.3 AF \n", + "25 6.3 AF \n", + "26 4.0 AF \n", + "27 4.0 AF \n", + "28 2.2 AS \n", + "29 5.8 AF \n", + ".. ... ... \n", + "161 5.6 SA \n", + "162 4.7 AF \n", + "163 7.2 EU \n", + "164 10.2 EU \n", + "165 1.0 AS \n", + "166 0.3 AS \n", + "167 6.4 AS \n", + "168 3.9 EU \n", + "169 0.1 AS \n", + "170 1.3 AF \n", + "171 1.1 OC \n", + "172 6.4 NaN \n", + "173 1.3 AF \n", + "174 1.4 AS \n", + "175 2.2 AS \n", + "176 1.0 OC \n", + "177 8.3 AF \n", + "178 8.9 EU \n", + "179 2.8 AS \n", + "180 10.4 EU \n", + "181 5.7 AF \n", + "182 8.7 NaN \n", + "183 6.6 SA \n", + "184 2.4 AS \n", + "185 0.9 OC \n", + "186 7.7 SA \n", + "187 2.0 AS \n", + "188 0.1 AS \n", + "189 2.5 AF \n", + "190 4.7 AF \n", + "\n", + "[191 rows x 6 columns]" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Limit which rows are read when reading in a file — useful for large files!\n", + "pd.read_csv('./data/drinks.csv', nrows=10) # Only read first 10 rows\n", + "pd.read_csv('./data/drinks.csv', skiprows=[1, 2]) # Skip the first two rows of data" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "# Write a DataFrame out to a .csv\n", + "drinks.to_csv('drinks_updated.csv') # Index is used as first column\n", + "drinks.to_csv('drinks_updated.csv', index=False) # Ignore index" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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capitalstate
0MontgomeryAL
1JuneauAK
2PhoenixAZ
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" + ], + "text/plain": [ + " capital state\n", + "0 Montgomery AL\n", + "1 Juneau AK\n", + "2 Phoenix AZ" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a DataFrame from a dictionary.\n", + "pd.DataFrame({'capital':['Montgomery', 'Juneau', 'Phoenix'], 'state':['AL', 'AK', 'AZ']})" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2PhoenixAZ
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" + ], + "text/plain": [ + " capital state\n", + "0 Montgomery AL\n", + "1 Juneau AK\n", + "2 Phoenix AZ" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a DataFrame from a list of lists.\n", + "pd.DataFrame([['Montgomery', 'AL'], ['Juneau', 'AK'], ['Phoenix', 'AZ']], columns=['capital', 'state'])" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly sample a DataFrame.\n", + "import numpy as np\n", + "mask = np.random.rand(len(drinks)) < 0.66 # Create a Series of Booleans\n", + "train = drinks[mask] # Will contain around 66% of the rows\n", + "test = drinks[~mask] # Will contain the remaining rows" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " country beer spirit wine liters continent \\\n", + "0 Afghanistan 0.0 0 0 0.0 AS \n", + "1 Albania 89.0 132 54 4.9 EU \n", + "2 Algeria 25.0 0 14 0.7 AF \n", + "3 Andorra 245.0 138 312 12.4 EU \n", + "4 Angola 217.0 57 45 5.9 AF \n", + "5 Antigua & Barbuda 102.0 128 45 4.9 NA \n", + "6 Argentina 193.0 25 221 8.3 SA \n", + "7 Armenia 21.0 179 11 3.8 EU \n", + "8 Australia 261.0 72 212 10.4 OC \n", + "9 Austria 279.0 75 191 9.7 EU \n", + "10 Azerbaijan 21.0 46 5 1.3 EU \n", + "11 Bahamas 122.0 176 51 6.3 NA \n", + "12 Bahrain 42.0 63 7 2.0 AS \n", + "13 Bangladesh 0.0 0 0 0.0 AS \n", + "14 Barbados 143.0 173 36 6.3 NA \n", + "15 Belarus 142.0 373 42 14.4 EU \n", + "16 Belgium 295.0 84 212 10.5 EU \n", + "17 Belize 263.0 114 8 6.8 NA \n", + "18 Benin 34.0 4 13 1.1 AF \n", + "19 Bhutan 23.0 0 0 0.4 AS \n", + "20 Bolivia 167.0 41 8 3.8 SA \n", + "21 Bosnia-Herzegovina 76.0 173 8 4.6 EU \n", + "22 Botswana 173.0 35 35 5.4 AF \n", + "23 Brazil 245.0 145 16 7.2 SA \n", + "24 Brunei 31.0 2 1 0.6 AS \n", + "25 Bulgaria 231.0 252 94 10.3 EU \n", + "26 Burkina Faso 25.0 7 7 4.3 AF \n", + "27 Burundi 88.0 0 0 6.3 AF \n", + "28 Cote d'Ivoire 37.0 1 7 4.0 AF \n", + "29 Cabo Verde 144.0 56 16 4.0 AF \n", + "30 Cambodia 57.0 65 1 2.2 AS \n", + "31 Cameroon 147.0 1 4 5.8 AF \n", + "32 Canada 240.0 122 100 8.2 NA \n", + "33 Central African Republic 17.0 2 1 1.8 AF \n", + "34 Chad 15.0 1 1 0.4 AF \n", + "35 Chile 130.0 124 172 7.6 SA \n", + "36 China 79.0 192 8 5.0 AS \n", + "37 Colombia 159.0 76 3 4.2 SA \n", + "38 Comoros 1.0 3 1 0.1 AF \n", + "39 Congo 76.0 1 9 1.7 AF \n", + "40 Cook Islands 0.0 254 74 5.9 OC \n", + "41 Costa Rica 149.0 87 11 4.4 NA \n", + "42 Croatia 230.0 87 254 10.2 EU \n", + "43 Cuba 93.0 137 5 4.2 NA \n", + "44 Cyprus 192.0 154 113 8.2 EU \n", + "45 Czech Republic 361.0 170 134 11.8 EU \n", + "46 North Korea 0.0 0 0 0.0 AS \n", + "47 DR Congo 32.0 3 1 2.3 AF \n", + "48 Denmark 224.0 81 278 10.4 EU \n", + "49 Djibouti 15.0 44 3 1.1 AF \n", + "50 Dominica 52.0 286 26 6.6 NA \n", + "51 Dominican Republic 193.0 147 9 6.2 NA \n", + "52 Ecuador 162.0 74 3 4.2 SA \n", + "53 Egypt 6.0 4 1 0.2 AF \n", + "54 El Salvador 52.0 69 2 2.2 NA \n", + "55 Equatorial Guinea 92.0 0 233 5.8 AF \n", + "56 Eritrea 18.0 0 0 0.5 AF \n", + "57 Estonia 224.0 194 59 9.5 EU \n", + "58 Ethiopia 20.0 3 0 0.7 AF \n", + "59 Fiji 77.0 35 1 2.0 OC \n", + "60 Finland 263.0 133 97 10.0 EU \n", + "61 France 127.0 151 370 11.8 EU \n", + "62 Gabon 347.0 98 59 8.9 AF \n", + "63 Gambia 8.0 0 1 2.4 AF \n", + "64 Georgia 52.0 100 149 5.4 EU \n", + "65 Germany 346.0 117 175 11.3 EU \n", + "66 Ghana 31.0 3 10 1.8 AF \n", + "67 Greece 133.0 112 218 8.3 EU \n", + "68 Grenada 199.0 438 28 11.9 NA \n", + "69 Guatemala 53.0 69 2 2.2 NA \n", + "70 Guinea 9.0 0 2 0.2 AF \n", + "71 Guinea-Bissau 28.0 31 21 2.5 AF \n", + "72 Guyana 93.0 302 1 7.1 SA \n", + "73 Haiti 1.0 326 1 5.9 NA \n", + "74 Honduras 69.0 98 2 3.0 NA \n", + "75 Hungary 234.0 215 185 11.3 EU \n", + "76 Iceland 233.0 61 78 6.6 EU \n", + "77 India 9.0 114 0 2.2 AS \n", + "78 Indonesia 5.0 1 0 0.1 AS \n", + "79 Iran 0.0 0 0 0.0 AS \n", + "80 Iraq 9.0 3 0 0.2 AS \n", + "81 Ireland 313.0 118 165 11.4 EU \n", + "82 Israel 63.0 69 9 2.5 AS \n", + "83 Italy 85.0 42 237 6.5 EU \n", + "84 Jamaica 82.0 97 9 3.4 NA \n", + "85 Japan 77.0 202 16 7.0 AS \n", + "86 Jordan 6.0 21 1 0.5 AS \n", + "87 Kazakhstan 124.0 246 12 6.8 AS \n", + "88 Kenya 58.0 22 2 1.8 AF \n", + "89 Kiribati 21.0 34 1 1.0 OC \n", + "90 Kuwait 0.0 0 0 0.0 AS \n", + "91 Kyrgyzstan 31.0 97 6 2.4 AS \n", + "92 Laos 62.0 0 123 6.2 AS \n", + "93 Latvia 281.0 216 62 10.5 EU \n", + "94 Lebanon 20.0 55 31 1.9 AS \n", + "95 Lesotho 82.0 29 0 2.8 AF \n", + "96 Liberia 19.0 152 2 3.1 AF \n", + "97 Libya 0.0 0 0 0.0 AF \n", + "98 Lithuania 343.0 244 56 12.9 EU \n", + "99 Luxembourg 236.0 133 271 11.4 EU \n", + "100 Madagascar 26.0 15 4 0.8 AF \n", + "101 Malawi 8.0 11 1 1.5 AF \n", + "102 Malaysia 13.0 4 0 0.3 AS \n", + "103 Maldives 0.0 0 0 0.0 AS \n", + "104 Mali 5.0 1 1 0.6 AF \n", + "105 Malta 149.0 100 120 6.6 EU \n", + "106 Marshall Islands 0.0 0 0 0.0 OC \n", + "107 Mauritania 0.0 0 0 0.0 AF \n", + "108 Mauritius 98.0 31 18 2.6 AF \n", + "109 Mexico 238.0 68 5 5.5 NA \n", + "110 Micronesia 62.0 50 18 2.3 OC \n", + "111 Monaco 0.0 0 0 0.0 EU \n", + "112 Mongolia 77.0 189 8 4.9 AS \n", + "113 Montenegro 31.0 114 128 4.9 EU \n", + "114 Morocco 12.0 6 10 0.5 AF \n", + "115 Mozambique 47.0 18 5 1.3 AF \n", + "116 Myanmar 5.0 1 0 0.1 AS \n", + "117 Namibia 376.0 3 1 6.8 AF \n", + "118 Nauru 49.0 0 8 1.0 OC \n", + "119 Nepal 5.0 6 0 0.2 AS \n", + "120 Netherlands 251.0 88 190 9.4 EU \n", + "121 New Zealand 203.0 79 175 9.3 OC \n", + "122 Nicaragua 78.0 118 1 3.5 NA \n", + "123 Niger 3.0 2 1 0.1 AF \n", + "124 Nigeria 42.0 5 2 9.1 AF \n", + "125 Niue 188.0 200 7 7.0 OC \n", + "126 Norway 169.0 71 129 6.7 EU \n", + "127 Oman 22.0 16 1 0.7 AS \n", + "128 Pakistan 0.0 0 0 0.0 AS \n", + "129 Palau 306.0 63 23 6.9 OC \n", + "130 Panama 285.0 104 18 7.2 NA \n", + "131 Papua New Guinea 44.0 39 1 1.5 OC \n", + "132 Paraguay 213.0 117 74 7.3 SA \n", + "133 Peru 163.0 160 21 6.1 SA \n", + "134 Philippines 71.0 186 1 4.6 AS \n", + "135 Poland 343.0 215 56 10.9 EU \n", + "136 Portugal 194.0 67 339 11.0 EU \n", + "137 Qatar 1.0 42 7 0.9 AS \n", + "138 South Korea 140.0 16 9 9.8 AS \n", + "139 Moldova 109.0 226 18 6.3 EU \n", + "140 Romania 297.0 122 167 10.4 EU \n", + "141 Russian Federation 247.0 326 73 11.5 AS \n", + "142 Rwanda 43.0 2 0 6.8 AF \n", + "143 St. Kitts & Nevis 194.0 205 32 7.7 NA \n", + "144 St. Lucia 171.0 315 71 10.1 NA \n", + "145 St. Vincent & the Grenadines 120.0 221 11 6.3 NA \n", + "146 Samoa 105.0 18 24 2.6 OC \n", + "147 San Marino 0.0 0 0 0.0 EU \n", + "148 Sao Tome & Principe 56.0 38 140 4.2 AF \n", + "149 Saudi Arabia 0.0 5 0 0.1 AS \n", + "150 Senegal 9.0 1 7 0.3 AF \n", + "151 Serbia 283.0 131 127 9.6 EU \n", + "152 Seychelles 157.0 25 51 4.1 AF \n", + "153 Sierra Leone 25.0 3 2 6.7 AF \n", + "154 Singapore 60.0 12 11 1.5 AS \n", + "155 Slovakia 196.0 293 116 11.4 EU \n", + "156 Slovenia 270.0 51 276 10.6 EU \n", + "157 Solomon Islands 56.0 11 1 1.2 OC \n", + "158 Somalia 0.0 0 0 0.0 AF \n", + "159 South Africa 225.0 76 81 8.2 AF \n", + "160 Spain 284.0 157 112 10.0 EU \n", + "161 Sri Lanka 16.0 104 0 2.2 AS \n", + "162 Sudan 8.0 13 0 1.7 AF \n", + "163 Suriname 128.0 178 7 5.6 SA \n", + "164 Swaziland 90.0 2 2 4.7 AF \n", + "165 Sweden 152.0 60 186 7.2 EU \n", + "166 Switzerland 185.0 100 280 10.2 EU \n", + "167 Syria 5.0 35 16 1.0 AS \n", + "168 Tajikistan 2.0 15 0 0.3 AS \n", + "169 Thailand 99.0 258 1 6.4 AS \n", + "170 Macedonia 106.0 27 86 3.9 EU \n", + "171 Timor-Leste 1.0 1 4 0.1 AS \n", + "172 Togo 36.0 2 19 1.3 AF \n", + "173 Tonga 36.0 21 5 1.1 OC \n", + "174 Trinidad & Tobago 197.0 156 7 6.4 NA \n", + "175 Tunisia 51.0 3 20 1.3 AF \n", + "176 Turkey 51.0 22 7 1.4 AS \n", + "177 Turkmenistan 19.0 71 32 2.2 AS \n", + "178 Tuvalu 6.0 41 9 1.0 OC \n", + "179 Uganda 45.0 9 0 8.3 AF \n", + "180 Ukraine 206.0 237 45 8.9 EU \n", + "181 United Arab Emirates 16.0 135 5 2.8 AS \n", + "182 United Kingdom 219.0 126 195 10.4 EU \n", + "183 Tanzania 36.0 6 1 5.7 AF \n", + "184 USA 249.0 158 84 8.7 NA \n", + "185 Uruguay 115.0 35 220 6.6 SA \n", + "186 Uzbekistan 25.0 101 8 2.4 AS \n", + "187 Vanuatu 21.0 18 11 0.9 OC \n", + "188 Venezuela 333.0 100 3 7.7 SA \n", + "189 Vietnam 111.0 2 1 2.0 AS \n", + "190 Yemen 6.0 0 0 0.1 AS \n", + "191 Zambia 32.0 19 4 2.5 AF \n", + "192 Zimbabwe 64.0 18 4 4.7 AF \n", + "\n", + " cont_AS cont_EU cont_NA cont_OC cont_SA beer_level \n", + "0 1 0 0 0 0 low \n", + "1 0 1 0 0 0 low \n", + "2 0 0 0 0 0 low \n", + "3 0 1 0 0 0 high \n", + "4 0 0 0 0 0 high \n", + "5 0 0 1 0 0 med \n", + "6 0 0 0 0 1 med \n", + "7 0 1 0 0 0 low \n", + "8 0 0 0 1 0 high \n", + "9 0 1 0 0 0 high \n", + "10 0 1 0 0 0 low \n", + "11 0 0 1 0 0 med \n", + "12 1 0 0 0 0 low \n", + "13 1 0 0 0 0 low \n", + "14 0 0 1 0 0 med \n", + "15 0 1 0 0 0 med \n", + "16 0 1 0 0 0 high \n", + "17 0 0 1 0 0 high \n", + "18 0 0 0 0 0 low \n", + "19 1 0 0 0 0 low \n", + "20 0 0 0 0 1 med \n", + "21 0 1 0 0 0 low \n", + "22 0 0 0 0 0 med \n", + "23 0 0 0 0 1 high \n", + "24 1 0 0 0 0 low \n", + "25 0 1 0 0 0 high \n", + "26 0 0 0 0 0 low \n", + "27 0 0 0 0 0 low \n", + "28 0 0 0 0 0 low \n", + "29 0 0 0 0 0 med \n", + "30 1 0 0 0 0 low \n", + "31 0 0 0 0 0 med \n", + "32 0 0 1 0 0 high \n", + "33 0 0 0 0 0 low \n", + "34 0 0 0 0 0 low \n", + "35 0 0 0 0 1 med \n", + "36 1 0 0 0 0 low \n", + "37 0 0 0 0 1 med \n", + "38 0 0 0 0 0 low \n", + "39 0 0 0 0 0 low \n", + "40 0 0 0 1 0 low \n", + "41 0 0 1 0 0 med \n", + "42 0 1 0 0 0 high \n", + "43 0 0 1 0 0 low \n", + "44 0 1 0 0 0 med \n", + "45 0 1 0 0 0 high \n", + "46 1 0 0 0 0 low \n", + "47 0 0 0 0 0 low \n", + "48 0 1 0 0 0 high \n", + "49 0 0 0 0 0 low \n", + "50 0 0 1 0 0 low \n", + "51 0 0 1 0 0 med \n", + "52 0 0 0 0 1 med \n", + "53 0 0 0 0 0 low \n", + "54 0 0 1 0 0 low \n", + "55 0 0 0 0 0 low \n", + "56 0 0 0 0 0 low \n", + "57 0 1 0 0 0 high \n", + "58 0 0 0 0 0 low \n", + "59 0 0 0 1 0 low \n", + "60 0 1 0 0 0 high \n", + "61 0 1 0 0 0 med \n", + "62 0 0 0 0 0 high \n", + "63 0 0 0 0 0 low \n", + "64 0 1 0 0 0 low \n", + "65 0 1 0 0 0 high \n", + "66 0 0 0 0 0 low \n", + "67 0 1 0 0 0 med \n", + "68 0 0 1 0 0 med \n", + "69 0 0 1 0 0 low \n", + "70 0 0 0 0 0 low \n", + "71 0 0 0 0 0 low \n", + "72 0 0 0 0 1 low \n", + "73 0 0 1 0 0 low \n", + "74 0 0 1 0 0 low \n", + "75 0 1 0 0 0 high \n", + "76 0 1 0 0 0 high \n", + "77 1 0 0 0 0 low \n", + "78 1 0 0 0 0 low \n", + "79 1 0 0 0 0 low \n", + "80 1 0 0 0 0 low \n", + "81 0 1 0 0 0 high \n", + "82 1 0 0 0 0 low \n", + "83 0 1 0 0 0 low \n", + "84 0 0 1 0 0 low \n", + "85 1 0 0 0 0 low \n", + "86 1 0 0 0 0 low \n", + "87 1 0 0 0 0 med \n", + "88 0 0 0 0 0 low \n", + "89 0 0 0 1 0 low \n", + "90 1 0 0 0 0 low \n", + "91 1 0 0 0 0 low \n", + "92 1 0 0 0 0 low \n", + "93 0 1 0 0 0 high \n", + "94 1 0 0 0 0 low \n", + "95 0 0 0 0 0 low \n", + "96 0 0 0 0 0 low \n", + "97 0 0 0 0 0 low \n", + "98 0 1 0 0 0 high \n", + "99 0 1 0 0 0 high \n", + "100 0 0 0 0 0 low \n", + "101 0 0 0 0 0 low \n", + "102 1 0 0 0 0 low \n", + "103 1 0 0 0 0 low \n", + "104 0 0 0 0 0 low \n", + "105 0 1 0 0 0 med \n", + "106 0 0 0 1 0 low \n", + "107 0 0 0 0 0 low \n", + "108 0 0 0 0 0 low \n", + "109 0 0 1 0 0 high \n", + "110 0 0 0 1 0 low \n", + "111 0 1 0 0 0 low \n", + "112 1 0 0 0 0 low \n", + "113 0 1 0 0 0 low \n", + "114 0 0 0 0 0 low \n", + "115 0 0 0 0 0 low \n", + "116 1 0 0 0 0 low \n", + "117 0 0 0 0 0 high \n", + "118 0 0 0 1 0 low \n", + "119 1 0 0 0 0 low \n", + "120 0 1 0 0 0 high \n", + "121 0 0 0 1 0 high \n", + "122 0 0 1 0 0 low \n", + "123 0 0 0 0 0 low \n", + "124 0 0 0 0 0 low \n", + "125 0 0 0 1 0 med \n", + "126 0 1 0 0 0 med \n", + "127 1 0 0 0 0 low \n", + "128 1 0 0 0 0 low \n", + "129 0 0 0 1 0 high \n", + "130 0 0 1 0 0 high \n", + "131 0 0 0 1 0 low \n", + "132 0 0 0 0 1 high \n", + "133 0 0 0 0 1 med \n", + "134 1 0 0 0 0 low \n", + "135 0 1 0 0 0 high \n", + "136 0 1 0 0 0 med \n", + "137 1 0 0 0 0 low \n", + "138 1 0 0 0 0 med \n", + "139 0 1 0 0 0 med \n", + "140 0 1 0 0 0 high \n", + "141 1 0 0 0 0 high \n", + "142 0 0 0 0 0 low \n", + "143 0 0 1 0 0 med \n", + "144 0 0 1 0 0 med \n", + "145 0 0 1 0 0 med \n", + "146 0 0 0 1 0 med \n", + "147 0 1 0 0 0 low \n", + "148 0 0 0 0 0 low \n", + "149 1 0 0 0 0 low \n", + "150 0 0 0 0 0 low \n", + "151 0 1 0 0 0 high \n", + "152 0 0 0 0 0 med \n", + "153 0 0 0 0 0 low \n", + "154 1 0 0 0 0 low \n", + "155 0 1 0 0 0 med \n", + "156 0 1 0 0 0 high \n", + "157 0 0 0 1 0 low \n", + "158 0 0 0 0 0 low \n", + "159 0 0 0 0 0 high \n", + "160 0 1 0 0 0 high \n", + "161 1 0 0 0 0 low \n", + "162 0 0 0 0 0 low \n", + "163 0 0 0 0 1 med \n", + "164 0 0 0 0 0 low \n", + "165 0 1 0 0 0 med \n", + "166 0 1 0 0 0 med \n", + "167 1 0 0 0 0 low \n", + "168 1 0 0 0 0 low \n", + "169 1 0 0 0 0 low \n", + "170 0 1 0 0 0 med \n", + "171 1 0 0 0 0 low \n", + "172 0 0 0 0 0 low \n", + "173 0 0 0 1 0 low \n", + "174 0 0 1 0 0 med \n", + "175 0 0 0 0 0 low \n", + "176 1 0 0 0 0 low \n", + "177 1 0 0 0 0 low \n", + "178 0 0 0 1 0 low \n", + "179 0 0 0 0 0 low \n", + "180 0 1 0 0 0 high \n", + "181 1 0 0 0 0 low \n", + "182 0 1 0 0 0 high \n", + "183 0 0 0 0 0 low \n", + "184 0 0 1 0 0 high \n", + "185 0 0 0 0 1 med \n", + "186 1 0 0 0 0 low \n", + "187 0 0 0 1 0 low \n", + "188 0 0 0 0 1 high \n", + "189 1 0 0 0 0 med \n", + "190 1 0 0 0 0 low \n", + "191 0 0 0 0 0 low \n", + "192 0 0 0 0 0 low \n" + ] + } + ], + "source": [ + "# Change the maximum number of rows and columns printed ('None' means unlimited).\n", + "pd.set_option('max_rows', None) # Default is 60 rows\n", + "pd.set_option('max_columns', None) # Default is 20 columns\n", + "print(drinks)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [], + "source": [ + "# Reset options to defaults.\n", + "pd.reset_option('max_rows')\n", + "pd.reset_option('max_columns')" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " country beer spirit wine liters continent \\\n", + "0 Afghanistan 0.0 0 0 0.0 AS \n", + "1 Albania 89.0 132 54 4.9 EU \n", + "2 Algeria 25.0 0 14 0.7 AF \n", + "3 Andorra 245.0 138 312 12.4 EU \n", + "4 Angola 217.0 57 45 5.9 AF \n", + "5 Antigua & Barbuda 102.0 128 45 4.9 NA \n", + "6 Argentina 193.0 25 221 8.3 SA \n", + "7 Armenia 21.0 179 11 3.8 EU \n", + "8 Australia 261.0 72 212 10.4 OC \n", + "9 Austria 279.0 75 191 9.7 EU \n", + "10 Azerbaijan 21.0 46 5 1.3 EU \n", + "11 Bahamas 122.0 176 51 6.3 NA \n", + "12 Bahrain 42.0 63 7 2.0 AS \n", + "13 Bangladesh 0.0 0 0 0.0 AS \n", + "14 Barbados 143.0 173 36 6.3 NA \n", + "15 Belarus 142.0 373 42 14.4 EU \n", + "16 Belgium 295.0 84 212 10.5 EU \n", + "17 Belize 263.0 114 8 6.8 NA \n", + "18 Benin 34.0 4 13 1.1 AF \n", + "19 Bhutan 23.0 0 0 0.4 AS \n", + "20 Bolivia 167.0 41 8 3.8 SA \n", + "21 Bosnia-Herzegovina 76.0 173 8 4.6 EU \n", + "22 Botswana 173.0 35 35 5.4 AF \n", + "23 Brazil 245.0 145 16 7.2 SA \n", + "24 Brunei 31.0 2 1 0.6 AS \n", + "25 Bulgaria 231.0 252 94 10.3 EU \n", + "26 Burkina Faso 25.0 7 7 4.3 AF \n", + "27 Burundi 88.0 0 0 6.3 AF \n", + "28 Cote d'Ivoire 37.0 1 7 4.0 AF \n", + "29 Cabo Verde 144.0 56 16 4.0 AF \n", + "30 Cambodia 57.0 65 1 2.2 AS \n", + "31 Cameroon 147.0 1 4 5.8 AF \n", + "32 Canada 240.0 122 100 8.2 NA \n", + "33 Central African Republic 17.0 2 1 1.8 AF \n", + "34 Chad 15.0 1 1 0.4 AF \n", + "35 Chile 130.0 124 172 7.6 SA \n", + "36 China 79.0 192 8 5.0 AS \n", + "37 Colombia 159.0 76 3 4.2 SA \n", + "38 Comoros 1.0 3 1 0.1 AF \n", + "39 Congo 76.0 1 9 1.7 AF \n", + "40 Cook Islands 0.0 254 74 5.9 OC \n", + "41 Costa Rica 149.0 87 11 4.4 NA \n", + "42 Croatia 230.0 87 254 10.2 EU \n", + "43 Cuba 93.0 137 5 4.2 NA \n", + "44 Cyprus 192.0 154 113 8.2 EU \n", + "45 Czech Republic 361.0 170 134 11.8 EU \n", + "46 North Korea 0.0 0 0 0.0 AS \n", + "47 DR Congo 32.0 3 1 2.3 AF \n", + "48 Denmark 224.0 81 278 10.4 EU \n", + "49 Djibouti 15.0 44 3 1.1 AF \n", + "50 Dominica 52.0 286 26 6.6 NA \n", + "51 Dominican Republic 193.0 147 9 6.2 NA \n", + "52 Ecuador 162.0 74 3 4.2 SA \n", + "53 Egypt 6.0 4 1 0.2 AF \n", + "54 El Salvador 52.0 69 2 2.2 NA \n", + "55 Equatorial Guinea 92.0 0 233 5.8 AF \n", + "56 Eritrea 18.0 0 0 0.5 AF \n", + "57 Estonia 224.0 194 59 9.5 EU \n", + "58 Ethiopia 20.0 3 0 0.7 AF \n", + "59 Fiji 77.0 35 1 2.0 OC \n", + "60 Finland 263.0 133 97 10.0 EU \n", + "61 France 127.0 151 370 11.8 EU \n", + "62 Gabon 347.0 98 59 8.9 AF \n", + "63 Gambia 8.0 0 1 2.4 AF \n", + "64 Georgia 52.0 100 149 5.4 EU \n", + "65 Germany 346.0 117 175 11.3 EU \n", + "66 Ghana 31.0 3 10 1.8 AF \n", + "67 Greece 133.0 112 218 8.3 EU \n", + "68 Grenada 199.0 438 28 11.9 NA \n", + "69 Guatemala 53.0 69 2 2.2 NA \n", + "70 Guinea 9.0 0 2 0.2 AF \n", + "71 Guinea-Bissau 28.0 31 21 2.5 AF \n", + "72 Guyana 93.0 302 1 7.1 SA \n", + "73 Haiti 1.0 326 1 5.9 NA \n", + "74 Honduras 69.0 98 2 3.0 NA \n", + "75 Hungary 234.0 215 185 11.3 EU \n", + "76 Iceland 233.0 61 78 6.6 EU \n", + "77 India 9.0 114 0 2.2 AS \n", + "78 Indonesia 5.0 1 0 0.1 AS \n", + "79 Iran 0.0 0 0 0.0 AS \n", + "80 Iraq 9.0 3 0 0.2 AS \n", + "81 Ireland 313.0 118 165 11.4 EU \n", + "82 Israel 63.0 69 9 2.5 AS \n", + "83 Italy 85.0 42 237 6.5 EU \n", + "84 Jamaica 82.0 97 9 3.4 NA \n", + "85 Japan 77.0 202 16 7.0 AS \n", + "86 Jordan 6.0 21 1 0.5 AS \n", + "87 Kazakhstan 124.0 246 12 6.8 AS \n", + "88 Kenya 58.0 22 2 1.8 AF \n", + "89 Kiribati 21.0 34 1 1.0 OC \n", + "90 Kuwait 0.0 0 0 0.0 AS \n", + "91 Kyrgyzstan 31.0 97 6 2.4 AS \n", + "92 Laos 62.0 0 123 6.2 AS \n", + "93 Latvia 281.0 216 62 10.5 EU \n", + "94 Lebanon 20.0 55 31 1.9 AS \n", + "95 Lesotho 82.0 29 0 2.8 AF \n", + "96 Liberia 19.0 152 2 3.1 AF \n", + "97 Libya 0.0 0 0 0.0 AF \n", + "98 Lithuania 343.0 244 56 12.9 EU \n", + "99 Luxembourg 236.0 133 271 11.4 EU \n", + "100 Madagascar 26.0 15 4 0.8 AF \n", + "101 Malawi 8.0 11 1 1.5 AF \n", + "102 Malaysia 13.0 4 0 0.3 AS \n", + "103 Maldives 0.0 0 0 0.0 AS \n", + "104 Mali 5.0 1 1 0.6 AF \n", + "105 Malta 149.0 100 120 6.6 EU \n", + "106 Marshall Islands 0.0 0 0 0.0 OC \n", + "107 Mauritania 0.0 0 0 0.0 AF \n", + "108 Mauritius 98.0 31 18 2.6 AF \n", + "109 Mexico 238.0 68 5 5.5 NA \n", + "110 Micronesia 62.0 50 18 2.3 OC \n", + "111 Monaco 0.0 0 0 0.0 EU \n", + "112 Mongolia 77.0 189 8 4.9 AS \n", + "113 Montenegro 31.0 114 128 4.9 EU \n", + "114 Morocco 12.0 6 10 0.5 AF \n", + "115 Mozambique 47.0 18 5 1.3 AF \n", + "116 Myanmar 5.0 1 0 0.1 AS \n", + "117 Namibia 376.0 3 1 6.8 AF \n", + "118 Nauru 49.0 0 8 1.0 OC \n", + "119 Nepal 5.0 6 0 0.2 AS \n", + "120 Netherlands 251.0 88 190 9.4 EU \n", + "121 New Zealand 203.0 79 175 9.3 OC \n", + "122 Nicaragua 78.0 118 1 3.5 NA \n", + "123 Niger 3.0 2 1 0.1 AF \n", + "124 Nigeria 42.0 5 2 9.1 AF \n", + "125 Niue 188.0 200 7 7.0 OC \n", + "126 Norway 169.0 71 129 6.7 EU \n", + "127 Oman 22.0 16 1 0.7 AS \n", + "128 Pakistan 0.0 0 0 0.0 AS \n", + "129 Palau 306.0 63 23 6.9 OC \n", + "130 Panama 285.0 104 18 7.2 NA \n", + "131 Papua New Guinea 44.0 39 1 1.5 OC \n", + "132 Paraguay 213.0 117 74 7.3 SA \n", + "133 Peru 163.0 160 21 6.1 SA \n", + "134 Philippines 71.0 186 1 4.6 AS \n", + "135 Poland 343.0 215 56 10.9 EU \n", + "136 Portugal 194.0 67 339 11.0 EU \n", + "137 Qatar 1.0 42 7 0.9 AS \n", + "138 South Korea 140.0 16 9 9.8 AS \n", + "139 Moldova 109.0 226 18 6.3 EU \n", + "140 Romania 297.0 122 167 10.4 EU \n", + "141 Russian Federation 247.0 326 73 11.5 AS \n", + "142 Rwanda 43.0 2 0 6.8 AF \n", + "143 St. Kitts & Nevis 194.0 205 32 7.7 NA \n", + "144 St. Lucia 171.0 315 71 10.1 NA \n", + "145 St. Vincent & the Grenadines 120.0 221 11 6.3 NA \n", + "146 Samoa 105.0 18 24 2.6 OC \n", + "147 San Marino 0.0 0 0 0.0 EU \n", + "148 Sao Tome & Principe 56.0 38 140 4.2 AF \n", + "149 Saudi Arabia 0.0 5 0 0.1 AS \n", + "150 Senegal 9.0 1 7 0.3 AF \n", + "151 Serbia 283.0 131 127 9.6 EU \n", + "152 Seychelles 157.0 25 51 4.1 AF \n", + "153 Sierra Leone 25.0 3 2 6.7 AF \n", + "154 Singapore 60.0 12 11 1.5 AS \n", + "155 Slovakia 196.0 293 116 11.4 EU \n", + "156 Slovenia 270.0 51 276 10.6 EU \n", + "157 Solomon Islands 56.0 11 1 1.2 OC \n", + "158 Somalia 0.0 0 0 0.0 AF \n", + "159 South Africa 225.0 76 81 8.2 AF \n", + "160 Spain 284.0 157 112 10.0 EU \n", + "161 Sri Lanka 16.0 104 0 2.2 AS \n", + "162 Sudan 8.0 13 0 1.7 AF \n", + "163 Suriname 128.0 178 7 5.6 SA \n", + "164 Swaziland 90.0 2 2 4.7 AF \n", + "165 Sweden 152.0 60 186 7.2 EU \n", + "166 Switzerland 185.0 100 280 10.2 EU \n", + "167 Syria 5.0 35 16 1.0 AS \n", + "168 Tajikistan 2.0 15 0 0.3 AS \n", + "169 Thailand 99.0 258 1 6.4 AS \n", + "170 Macedonia 106.0 27 86 3.9 EU \n", + "171 Timor-Leste 1.0 1 4 0.1 AS \n", + "172 Togo 36.0 2 19 1.3 AF \n", + "173 Tonga 36.0 21 5 1.1 OC \n", + "174 Trinidad & Tobago 197.0 156 7 6.4 NA \n", + "175 Tunisia 51.0 3 20 1.3 AF \n", + "176 Turkey 51.0 22 7 1.4 AS \n", + "177 Turkmenistan 19.0 71 32 2.2 AS \n", + "178 Tuvalu 6.0 41 9 1.0 OC \n", + "179 Uganda 45.0 9 0 8.3 AF \n", + "180 Ukraine 206.0 237 45 8.9 EU \n", + "181 United Arab Emirates 16.0 135 5 2.8 AS \n", + "182 United Kingdom 219.0 126 195 10.4 EU \n", + "183 Tanzania 36.0 6 1 5.7 AF \n", + "184 USA 249.0 158 84 8.7 NA \n", + "185 Uruguay 115.0 35 220 6.6 SA \n", + "186 Uzbekistan 25.0 101 8 2.4 AS \n", + "187 Vanuatu 21.0 18 11 0.9 OC \n", + "188 Venezuela 333.0 100 3 7.7 SA \n", + "189 Vietnam 111.0 2 1 2.0 AS \n", + "190 Yemen 6.0 0 0 0.1 AS \n", + "191 Zambia 32.0 19 4 2.5 AF \n", + "192 Zimbabwe 64.0 18 4 4.7 AF \n", + "\n", + " cont_AS cont_EU cont_NA cont_OC cont_SA beer_level \n", + "0 1 0 0 0 0 low \n", + "1 0 1 0 0 0 low \n", + "2 0 0 0 0 0 low \n", + "3 0 1 0 0 0 high \n", + "4 0 0 0 0 0 high \n", + "5 0 0 1 0 0 med \n", + "6 0 0 0 0 1 med \n", + "7 0 1 0 0 0 low \n", + "8 0 0 0 1 0 high \n", + "9 0 1 0 0 0 high \n", + "10 0 1 0 0 0 low \n", + "11 0 0 1 0 0 med \n", + "12 1 0 0 0 0 low \n", + "13 1 0 0 0 0 low \n", + "14 0 0 1 0 0 med \n", + "15 0 1 0 0 0 med \n", + "16 0 1 0 0 0 high \n", + "17 0 0 1 0 0 high \n", + "18 0 0 0 0 0 low \n", + "19 1 0 0 0 0 low \n", + "20 0 0 0 0 1 med \n", + "21 0 1 0 0 0 low \n", + "22 0 0 0 0 0 med \n", + "23 0 0 0 0 1 high \n", + "24 1 0 0 0 0 low \n", + "25 0 1 0 0 0 high \n", + "26 0 0 0 0 0 low \n", + "27 0 0 0 0 0 low \n", + "28 0 0 0 0 0 low \n", + "29 0 0 0 0 0 med \n", + "30 1 0 0 0 0 low \n", + "31 0 0 0 0 0 med \n", + "32 0 0 1 0 0 high \n", + "33 0 0 0 0 0 low \n", + "34 0 0 0 0 0 low \n", + "35 0 0 0 0 1 med \n", + "36 1 0 0 0 0 low \n", + "37 0 0 0 0 1 med \n", + "38 0 0 0 0 0 low \n", + "39 0 0 0 0 0 low \n", + "40 0 0 0 1 0 low \n", + "41 0 0 1 0 0 med \n", + "42 0 1 0 0 0 high \n", + "43 0 0 1 0 0 low \n", + "44 0 1 0 0 0 med \n", + "45 0 1 0 0 0 high \n", + "46 1 0 0 0 0 low \n", + "47 0 0 0 0 0 low \n", + "48 0 1 0 0 0 high \n", + "49 0 0 0 0 0 low \n", + "50 0 0 1 0 0 low \n", + "51 0 0 1 0 0 med \n", + "52 0 0 0 0 1 med \n", + "53 0 0 0 0 0 low \n", + "54 0 0 1 0 0 low \n", + "55 0 0 0 0 0 low \n", + "56 0 0 0 0 0 low \n", + "57 0 1 0 0 0 high \n", + "58 0 0 0 0 0 low \n", + "59 0 0 0 1 0 low \n", + "60 0 1 0 0 0 high \n", + "61 0 1 0 0 0 med \n", + "62 0 0 0 0 0 high \n", + "63 0 0 0 0 0 low \n", + "64 0 1 0 0 0 low \n", + "65 0 1 0 0 0 high \n", + "66 0 0 0 0 0 low \n", + "67 0 1 0 0 0 med \n", + "68 0 0 1 0 0 med \n", + "69 0 0 1 0 0 low \n", + "70 0 0 0 0 0 low \n", + "71 0 0 0 0 0 low \n", + "72 0 0 0 0 1 low \n", + "73 0 0 1 0 0 low \n", + "74 0 0 1 0 0 low \n", + "75 0 1 0 0 0 high \n", + "76 0 1 0 0 0 high \n", + "77 1 0 0 0 0 low \n", + "78 1 0 0 0 0 low \n", + "79 1 0 0 0 0 low \n", + "80 1 0 0 0 0 low \n", + "81 0 1 0 0 0 high \n", + "82 1 0 0 0 0 low \n", + "83 0 1 0 0 0 low \n", + "84 0 0 1 0 0 low \n", + "85 1 0 0 0 0 low \n", + "86 1 0 0 0 0 low \n", + "87 1 0 0 0 0 med \n", + "88 0 0 0 0 0 low \n", + "89 0 0 0 1 0 low \n", + "90 1 0 0 0 0 low \n", + "91 1 0 0 0 0 low \n", + "92 1 0 0 0 0 low \n", + "93 0 1 0 0 0 high \n", + "94 1 0 0 0 0 low \n", + "95 0 0 0 0 0 low \n", + "96 0 0 0 0 0 low \n", + "97 0 0 0 0 0 low \n", + "98 0 1 0 0 0 high \n", + "99 0 1 0 0 0 high \n", + "100 0 0 0 0 0 low \n", + "101 0 0 0 0 0 low \n", + "102 1 0 0 0 0 low \n", + "103 1 0 0 0 0 low \n", + "104 0 0 0 0 0 low \n", + "105 0 1 0 0 0 med \n", + "106 0 0 0 1 0 low \n", + "107 0 0 0 0 0 low \n", + "108 0 0 0 0 0 low \n", + "109 0 0 1 0 0 high \n", + "110 0 0 0 1 0 low \n", + "111 0 1 0 0 0 low \n", + "112 1 0 0 0 0 low \n", + "113 0 1 0 0 0 low \n", + "114 0 0 0 0 0 low \n", + "115 0 0 0 0 0 low \n", + "116 1 0 0 0 0 low \n", + "117 0 0 0 0 0 high \n", + "118 0 0 0 1 0 low \n", + "119 1 0 0 0 0 low \n", + "120 0 1 0 0 0 high \n", + "121 0 0 0 1 0 high \n", + "122 0 0 1 0 0 low \n", + "123 0 0 0 0 0 low \n", + "124 0 0 0 0 0 low \n", + "125 0 0 0 1 0 med \n", + "126 0 1 0 0 0 med \n", + "127 1 0 0 0 0 low \n", + "128 1 0 0 0 0 low \n", + "129 0 0 0 1 0 high \n", + "130 0 0 1 0 0 high \n", + "131 0 0 0 1 0 low \n", + "132 0 0 0 0 1 high \n", + "133 0 0 0 0 1 med \n", + "134 1 0 0 0 0 low \n", + "135 0 1 0 0 0 high \n", + "136 0 1 0 0 0 med \n", + "137 1 0 0 0 0 low \n", + "138 1 0 0 0 0 med \n", + "139 0 1 0 0 0 med \n", + "140 0 1 0 0 0 high \n", + "141 1 0 0 0 0 high \n", + "142 0 0 0 0 0 low \n", + "143 0 0 1 0 0 med \n", + "144 0 0 1 0 0 med \n", + "145 0 0 1 0 0 med \n", + "146 0 0 0 1 0 med \n", + "147 0 1 0 0 0 low \n", + "148 0 0 0 0 0 low \n", + "149 1 0 0 0 0 low \n", + "150 0 0 0 0 0 low \n", + "151 0 1 0 0 0 high \n", + "152 0 0 0 0 0 med \n", + "153 0 0 0 0 0 low \n", + "154 1 0 0 0 0 low \n", + "155 0 1 0 0 0 med \n", + "156 0 1 0 0 0 high \n", + "157 0 0 0 1 0 low \n", + "158 0 0 0 0 0 low \n", + "159 0 0 0 0 0 high \n", + "160 0 1 0 0 0 high \n", + "161 1 0 0 0 0 low \n", + "162 0 0 0 0 0 low \n", + "163 0 0 0 0 1 med \n", + "164 0 0 0 0 0 low \n", + "165 0 1 0 0 0 med \n", + "166 0 1 0 0 0 med \n", + "167 1 0 0 0 0 low \n", + "168 1 0 0 0 0 low \n", + "169 1 0 0 0 0 low \n", + "170 0 1 0 0 0 med \n", + "171 1 0 0 0 0 low \n", + "172 0 0 0 0 0 low \n", + "173 0 0 0 1 0 low \n", + "174 0 0 1 0 0 med \n", + "175 0 0 0 0 0 low \n", + "176 1 0 0 0 0 low \n", + "177 1 0 0 0 0 low \n", + "178 0 0 0 1 0 low \n", + "179 0 0 0 0 0 low \n", + "180 0 1 0 0 0 high \n", + "181 1 0 0 0 0 low \n", + "182 0 1 0 0 0 high \n", + "183 0 0 0 0 0 low \n", + "184 0 0 1 0 0 high \n", + "185 0 0 0 0 1 med \n", + "186 1 0 0 0 0 low \n", + "187 0 0 0 1 0 low \n", + "188 0 0 0 0 1 high \n", + "189 1 0 0 0 0 med \n", + "190 1 0 0 0 0 low \n", + "191 0 0 0 0 0 low \n", + "192 0 0 0 0 0 low \n" + ] + } + ], + "source": [ + "# Change the options temporarily (settings are restored when you exit the \"with\" block).\n", + "with pd.option_context('max_rows', None, 'max_columns', None):\n", + " print(drinks)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "### Summary\n", + "\n", + "Believe it or not, we've only barely touched the surface of everything that Pandas offers. Don't worry if you don't remember most of it — for now, just knowing what exists is key. Remember that the more you use Pandas to manipulate data, the more of these functions you will take interest in, look up, and remember.\n", + "\n", + "In this notebook, the most important things to familiarize yourself with are the basics:\n", + "- Manipulating `DataFrames` and `Series`\n", + "- Filtering columns and rows\n", + "- Handling missing values\n", + "- Split-apply-combine (this one takes some practice!)" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Data Manipulation and Exploration/Data Manipulation.ipynb b/Data Manipulation and Exploration/Data Manipulation.ipynb index ee3a856..576cbb8 100644 --- a/Data Manipulation and Exploration/Data Manipulation.ipynb +++ b/Data Manipulation and Exploration/Data Manipulation.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -178,12 +178,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# using the read_csv method of pandas we read file \n", - "users = pd.read_csv('./data/user.tbl', sep='|')" + "users = pd.read_csv('./data/user.tbl', sep='|')\n", + "print(users)\n" ] }, { @@ -268,206 +269,6 @@ " 15213\n", " \n", " \n", - " 5\n", - " 6\n", - " 42\n", - " M\n", - " executive\n", - " 98101\n", - " \n", - " \n", - " 6\n", - " 7\n", - " 57\n", - " M\n", - " administrator\n", - " 91344\n", - " \n", - " \n", - " 7\n", - " 8\n", - " 36\n", - " M\n", - " administrator\n", - " 05201\n", - " \n", - " \n", - " 8\n", - " 9\n", - " 29\n", - " M\n", - " student\n", - " 01002\n", - " \n", - " \n", - " 9\n", - " 10\n", - " 53\n", - " M\n", - " lawyer\n", - " 90703\n", - " \n", - " \n", - " 10\n", - " 11\n", - " 39\n", - " F\n", - " other\n", - " 30329\n", - " \n", - " \n", - " 11\n", - " 12\n", - " 28\n", - " F\n", - " other\n", - " 06405\n", - " \n", - " \n", - " 12\n", - " 13\n", - " 47\n", - " M\n", - " educator\n", - " 29206\n", - " \n", - " \n", - " 13\n", - " 14\n", - " 45\n", - " M\n", - " scientist\n", - " 55106\n", - " \n", - " \n", - " 14\n", - " 15\n", - " 49\n", - " F\n", - " educator\n", - " 97301\n", - " \n", - " \n", - " 15\n", - " 16\n", - " 21\n", - " M\n", - " entertainment\n", - " 10309\n", - " \n", - " \n", - " 16\n", - " 17\n", - " 30\n", - " M\n", - " programmer\n", - " 06355\n", - " \n", - " \n", - " 17\n", - " 18\n", - " 35\n", - " F\n", - " other\n", - " 37212\n", - " \n", - " \n", - " 18\n", - " 19\n", - " 40\n", - " M\n", - " librarian\n", - " 02138\n", - " \n", - " \n", - " 19\n", - " 20\n", - " 42\n", - " F\n", - " homemaker\n", - " 95660\n", - " \n", - " \n", - " 20\n", - " 21\n", - " 26\n", - " M\n", - " writer\n", - " 30068\n", - " \n", - " \n", - " 21\n", - " 22\n", - " 25\n", - " M\n", - " writer\n", - " 40206\n", - " \n", - " \n", - " 22\n", - " 23\n", - " 30\n", - " F\n", - " artist\n", - " 48197\n", - " \n", - " \n", - " 23\n", - " 24\n", - " 21\n", - " F\n", - " artist\n", - " 94533\n", - " \n", - " \n", - " 24\n", - " 25\n", - " 39\n", - " M\n", - " engineer\n", - " 55107\n", - " \n", - " \n", - " 25\n", - " 26\n", - " 49\n", - " M\n", - " engineer\n", - " 21044\n", - " \n", - " \n", - " 26\n", - " 27\n", - " 40\n", - " F\n", - " librarian\n", - " 30030\n", - " \n", - " \n", - " 27\n", - " 28\n", - " 32\n", - " M\n", - " writer\n", - " 55369\n", - " \n", - " \n", - " 28\n", - " 29\n", - " 41\n", - " M\n", - " programmer\n", - " 94043\n", - " \n", - " \n", - " 29\n", - " 30\n", - " 7\n", - " M\n", - " student\n", - " 55436\n", - " \n", - " \n", " ...\n", " ...\n", " ...\n", @@ -476,206 +277,6 @@ " ...\n", " \n", " \n", - " 913\n", - " 914\n", - " 44\n", - " F\n", - " other\n", - " 08105\n", - " \n", - " \n", - " 914\n", - " 915\n", - " 50\n", - " M\n", - " entertainment\n", - " 60614\n", - " \n", - " \n", - " 915\n", - " 916\n", - " 27\n", - " M\n", - " engineer\n", - " N2L5N\n", - " \n", - " \n", - " 916\n", - " 917\n", - " 22\n", - " F\n", - " student\n", - " 20006\n", - " \n", - " \n", - " 917\n", - " 918\n", - " 40\n", - " M\n", - " scientist\n", - " 70116\n", - " \n", - " \n", - " 918\n", - " 919\n", - " 25\n", - " M\n", - " other\n", - " 14216\n", - " \n", - " \n", - " 919\n", - " 920\n", - " 30\n", - " F\n", - " artist\n", - " 90008\n", - " \n", - " \n", - " 920\n", - " 921\n", - " 20\n", - " F\n", - " student\n", - " 98801\n", - " \n", - " \n", - " 921\n", - " 922\n", - " 29\n", - " F\n", - " administrator\n", - " 21114\n", - " \n", - " \n", - " 922\n", - " 923\n", - " 21\n", - " M\n", - " student\n", - " E2E3R\n", - " \n", - " \n", - " 923\n", - " 924\n", - " 29\n", - " M\n", - " other\n", - " 11753\n", - " \n", - " \n", - " 924\n", - " 925\n", - " 18\n", - " F\n", - " salesman\n", - " 49036\n", - " \n", - " \n", - " 925\n", - " 926\n", - " 49\n", - " M\n", - " entertainment\n", - " 01701\n", - " \n", - " \n", - " 926\n", - " 927\n", - " 23\n", - " M\n", - " programmer\n", - " 55428\n", - " \n", - " \n", - " 927\n", - " 928\n", - " 21\n", - " M\n", - " student\n", - " 55408\n", - " \n", - " \n", - " 928\n", - " 929\n", - " 44\n", - " M\n", - " scientist\n", - " 53711\n", - " \n", - " \n", - " 929\n", - " 930\n", - " 28\n", - " F\n", - " scientist\n", - " 07310\n", - " \n", - " \n", - " 930\n", - " 931\n", - " 60\n", - " M\n", - " educator\n", - " 33556\n", - " \n", - " \n", - " 931\n", - " 932\n", - " 58\n", - " M\n", - " educator\n", - " 06437\n", - " \n", - " \n", - " 932\n", - " 933\n", - " 28\n", - " M\n", - " student\n", - " 48105\n", - " \n", - " \n", - " 933\n", - " 934\n", - " 61\n", - " M\n", - " engineer\n", - " 22902\n", - " \n", - " \n", - " 934\n", - " 935\n", - " 42\n", - " M\n", - " doctor\n", - " 66221\n", - " \n", - " \n", - " 935\n", - " 936\n", - " 24\n", - " M\n", - " other\n", - " 32789\n", - " \n", - " \n", - " 936\n", - " 937\n", - " 48\n", - " M\n", - " educator\n", - " 98072\n", - " \n", - " \n", - " 937\n", - " 938\n", - " 38\n", - " F\n", - " technician\n", - " 55038\n", - " \n", - " \n", " 938\n", " 939\n", " 26\n", @@ -727,57 +328,7 @@ "2 3 23 M writer 32067\n", "3 4 24 M technician 43537\n", "4 5 33 F other 15213\n", - "5 6 42 M executive 98101\n", - "6 7 57 M administrator 91344\n", - "7 8 36 M administrator 05201\n", - "8 9 29 M student 01002\n", - "9 10 53 M lawyer 90703\n", - "10 11 39 F other 30329\n", - "11 12 28 F other 06405\n", - "12 13 47 M educator 29206\n", - "13 14 45 M scientist 55106\n", - "14 15 49 F educator 97301\n", - "15 16 21 M entertainment 10309\n", - "16 17 30 M programmer 06355\n", - "17 18 35 F other 37212\n", - "18 19 40 M librarian 02138\n", - "19 20 42 F homemaker 95660\n", - "20 21 26 M writer 30068\n", - "21 22 25 M writer 40206\n", - "22 23 30 F artist 48197\n", - "23 24 21 F artist 94533\n", - "24 25 39 M engineer 55107\n", - "25 26 49 M engineer 21044\n", - "26 27 40 F librarian 30030\n", - "27 28 32 M writer 55369\n", - "28 29 41 M programmer 94043\n", - "29 30 7 M student 55436\n", ".. ... ... ... ... ...\n", - "913 914 44 F other 08105\n", - "914 915 50 M entertainment 60614\n", - "915 916 27 M engineer N2L5N\n", - "916 917 22 F student 20006\n", - "917 918 40 M scientist 70116\n", - "918 919 25 M other 14216\n", - "919 920 30 F artist 90008\n", - "920 921 20 F student 98801\n", - "921 922 29 F administrator 21114\n", - "922 923 21 M student E2E3R\n", - "923 924 29 M other 11753\n", - "924 925 18 F salesman 49036\n", - "925 926 49 M entertainment 01701\n", - "926 927 23 M programmer 55428\n", - "927 928 21 M student 55408\n", - "928 929 44 M scientist 53711\n", - "929 930 28 F scientist 07310\n", - "930 931 60 M educator 33556\n", - "931 932 58 M educator 06437\n", - "932 933 28 M student 48105\n", - "933 934 61 M engineer 22902\n", - "934 935 42 M doctor 66221\n", - "935 936 24 M other 32789\n", - "936 937 48 M educator 98072\n", - "937 938 38 F technician 55038\n", "938 939 26 F student 33319\n", "939 940 32 M administrator 02215\n", "940 941 20 M student 97229\n", @@ -798,40 +349,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pandas.core.frame.DataFrame" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(users) # DataFrame" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(943, 5)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# how to get the size of the dtaframe?\n", "users.shape" @@ -839,322 +368,326 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idagegenderoccupationzip_code
0124Mtechnician85711
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2323Mwriter32067
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" - ], + "outputs": [], + "source": [ + "users.head(3) # Print the first five rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "users.head(10) # Print the first 10 rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "users.head() # Print the last five rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "users.tail(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { "text/plain": [ - " user_id age gender occupation zip_code\n", - "0 1 24 M technician 85711\n", - "1 2 53 F other 94043\n", - "2 3 23 M writer 32067" + "RangeIndex(start=0, stop=943, step=1)" ] }, - "execution_count": 11, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users.head(3) # Print the first five rows." + " # The row index (aka \"the row labels\" — in this case integers)\n", + "users.index " ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Column names (which is \"an index\")\n", + "users.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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user_idagegenderoccupationzip_code
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" - ], "text/plain": [ - " user_id age gender occupation zip_code\n", - "0 1 24 M technician 85711\n", - "1 2 53 F other 94043\n", - "2 3 23 M writer 32067\n", - "3 4 24 M technician 43537\n", - "4 5 33 F other 15213\n", - "5 6 42 M executive 98101\n", - "6 7 57 M administrator 91344\n", - "7 8 36 M administrator 05201\n", - "8 9 29 M student 01002\n", - "9 10 53 M lawyer 90703" + "user_id int64\n", + "age int64\n", + "gender object\n", + "occupation object\n", + "zip_code object\n", + "dtype: object" ] }, - "execution_count": 12, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users.head(10) # Print the first 10 rows." + "# Datatypes of each column — each column is stored as an ndarray, which has a datatype\n", + "users.dtypes" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Number of rows and columns\n", + "users.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "users.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", + "text/plain": [ + "array([[1, 24, 'M', 'technician', '85711'],\n", + " [2, 53, 'F', 'other', '94043'],\n", + " [3, 23, 'M', 'writer', '32067'],\n", + " ...,\n", + " [941, 20, 'M', 'student', '97229'],\n", + " [942, 48, 'F', 'librarian', '78209'],\n", + " [943, 22, 'M', 'student', '77841']], dtype=object)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# All values as a NumPy array\n", + "users.values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Concise summary (including memory usage) — useful to quickly see if nulls exist\n", + "users.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Select or index data.**
\n", + "Pandas `DataFrame`s have structural similarities with Python-style lists and dictionaries. \n", + "In the example below, we select a column of data using the name of the column in a similar manner to how we select a dictionary value with the dictionary key." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select a column — returns a Pandas Series (essentially an ndarray with an index)\n", + "users['gender']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# DataFrame columns are Pandas Series.\n", + "type(users['age'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 M\n", + "1 F\n", + "2 M\n", + "3 M\n", + "4 F\n", + " ..\n", + "938 F\n", + "939 M\n", + "940 M\n", + "941 F\n", + "942 M\n", + "Name: gender, Length: 943, dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select one column using the DataFrame attribute.\n", + "users.gender\n", + "# While a useful shorthand, these attributes only exist\n", + "# if the column name has no punctuations or spaces." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Summarize (describe) the data.**
\n", + "Pandas has a bunch of built-in methods to quickly summarize your data and provide you with a quick general understanding." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idagegenderoccupationzip_code
0124Mtechnician85711count943.000000943.000000
1253Fother94043mean472.00000034.051962
2323Mwriter32067std272.36495112.192740
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" ], "text/plain": [ - " user_id age gender occupation zip_code\n", - "0 1 24 M technician 85711\n", - "1 2 53 F other 94043\n", - "2 3 23 M writer 32067\n", - "3 4 24 M technician 43537\n", - "4 5 33 F other 15213" + " user_id age\n", + "count 943.000000 943.000000\n", + "mean 472.000000 34.051962\n", + "std 272.364951 12.192740\n", + "min 1.000000 7.000000\n", + "25% 236.500000 25.000000\n", + "50% 472.000000 31.000000\n", + "75% 707.500000 43.000000\n", + "max 943.000000 73.000000" ] }, - "execution_count": 13, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users.head() # Print the last five rows." + "# Describe all numeric columns.\n", + "users.describe()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1187,430 +720,242 @@ " \n", " \n", " \n", - " 939\n", - " 940\n", - " 32\n", - " M\n", - " administrator\n", - " 02215\n", + " count\n", + " 943.000000\n", + " 943.000000\n", + " 943\n", + " 943\n", + " 943\n", " \n", " \n", - 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" user_id age gender occupation zip_code\n", - "939 940 32 M administrator 02215\n", - "940 941 20 M student 97229\n", - "941 942 48 F librarian 78209\n", - "942 943 22 M student 77841" + " user_id age gender occupation zip_code\n", + "count 943.000000 943.000000 943 943 943\n", + "unique NaN NaN 2 21 795\n", + "top NaN NaN M student 55414\n", + "freq NaN NaN 670 196 9\n", + "mean 472.000000 34.051962 NaN NaN NaN\n", + "std 272.364951 12.192740 NaN NaN NaN\n", + "min 1.000000 7.000000 NaN NaN NaN\n", + "25% 236.500000 25.000000 NaN NaN NaN\n", + "50% 472.000000 31.000000 NaN NaN NaN\n", + "75% 707.500000 43.000000 NaN NaN NaN\n", + "max 943.000000 73.000000 NaN NaN NaN" ] }, - "execution_count": 4, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users.tail(4)" + "# Describe all columns, including non-numeric.\n", + "users.describe(include='all')" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RangeIndex(start=0, stop=943, step=1)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - " # The row index (aka \"the row labels\" — in this case integers)\n", - "users.index " + "# Describe a single column — recall that \"users.gender\" refers to a Series.\n", + "users.gender.describe()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['user_id', 'age', 'gender', 'occupation', 'zip_code'], dtype='object')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Column names (which is \"an index\")\n", - "users.columns" + "# Calculate the mean of the ages.\n", + "users.age.mean()" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "user_id int64\n", - "age int64\n", - "gender object\n", - "occupation object\n", - "zip_code object\n", - "dtype: object" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Datatypes of each column — each column is stored as an ndarray, which has a datatype\n", - "users.dtypes" + "# Draw a histogram of a column (the distribution of ages).\n", + "users.age.hist(bins=5);" ] }, { - "cell_type": "code", - "execution_count": 18, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(943, 5)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "# Number of rows and columns\n", - "users.shape" + "**Count the number of occurrences of each value.**" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "943" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "users.shape[0]" + "users.gender.value_counts() \n", + "\n", + "# Most useful for categorical variables" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 24, 'M', 'technician', '85711'],\n", - " [2, 53, 'F', 'other', '94043'],\n", - " [3, 23, 'M', 'writer', '32067'],\n", - " ...,\n", - " [941, 20, 'M', 'student', '97229'],\n", - " [942, 48, 'F', 'librarian', '78209'],\n", - " [943, 22, 'M', 'student', '77841']], dtype=object)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# All values as a NumPy array\n", - "users.values" + "# How many of each zip codes do we have? \n", + "users.zip_code.value_counts()" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 943 entries, 0 to 942\n", - "Data columns (total 5 columns):\n", - "user_id 943 non-null int64\n", - "age 943 non-null int64\n", - "gender 943 non-null object\n", - "occupation 943 non-null object\n", - "zip_code 943 non-null object\n", - "dtypes: int64(2), object(3)\n", - "memory usage: 36.9+ KB\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "# Concise summary (including memory usage) — useful to quickly see if nulls exist\n", - "users.info()" + "users.gender.value_counts()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "** Select or index data.**
\n", - "Pandas `DataFrame`s have structural similarities with Python-style lists and dictionaries. \n", - "In the example below, we select a column of data using the name of the column in a similar manner to how we select a dictionary value with the dictionary key." + "users.gender.value_counts().plot(kind='bar') # Quick plot by category" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 F\n", - "2 M\n", - "3 M\n", - "4 F\n", - "5 M\n", - "6 M\n", - "7 M\n", - "8 M\n", - "9 M\n", - "10 F\n", - "11 F\n", - "12 M\n", - "13 M\n", - "14 F\n", - "15 M\n", - "16 M\n", - "17 F\n", - "18 M\n", - "19 F\n", - "20 M\n", - "21 M\n", - "22 F\n", - "23 F\n", - "24 M\n", - "25 M\n", - "26 F\n", - "27 M\n", - "28 M\n", - "29 M\n", - " ..\n", - "913 F\n", - "914 M\n", - "915 M\n", - "916 F\n", - "917 M\n", - "918 M\n", - "919 F\n", - "920 F\n", - "921 F\n", - "922 M\n", - "923 M\n", - "924 F\n", - "925 M\n", - "926 M\n", - "927 M\n", - "928 M\n", - "929 F\n", - "930 M\n", - "931 M\n", - "932 M\n", - "933 M\n", - "934 M\n", - "935 M\n", - "936 M\n", - "937 F\n", - "938 F\n", - "939 M\n", - "940 M\n", - "941 F\n", - "942 M\n", - "Name: gender, Length: 943, dtype: object" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Select a column — returns a Pandas Series (essentially an ndarray with an index)\n", - "users['gender']" + "# Can also be used with numeric variables\n", + "# Try .sort_index() to sort by indices or .sort_values() to sort by counts.\n", + "users.age.value_counts()" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pandas.core.series.Series" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# DataFrame columns are Pandas Series.\n", - "type(users['age'])" + "users.age.value_counts().sort_index()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 F\n", - "2 M\n", - "3 M\n", - "4 F\n", - "5 M\n", - "6 M\n", - "7 M\n", - "8 M\n", - "9 M\n", - "10 F\n", - "11 F\n", - "12 M\n", - "13 M\n", - "14 F\n", - "15 M\n", - "16 M\n", - "17 F\n", - "18 M\n", - "19 F\n", - "20 M\n", - "21 M\n", - "22 F\n", - "23 F\n", - "24 M\n", - "25 M\n", - "26 F\n", - "27 M\n", - "28 M\n", - "29 M\n", - " ..\n", - "913 F\n", - "914 M\n", - "915 M\n", - "916 F\n", - "917 M\n", - "918 M\n", - "919 F\n", - "920 F\n", - "921 F\n", - "922 M\n", - "923 M\n", - "924 F\n", - "925 M\n", - "926 M\n", - "927 M\n", - "928 M\n", - "929 F\n", - "930 M\n", - "931 M\n", - "932 M\n", - "933 M\n", - "934 M\n", - "935 M\n", - "936 M\n", - "937 F\n", - "938 F\n", - "939 M\n", - "940 M\n", - "941 F\n", - "942 M\n", - "Name: gender, Length: 943, dtype: object" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Select one column using the DataFrame attribute.\n", - "users.gender\n", - "# While a useful shorthand, these attributes only exist\n", - "# if the column name has no punctuations or spaces." + "users.age.value_counts().sort_index().plot(kind='bar', figsize=(16,5)); # Bigger plot by increasing age\n", + "plt.xlabel('Age');\n", + "plt.ylabel('Number of users');\n", + "plt.title('Number of users per age');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Summarize (describe) the data.**
\n", - "Pandas has a bunch of built-in methods to quickly summarize your data and provide you with a quick general understanding." + "# Excercise 1 \n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -1634,251 +979,121 @@ " \n", " \n", " \n", - " user_id\n", - " age\n", + " country\n", + " beer_servings\n", + " spirit_servings\n", + " wine_servings\n", + " total_litres_of_pure_alcohol\n", + " continent\n", " \n", " \n", " \n", " \n", - " count\n", - " 943.000000\n", - " 943.000000\n", - " \n", - " \n", - " mean\n", - " 472.000000\n", - " 34.051962\n", - " \n", - " \n", - " std\n", - " 272.364951\n", - " 12.192740\n", - " \n", - " \n", - " min\n", - " 1.000000\n", - " 7.000000\n", + " 0\n", + " Afghanistan\n", + " 0\n", + " 0\n", + " 0\n", + " 0.0\n", + " AS\n", " \n", " \n", - " 25%\n", - " 236.500000\n", - " 25.000000\n", + " 1\n", + " Albania\n", + " 89\n", + " 132\n", + " 54\n", + " 4.9\n", + " EU\n", " \n", " \n", - " 50%\n", - " 472.000000\n", - " 31.000000\n", + " 2\n", + " Algeria\n", + " 25\n", + " 0\n", + " 14\n", + " 0.7\n", + " AF\n", " \n", " \n", - " 75%\n", - " 707.500000\n", - " 43.000000\n", + " 3\n", + " Andorra\n", + " 245\n", + " 138\n", + " 312\n", + " 12.4\n", + " EU\n", " \n", " \n", - " max\n", - " 943.000000\n", - " 73.000000\n", + " 4\n", + " Angola\n", + " 217\n", + " 57\n", + " 45\n", + " 5.9\n", + " AF\n", " \n", " \n", "\n", "" ], "text/plain": [ - " user_id age\n", - "count 943.000000 943.000000\n", - "mean 472.000000 34.051962\n", - "std 272.364951 12.192740\n", - "min 1.000000 7.000000\n", - "25% 236.500000 25.000000\n", - "50% 472.000000 31.000000\n", - "75% 707.500000 43.000000\n", - "max 943.000000 73.000000" + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "2 Algeria 25 0 14 \n", + "3 Andorra 245 138 312 \n", + "4 Angola 217 57 45 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.0 AS \n", + "1 4.9 EU \n", + "2 0.7 AF \n", + "3 12.4 EU \n", + "4 5.9 AF " ] }, - "execution_count": 19, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Describe all numeric columns.\n", - "users.describe()" + "# Read drinks.csv into a DataFrame called \"drinks\".\n", + "import pandas as pd\n", + "drinks = pd.read_csv('./data/drinks.csv')\n", + "drinks.head()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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" - ], "text/plain": [ - " user_id age gender occupation zip_code\n", - "count 943.000000 943.000000 943 943 943\n", - "unique NaN NaN 2 21 795\n", - "top NaN NaN M student 55414\n", - "freq NaN NaN 670 196 9\n", - "mean 472.000000 34.051962 NaN NaN NaN\n", - "std 272.364951 12.192740 NaN NaN NaN\n", - "min 1.000000 7.000000 NaN NaN NaN\n", - "25% 236.500000 25.000000 NaN NaN NaN\n", - "50% 472.000000 31.000000 NaN NaN NaN\n", - "75% 707.500000 43.000000 NaN NaN NaN\n", - "max 943.000000 73.000000 NaN NaN NaN" + "(193, 6)" ] }, - "execution_count": 20, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Describe all columns, including non-numeric.\n", - "users.describe(include='all')" + "drinks.shape" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 943\n", - "unique 2\n", - "top M\n", - "freq 670\n", - "Name: gender, dtype: object" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Describe a single column — recall that \"users.gender\" refers to a Series.\n", - "users.gender.describe()" + "# Print the head and the tail.\n", + "drinks.head()\n", + "drinks.tail()" ] }, { @@ -1889,7 +1104,13 @@ { "data": { "text/plain": [ - "34.05196182396607" + "country object\n", + "beer_servings int64\n", + "spirit_servings int64\n", + "wine_servings int64\n", + "total_litres_of_pure_alcohol float64\n", + "continent object\n", + "dtype: object" ] }, "execution_count": 22, @@ -1898,8 +1119,8 @@ } ], "source": [ - "# Calculate the mean of the ages.\n", - "users.age.mean()" + "# Examine the default index, datatypes, and shape.\n", + "drinks.dtypes" ] }, { @@ -1909,153 +1130,89 @@ "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "RangeIndex(start=0, stop=193, step=1)" ] }, + "execution_count": 23, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# Draw a histogram of a column (the distribution of ages).\n", - "users.age.hist(bins=5);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Count the number of occurrences of each value.**" + "drinks.index" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "M 670\n", - "F 273\n", - "Name: gender, dtype: int64" + "(193, 6)" ] }, - "execution_count": 24, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users.gender.value_counts() \n", - "\n", - "# Most useful for categorical variables" + "drinks.shape" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "55414 9\n", - "55105 6\n", - "55337 5\n", - "20009 5\n", - "10003 5\n", - "27514 4\n", - "55408 4\n", - "55454 4\n", - "55409 3\n", - "63108 3\n", - "02215 3\n", - "94043 3\n", - "60201 3\n", - "61820 3\n", - "97301 3\n", - "55113 3\n", - "48103 3\n", - "10021 3\n", - "61801 3\n", - "11217 3\n", - "14216 3\n", - "55106 3\n", - "22903 3\n", - "55108 3\n", - "55104 3\n", - "22902 3\n", - "80525 3\n", - "62901 3\n", - "80123 2\n", - "90034 2\n", - " ..\n", - "98133 1\n", - "77841 1\n", - "V0R2M 1\n", - "59717 1\n", - "55125 1\n", - "60615 1\n", - "95662 1\n", - "03755 1\n", - "40503 1\n", - "78155 1\n", - "06811 1\n", - "79070 1\n", - "80913 1\n", - "97408 1\n", - "63146 1\n", - "25652 1\n", - "15213 1\n", - "53713 1\n", - "85281 1\n", - "99835 1\n", - "55320 1\n", - "75094 1\n", - "48823 1\n", - "92660 1\n", - "97203 1\n", - "53188 1\n", - "31909 1\n", - "01940 1\n", - "90254 1\n", - "94403 1\n", - "Name: zip_code, Length: 795, dtype: int64" + "0 0\n", + "1 89\n", + "2 25\n", + "3 245\n", + "4 217\n", + " ... \n", + "188 333\n", + "189 111\n", + "190 6\n", + "191 32\n", + "192 64\n", + "Name: beer_servings, Length: 193, dtype: int64" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# How many of each zip codes do we have? \n", - "users.zip_code.value_counts()" + "# Print the beer_servings Series.\n", + "drinks.beer_servings" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "M 670\n", - "F 273\n", - "Name: gender, dtype: int64" + "dtype('int64')" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users.gender.value_counts()" + "drinks.beer_servings.dtype" ] }, { @@ -2066,227 +1223,117 @@ { "data": { "text/plain": [ - "" + "106.16062176165804" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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F34EW8NexFPKbgTuA+6fZk1RVF0y7By3xiv5VLsndwK8DDwCfrKqvT7klSa8wBv2rXJKfsvTPY3jpOqj/PJYEGPSS1D0fmJKkzhn0ktQ5g16SOmfQS1Ln/g8Y3oofMUpQ2gAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "users.gender.value_counts().plot(kind='bar') # Quick plot by category" + "# Calculate the average beer_servings for the entire data set.\n", + "drinks.beer_servings.mean()" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "30 39\n", - "25 38\n", - "22 37\n", - "28 36\n", - "27 35\n", - "26 34\n", - "24 33\n", - "29 32\n", - "20 32\n", - "32 28\n", - "23 28\n", - "35 27\n", - "21 27\n", - "33 26\n", - "31 25\n", - "19 23\n", - "44 23\n", - "39 22\n", - "40 21\n", - "36 21\n", - "42 21\n", - "51 20\n", - "50 20\n", - "48 20\n", - "49 19\n", - "37 19\n", - "18 18\n", - "34 17\n", - "38 17\n", - "45 15\n", - " ..\n", - "47 14\n", - "43 13\n", - "46 12\n", - "53 12\n", - "55 11\n", - "41 10\n", - "57 9\n", - "60 9\n", - "52 6\n", - "56 6\n", - "15 6\n", - "13 5\n", - "16 5\n", - "54 4\n", - "63 3\n", - "14 3\n", - "65 3\n", - "70 3\n", - "61 3\n", - "59 3\n", - "58 3\n", - "64 2\n", - "68 2\n", - "69 2\n", - "62 2\n", - "11 1\n", - "10 1\n", - "73 1\n", - "66 1\n", - "7 1\n", - "Name: age, Length: 61, dtype: int64" + "AF 53\n", + "EU 45\n", + "AS 44\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Can also be used with numeric variables\n", - "# Try .sort_index() to sort by indices or .sort_values() to sort by counts.\n", - "users.age.value_counts()" + "# Count the number of occurrences of each \"continent\" value and see if it looks correct.\n", + "drinks.continent.value_counts()" ] }, { - "cell_type": "code", - "execution_count": 29, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7 1\n", - "10 1\n", - "11 1\n", - "13 5\n", - "14 3\n", - "15 6\n", - "16 5\n", - "17 14\n", - "18 18\n", - "19 23\n", - "20 32\n", - "21 27\n", - "22 37\n", - "23 28\n", - "24 33\n", - "25 38\n", - "26 34\n", - "27 35\n", - "28 36\n", - "29 32\n", - "30 39\n", - "31 25\n", - "32 28\n", - "33 26\n", - "34 17\n", - "35 27\n", - "36 21\n", - "37 19\n", - "38 17\n", - "39 22\n", - " ..\n", - "41 10\n", - "42 21\n", - "43 13\n", - "44 23\n", - "45 15\n", - "46 12\n", - "47 14\n", - "48 20\n", - "49 19\n", - "50 20\n", - "51 20\n", - "52 6\n", - "53 12\n", - "54 4\n", - "55 11\n", - "56 6\n", - "57 9\n", - "58 3\n", - "59 3\n", - "60 9\n", - "61 3\n", - "62 2\n", - "63 3\n", - "64 2\n", - "65 3\n", - "66 1\n", - "68 2\n", - "69 2\n", - "70 3\n", - "73 1\n", - "Name: age, Length: 61, dtype: int64" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "users.age.value_counts().sort_index()" + "\n", + "### Filtering and Sorting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "- **Objective:** Filter and sort data using Pandas.\n", + "\n", + "We can use simple operator comparisons on columns to extract relevant or drop irrelevant information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Logical filtering: Only show users with age < 20.**" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "938 False\n", + "939 False\n", + "940 False\n", + "941 False\n", + "942 False\n", + "Name: age, Length: 943, dtype: bool" ] }, + "execution_count": 30, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "users.age.value_counts().sort_index().plot(kind='bar', figsize=(16,5)); # Bigger plot by increasing age\n", - "plt.xlabel('Age');\n", - "plt.ylabel('Number of users');\n", - "plt.title('Number of users per age');" + "# Create a Series of Booleans…\n", + "# In Pandas, this comparison is performed element-wise on each row of data.\n", + "young_bool = users.age < 20\n", + "young_bool" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "# Excercise 1 \n", - "\n", - "\n" + "# …and use that Series to filter rows.\n", + "# In Pandas, indexing a DataFrame by a Series of Booleans only selects rows that are True in the Boolean.\n", + "users[users.age==14]\n", + "#users[young_bool]" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -2310,348 +1357,131 @@ " \n", " \n", " \n", - " country\n", - " beer_servings\n", - " spirit_servings\n", - " wine_servings\n", - " total_litres_of_pure_alcohol\n", - " continent\n", + " user_id\n", + " age\n", + " gender\n", + " occupation\n", + " zip_code\n", + " is_young\n", + " boolage\n", " \n", " \n", " \n", " \n", " 0\n", - " Afghanistan\n", - " 0\n", - " 0\n", + " 1\n", + " 24\n", + " M\n", + " technician\n", + " 85711\n", " 0\n", - " 0.0\n", - " AS\n", + " True\n", " \n", " \n", " 1\n", - " Albania\n", - " 89\n", - " 132\n", - " 54\n", - " 4.9\n", - " EU\n", + " 2\n", + " 53\n", + " F\n", + " other\n", + " 94043\n", + " 0\n", + " False\n", " \n", " \n", " 2\n", - " Algeria\n", - " 25\n", + " 3\n", + " 23\n", + " M\n", + " writer\n", + " 32067\n", " 0\n", - " 14\n", - " 0.7\n", - " AF\n", + " True\n", " \n", " \n", " 3\n", - " Andorra\n", - " 245\n", - " 138\n", - " 312\n", - " 12.4\n", - " EU\n", + " 4\n", + " 24\n", + " M\n", + " technician\n", + " 43537\n", + " 0\n", + " True\n", " \n", " \n", " 4\n", - " Angola\n", - " 217\n", - " 57\n", - " 45\n", - " 5.9\n", - " AF\n", + " 5\n", + " 33\n", + " F\n", + " other\n", + " 15213\n", + " 0\n", + " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country beer_servings spirit_servings wine_servings \\\n", - "0 Afghanistan 0 0 0 \n", - "1 Albania 89 132 54 \n", - "2 Algeria 25 0 14 \n", - "3 Andorra 245 138 312 \n", - "4 Angola 217 57 45 \n", - "\n", - " total_litres_of_pure_alcohol continent \n", - "0 0.0 AS \n", - "1 4.9 EU \n", - "2 0.7 AF \n", - "3 12.4 EU \n", - "4 5.9 AF " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Read drinks.csv into a DataFrame called \"drinks\".\n", - "import pandas as pd\n", - "drinks = pd.read_csv('./data/drinks.csv')\n", - "drinks.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(193, 6)" + " user_id age gender occupation zip_code is_young boolage\n", + "0 1 24 M technician 85711 0 True\n", + "1 2 53 F other 94043 0 False\n", + "2 3 23 M writer 32067 0 True\n", + "3 4 24 M technician 43537 0 True\n", + "4 5 33 F other 15213 0 False" ] }, - "execution_count": 11, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "drinks.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the head and the tail.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Examine the default index, datatypes, and shape.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the beer_servings Series.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the average beer_servings for the entire data set.\n" + "users['boolage']=users.age < 30\n", + "users.head(5)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Count the number of occurrences of each \"continent\" value and see if it looks correct.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Filtering and Sorting\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "- **Objective:** Filter and sort data using Pandas.\n", - "\n", - "We can use simple operator comparisons on columns to extract relevant or drop irrelevant information." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Logical filtering: Only show users with age < 20.**" + "# Or, combine into a single step.\n", + "users[users.age < 20]" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "5 False\n", - "6 False\n", - "7 False\n", - "8 False\n", - "9 False\n", - "10 False\n", - "11 False\n", - "12 False\n", - "13 False\n", - "14 False\n", - "15 False\n", - "16 False\n", - "17 False\n", - "18 False\n", - "19 False\n", - "20 False\n", - "21 False\n", - "22 False\n", - "23 False\n", - "24 False\n", - "25 False\n", - "26 False\n", - "27 False\n", - "28 False\n", - "29 True\n", - " ... \n", - "913 False\n", - "914 False\n", - "915 False\n", - "916 False\n", - "917 False\n", - "918 False\n", - "919 False\n", - "920 False\n", - "921 False\n", - "922 False\n", - "923 False\n", - "924 True\n", - "925 False\n", - "926 False\n", - "927 False\n", - "928 False\n", - "929 False\n", - "930 False\n", - "931 False\n", - "932 False\n", - "933 False\n", - "934 False\n", - "935 False\n", - "936 False\n", - "937 False\n", - "938 False\n", - "939 False\n", - "940 False\n", - "941 False\n", - "942 False\n", - "Name: age, Length: 943, dtype: bool" + "Index(['user_id', 'age', 'gender', 'occupation', 'zip_code'], dtype='object')" ] }, - "execution_count": 40, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create a Series of Booleans…\n", - "# In Pandas, this comparison is performed element-wise on each row of data.\n", - "young_bool = users.age < 20\n", - "young_bool" + "users.columns" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " user_id age gender occupation zip_code\n", - "205 206 14 F student 53115\n", - "812 813 14 F student 02136\n", - "886 887 14 F student 27249" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# …and use that Series to filter rows.\n", - "# In Pandas, indexing a DataFrame by a Series of Booleans only selects rows that are True in the Boolean.\n", - "users[users.age==14]\n", - "#users[young_bool]" + "users['is_young']=0" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -2680,7 +1510,7 @@ " gender\n", " occupation\n", " zip_code\n", - " boolage\n", + " is_young\n", " \n", " \n", " \n", @@ -2691,7 +1521,7 @@ " M\n", " technician\n", " 85711\n", - " True\n", + " 0\n", " \n", " \n", " 1\n", @@ -2700,7 +1530,7 @@ " F\n", " other\n", " 94043\n", - " False\n", + " 0\n", " \n", " \n", " 2\n", @@ -2709,7 +1539,7 @@ " M\n", " writer\n", " 32067\n", - " True\n", + " 0\n", " \n", " \n", " 3\n", @@ -2718,7 +1548,7 @@ " M\n", " technician\n", " 43537\n", - " True\n", + " 0\n", " \n", " \n", " 4\n", @@ -2727,304 +1557,425 @@ " F\n", " other\n", " 15213\n", - " False\n", + " 0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " user_id age gender occupation zip_code boolage\n", - "0 1 24 M technician 85711 True\n", - "1 2 53 F other 94043 False\n", - "2 3 23 M writer 32067 True\n", - "3 4 24 M technician 43537 True\n", - "4 5 33 F other 15213 False" + " user_id age gender occupation zip_code is_young\n", + "0 1 24 M technician 85711 0\n", + "1 2 53 F other 94043 0\n", + "2 3 23 M writer 32067 0\n", + "3 4 24 M technician 43537 0\n", + "4 5 33 F other 15213 0" ] }, - "execution_count": 42, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users['boolage']=users.age < 30\n", - "users.head(5)" + "users.head()" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "outputs": [], + "source": [ + "# Important: This creates a view of the original DataFrame, not a new DataFrame.\n", + "# If you alter this view (e.g., by storing it in a variable and altering that)\n", + "# You will alter only the slice of the DataFrame and not the actual DataFrame itself\n", + "# Here, notice that Pandas gives you a SettingWithCopyWarning to alert you of this.\n", + "\n", + "# It is best practice to use .loc and .iloc instead of the syntax below\n", + "\n", + "users_under20 = users[users.age < 20] # To resolve this warning, copy the `DataFrame` using `.copy()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select one column from the filtered results.\n", + "users[users.age < 20].occupation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# value_counts of resulting Series\n", + "users[users.age < 20].occupation.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Logical filtering with multiple conditions**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ampersand for `AND` condition. (This is a \"bitwise\" `AND`.)\n", + "# Important: You MUST put parentheses around each expression because `&` has a higher precedence than `<`.\n", + "users[(users.age < 20) & (users.gender=='M')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pipe for `OR` condition. (This is a \"bitwise\" `OR`.)\n", + "# Important: You MUST put parentheses around each expression because `|` has a higher precedence than `<`.\n", + "users[(users.age < 20) | (users.age > 60)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Preferred alternative to multiple `OR` conditions\n", + "\n", + "users[(users['occupation']=='doctor') | (users['occupation']=='lawyer')]\n", + "#users[users.occupation.isin(['doctor', 'lawyer'])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Sorting**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sort a Series.\n", + "users.age.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sort a DataFrame by a single column.\n", + "users.sort_values('age')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use descending order instead.\n", + "users.sort_values('age', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sort by multiple columns.\n", + "users.sort_values(['occupation', 'age'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 2\n", + "\n", + "\n", + "\n", + "Use the `drinks.csv` or `drinks` `DataFrame` from earlier to complete the following." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'drinks' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdrinks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\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;31mNameError\u001b[0m: name 'drinks' is not defined" + ] + } + ], + "source": [ + "drinks.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter DataFrame to only include European countries.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter DataFrame to only include European countries with wine_servings > 300.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the average beer_servings for all of Europe.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Determine which 10 countries have the highest total_litres_of_pure_alcohol.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Renaming, Adding, and Removing Columns\n", + "\n", + "\n", + "\n", + "- **Objective:** Manipulate `DataFrame` columns." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwinetotal_litres_of_pure_alcoholcontinent
36636717Mstudent374110Afghanistan0000.0AS
36736818Mstudent921131Albania89132544.9EU
37437517Mentertainment377772Algeria250140.7AF
39239319Mstudent836863Andorra24513831212.4EU
39639717Mstudent275144Angola21757455.9AF
...............
60060119Fartist99687
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61861917Mstudent44134188Venezuela33310037.7SA
61962018Fwriter81648189Vietnam111212.0AS
62062117Mstudent60402190Yemen6000.1AS
623624191Zambia3219Mstudent30067
62762813Mnone9430642.5AF
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90390417Fstudent61073
92492518Fsalesman4903644.7AF
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77 rows × 5 columns

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193 rows × 6 columns

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" ], "text/plain": [ - " user_id age gender occupation zip_code\n", - "29 30 7 M student 55436\n", - "35 36 19 F student 93117\n", - "51 52 18 F student 55105\n", - "56 57 16 M none 84010\n", - "66 67 17 M student 60402\n", - "67 68 19 M student 22904\n", - "100 101 15 M student 05146\n", - "109 110 19 M student 77840\n", - "141 142 13 M other 48118\n", - "178 179 15 M entertainment 20755\n", - "205 206 14 F student 53115\n", - "220 221 19 M student 20685\n", - "222 223 19 F student 47906\n", - "245 246 19 M student 28734\n", - "256 257 17 M student 77005\n", - "257 258 19 F student 77801\n", - "261 262 19 F student 78264\n", - "269 270 18 F student 63119\n", - "280 281 15 F student 06059\n", - "288 289 11 M none 94619\n", - "290 291 19 M student 44106\n", - "302 303 19 M student 14853\n", - "319 320 19 M student 24060\n", - "340 341 17 F student 44405\n", - "346 347 18 M student 90210\n", - "366 367 17 M student 37411\n", - "367 368 18 M student 92113\n", - "374 375 17 M entertainment 37777\n", - "392 393 19 M student 83686\n", - "396 397 17 M student 27514\n", - ".. ... ... ... ... ...\n", - "600 601 19 F artist 99687\n", - "608 609 13 F student 55106\n", - "617 618 15 F student 44212\n", - "618 619 17 M student 44134\n", - "619 620 18 F writer 81648\n", - "620 621 17 M student 60402\n", - "623 624 19 M student 30067\n", - "627 628 13 M none 94306\n", - "630 631 18 F student 38866\n", - "631 632 18 M student 55454\n", - "641 642 18 F student 95521\n", - "645 646 17 F student 51250\n", - "673 674 13 F student 55337\n", - "699 700 17 M student 76309\n", - "709 710 19 M student 92020\n", - "728 729 19 M student 56567\n", - "746 747 19 M other 93612\n", - "760 761 17 M student 97302\n", - "786 787 18 F student 98620\n", - "812 813 14 F student 02136\n", - "816 817 19 M student 60152\n", - "848 849 15 F student 25652\n", - "850 851 18 M other 29646\n", - "858 859 18 F other 06492\n", - "862 863 17 M student 60089\n", - "871 872 19 F student 74078\n", - "879 880 13 M student 83702\n", - "886 887 14 F student 27249\n", - "903 904 17 F student 61073\n", - "924 925 18 F salesman 49036\n", + " country beer spirit wine total_litres_of_pure_alcohol continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU\n", + "2 Algeria 25 0 14 0.7 AF\n", + "3 Andorra 245 138 312 12.4 EU\n", + "4 Angola 217 57 45 5.9 AF\n", + ".. ... ... ... ... ... ...\n", + "188 Venezuela 333 100 3 7.7 SA\n", + "189 Vietnam 111 2 1 2.0 AS\n", + "190 Yemen 6 0 0 0.1 AS\n", + "191 Zambia 32 19 4 2.5 AF\n", + "192 Zimbabwe 64 18 4 4.7 AF\n", "\n", - "[77 rows x 5 columns]" + "[193 rows x 6 columns]" ] }, - "execution_count": 35, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Or, combine into a single step.\n", - "users[users.age < 20]" + "# Rename one or more columns in a single output using value mapping.\n", + "drinks.rename(columns={'beer_servings':'beer', 'wine_servings':'wine','spirit_servings':'spirit'})" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
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" + ], "text/plain": [ - "Index(['user_id', 'age', 'gender', 'occupation', 'zip_code', 'boolage'], dtype='object')" + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.0 AS \n", + "1 4.9 EU " ] }, - "execution_count": 44, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users.columns" + "drinks.head(2)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "users['is_young']=0" + "# Rename one or more columns in the original DataFrame.\n", + "drinks.rename(columns={'beer_servings':'beer', 'wine_servings':'wine'\n", + " ,'spirit_servings':'spirit','total_litres_of_pure_alcohol':'liters'}, inplace=True)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -3411,220 +2176,188 @@ " \n", " \n", " \n", - " user_id\n", - " age\n", - " gender\n", - " occupation\n", - " zip_code\n", - " is_young\n", + " country\n", + " beer\n", + " spirit\n", + " wine\n", + " liters\n", + " continent\n", " \n", " \n", " \n", " \n", " 0\n", - " 1\n", - " 24\n", - " M\n", - " technician\n", - " 85711\n", - " 0\n", - " \n", - " \n", - " 1\n", - " 2\n", - " 53\n", - " F\n", - " other\n", - " 94043\n", + " Afghanistan\n", " 0\n", - " \n", - " \n", - " 2\n", - " 3\n", - " 23\n", - " M\n", - " writer\n", - " 32067\n", " 0\n", - " \n", - " \n", - " 3\n", - " 4\n", - " 24\n", - " M\n", - " technician\n", - " 43537\n", " 0\n", + " 0.0\n", + " AS\n", " \n", " \n", - " 4\n", - " 5\n", - " 33\n", - " F\n", - " other\n", - " 15213\n", - " 0\n", + " 1\n", + " Albania\n", + " 89\n", + " 132\n", + " 54\n", + " 4.9\n", + " EU\n", " \n", " \n", "\n", "" ], "text/plain": [ - " user_id age gender occupation zip_code is_young\n", - "0 1 24 M technician 85711 0\n", - "1 2 53 F other 94043 0\n", - "2 3 23 M writer 32067 0\n", - "3 4 24 M technician 43537 0\n", - "4 5 33 F other 15213 0" + " country beer spirit wine liters continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU" ] }, - "execution_count": 26, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "users.head()" + "drinks.head(2)" ] }, { - "cell_type": "code", - "execution_count": 27, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Important: This creates a view of the original DataFrame, not a new DataFrame.\n", - "# If you alter this view (e.g., by storing it in a variable and altering that)\n", - "# You will alter only the slice of the DataFrame and not the actual DataFrame itself\n", - "# Here, notice that Pandas gives you a SettingWithCopyWarning to alert you of this.\n", - "\n", - "# It is best practice to use .loc and .iloc instead of the syntax below\n", - "\n", - "users_under20 = users[users.age < 20] # To resolve this warning, copy the `DataFrame` using `.copy()`." + "**Easy Column Operations**
\n", + "Rather than having to reference indexes and create for loops to do column-wise operations, Pandas is smart and knows that when we add columns together we want to add the values in each row together." ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinentservingsmL
0Afghanistan0000.0AS00.0
1Albania89132544.9EU2754900.0
2Algeria250140.7AF39700.0
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" + ], "text/plain": [ - "29 student\n", - "35 student\n", - "51 student\n", - "56 none\n", - "66 student\n", - "67 student\n", - "100 student\n", - "109 student\n", - "141 other\n", - "178 entertainment\n", - "205 student\n", - "220 student\n", - "222 student\n", - "245 student\n", - "256 student\n", - "257 student\n", - "261 student\n", - "269 student\n", - "280 student\n", - "288 none\n", - "290 student\n", - "302 student\n", - "319 student\n", - "340 student\n", - "346 student\n", - "366 student\n", - "367 student\n", - "374 entertainment\n", - "392 student\n", - "396 student\n", - " ... \n", - "600 artist\n", - "608 student\n", - "617 student\n", - "618 student\n", - "619 writer\n", - "620 student\n", - "623 student\n", - "627 none\n", - "630 student\n", - "631 student\n", - "641 student\n", - "645 student\n", - "673 student\n", - "699 student\n", - "709 student\n", - "728 student\n", - "746 other\n", - "760 student\n", - "786 student\n", - "812 student\n", - "816 student\n", - "848 student\n", - "850 other\n", - "858 other\n", - "862 student\n", - "871 student\n", - "879 student\n", - "886 student\n", - "903 student\n", - "924 salesman\n", - "Name: occupation, Length: 77, dtype: object" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Select one column from the filtered results.\n", - "users[users.age < 20].occupation" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "student 64\n", - "other 4\n", - "none 3\n", - "writer 2\n", - "entertainment 2\n", - "salesman 1\n", - "artist 1\n", - "Name: occupation, dtype: int64" + " country beer spirit wine liters continent servings mL\n", + "0 Afghanistan 0 0 0 0.0 AS 0 0.0\n", + "1 Albania 89 132 54 4.9 EU 275 4900.0\n", + "2 Algeria 25 0 14 0.7 AF 39 700.0\n", + "3 Andorra 245 138 312 12.4 EU 695 12400.0\n", + "4 Angola 217 57 45 5.9 AF 319 5900.0" ] }, - "execution_count": 37, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# value_counts of resulting Series\n", - "users[users.age < 20].occupation.value_counts()" + "# Add a new column as a function of existing columns.\n", + "drinks['servings'] = drinks.beer + drinks.spirit + drinks.wine\n", + "drinks['mL'] = drinks.liters * 1000\n", + "\n", + "drinks.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Logical filtering with multiple conditions**" + "**Removing Columns**" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -3648,533 +2381,149 @@ " \n", " \n", " \n", - " user_id\n", - " age\n", - " gender\n", - " occupation\n", - " zip_code\n", - " boolage\n", - " is_young\n", + " country\n", + " beer\n", + " spirit\n", + " wine\n", + " liters\n", + " continent\n", " \n", " \n", " \n", " \n", - " 29\n", - " 30\n", - " 7\n", - " M\n", - " student\n", - " 55436\n", - " True\n", + " 0\n", + " Afghanistan\n", " 0\n", - " \n", - " \n", - " 56\n", - " 57\n", - " 16\n", - " M\n", - " none\n", - " 84010\n", - " True\n", " 0\n", - " \n", - " \n", - " 66\n", - " 67\n", - " 17\n", - " M\n", - " student\n", - " 60402\n", - " True\n", " 0\n", + " 0.0\n", + " AS\n", " \n", " \n", - " 67\n", - " 68\n", - " 19\n", - " M\n", - " student\n", - " 22904\n", - " True\n", - " 0\n", + " 1\n", + " Albania\n", + " 89\n", + " 132\n", + " 54\n", + " 4.9\n", + " EU\n", " \n", " \n", - " 100\n", - " 101\n", - " 15\n", - " M\n", - " student\n", - " 05146\n", - " True\n", + " 2\n", + " Algeria\n", + " 25\n", " 0\n", + " 14\n", + " 0.7\n", + " AF\n", " \n", " \n", - " 109\n", - " 110\n", - " 19\n", - " M\n", - " student\n", - " 77840\n", - " True\n", - " 0\n", + " 3\n", + " Andorra\n", + " 245\n", + " 138\n", + " 312\n", + " 12.4\n", + " EU\n", " \n", " \n", - " 141\n", - " 142\n", - " 13\n", - " M\n", - " other\n", - " 48118\n", - " True\n", - " 0\n", + " 4\n", + " Angola\n", + " 217\n", + " 57\n", + " 45\n", + " 5.9\n", + " AF\n", " \n", " \n", - " 178\n", - " 179\n", - " 15\n", - " M\n", - " entertainment\n", - " 20755\n", - " True\n", - " 0\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 220\n", - " 221\n", - " 19\n", - " M\n", - " student\n", - " 20685\n", - " True\n", - " 0\n", + " 188\n", + " Venezuela\n", + " 333\n", + " 100\n", + " 3\n", + " 7.7\n", + " SA\n", " \n", " \n", - " 245\n", - " 246\n", - " 19\n", - " M\n", - " student\n", - " 28734\n", - " True\n", - " 0\n", + " 189\n", + " Vietnam\n", + " 111\n", + " 2\n", + " 1\n", + " 2.0\n", + " AS\n", " \n", " \n", - " 256\n", - " 257\n", - " 17\n", - " M\n", - " student\n", - " 77005\n", - " True\n", + " 190\n", + " Yemen\n", + " 6\n", " 0\n", - " \n", - " \n", - " 288\n", - " 289\n", - " 11\n", - " M\n", - " none\n", - " 94619\n", - " True\n", " 0\n", + " 0.1\n", + " AS\n", " \n", " \n", - " 290\n", - " 291\n", + " 191\n", + " Zambia\n", + " 32\n", " 19\n", - " M\n", - " student\n", - " 44106\n", - " True\n", - " 0\n", - " \n", - " \n", - " 302\n", - " 303\n", - " 19\n", - " M\n", - " student\n", - " 14853\n", - " True\n", - " 0\n", - " \n", - " \n", - " 319\n", - " 320\n", - " 19\n", - " M\n", - " student\n", - " 24060\n", - " True\n", - " 0\n", - " \n", - " \n", - " 346\n", - " 347\n", - " 18\n", - " M\n", - " student\n", - " 90210\n", - " True\n", - " 0\n", - " \n", - " \n", - " 366\n", - " 367\n", - " 17\n", - " M\n", - " student\n", - " 37411\n", - " True\n", - " 0\n", - " \n", - " \n", - " 367\n", - " 368\n", - " 18\n", - " M\n", - " student\n", - " 92113\n", - " True\n", - " 0\n", - " \n", - " \n", - " 374\n", - " 375\n", - " 17\n", - " M\n", - " entertainment\n", - " 37777\n", - " True\n", - " 0\n", - " \n", - " \n", - " 392\n", - " 393\n", - " 19\n", - " M\n", - " student\n", - " 83686\n", - " True\n", - " 0\n", - " \n", - " \n", - " 396\n", - " 397\n", - " 17\n", - " M\n", - " student\n", - " 27514\n", - " True\n", - " 0\n", - " \n", - " \n", - " 424\n", - " 425\n", - " 19\n", - " M\n", - " student\n", - " 58644\n", - " True\n", - " 0\n", - " \n", - " \n", - " 450\n", - " 451\n", - " 16\n", - " M\n", - " student\n", - " 48446\n", - " True\n", - " 0\n", - " \n", - " \n", - " 452\n", - " 453\n", - " 18\n", - " M\n", - " student\n", - " 06333\n", - " True\n", - " 0\n", - " \n", - " \n", - " 460\n", - " 461\n", - " 15\n", - " M\n", - " student\n", - " 98102\n", - " True\n", - " 0\n", - " \n", - " \n", - " 470\n", - " 471\n", - " 10\n", - " M\n", - " student\n", - " 77459\n", - " True\n", - " 0\n", - " \n", - " \n", - " 520\n", - " 521\n", - " 19\n", - " M\n", - " student\n", - " 02146\n", - " True\n", - " 0\n", - " \n", - " \n", - " 527\n", - " 528\n", - " 18\n", - " M\n", - " student\n", - " 55104\n", - " True\n", - " 0\n", - " \n", - " \n", - " 579\n", - " 580\n", - " 16\n", - " M\n", - " student\n", - " 17961\n", - " True\n", - " 0\n", - " \n", - " \n", - " 581\n", - " 582\n", - " 17\n", - " M\n", - " student\n", - " 93003\n", - " True\n", - " 0\n", - " \n", - " \n", - " 591\n", - " 592\n", - " 18\n", - " M\n", - " student\n", - " 97520\n", - " True\n", - " 0\n", - " \n", - " \n", - " 618\n", - " 619\n", - " 17\n", - " M\n", - " student\n", - " 44134\n", - " True\n", - " 0\n", - " \n", - " \n", - " 620\n", - " 621\n", - " 17\n", - " M\n", - " student\n", - " 60402\n", - " True\n", - " 0\n", - " \n", - " \n", - " 623\n", - " 624\n", - " 19\n", - " M\n", - " student\n", - " 30067\n", - " True\n", - " 0\n", - " \n", - " \n", - " 627\n", - " 628\n", - " 13\n", - " M\n", - " none\n", - " 94306\n", - " True\n", - " 0\n", - " \n", - " \n", - " 631\n", - " 632\n", - " 18\n", - " M\n", - " student\n", - " 55454\n", - " True\n", - " 0\n", - " \n", - " \n", - " 699\n", - " 700\n", - " 17\n", - " M\n", - " student\n", - " 76309\n", - " True\n", - " 0\n", - " \n", - " \n", - " 709\n", - " 710\n", - " 19\n", - " M\n", - " student\n", - " 92020\n", - " True\n", - " 0\n", - " \n", - " \n", - " 728\n", - " 729\n", - " 19\n", - " M\n", - " student\n", - " 56567\n", - " True\n", - " 0\n", - " \n", - " \n", - " 746\n", - " 747\n", - " 19\n", - " M\n", - " other\n", - " 93612\n", - " True\n", - " 0\n", - " \n", - " \n", - " 760\n", - " 761\n", - " 17\n", - " M\n", - " student\n", - " 97302\n", - " True\n", - " 0\n", - " \n", - " \n", - " 816\n", - " 817\n", - " 19\n", - " M\n", - " student\n", - " 60152\n", - " True\n", - " 0\n", + " 4\n", + " 2.5\n", + " AF\n", " \n", " \n", - " 850\n", - " 851\n", + " 192\n", + " Zimbabwe\n", + " 64\n", " 18\n", - " M\n", - " other\n", - " 29646\n", - " True\n", - " 0\n", - " \n", - " \n", - " 862\n", - " 863\n", - " 17\n", - " M\n", - " student\n", - " 60089\n", - " True\n", - " 0\n", - " \n", - " \n", - " 879\n", - " 880\n", - " 13\n", - " M\n", - " student\n", - " 83702\n", - " True\n", - " 0\n", + " 4\n", + " 4.7\n", + " AF\n", " \n", " \n", "\n", + "

193 rows × 6 columns

\n", "" ], "text/plain": [ - " user_id age gender occupation zip_code boolage is_young\n", - "29 30 7 M student 55436 True 0\n", - "56 57 16 M none 84010 True 0\n", - "66 67 17 M student 60402 True 0\n", - "67 68 19 M student 22904 True 0\n", - "100 101 15 M student 05146 True 0\n", - "109 110 19 M student 77840 True 0\n", - "141 142 13 M other 48118 True 0\n", - "178 179 15 M entertainment 20755 True 0\n", - "220 221 19 M student 20685 True 0\n", - "245 246 19 M student 28734 True 0\n", - "256 257 17 M student 77005 True 0\n", - "288 289 11 M none 94619 True 0\n", - "290 291 19 M student 44106 True 0\n", - "302 303 19 M student 14853 True 0\n", - "319 320 19 M student 24060 True 0\n", - "346 347 18 M student 90210 True 0\n", - "366 367 17 M student 37411 True 0\n", - "367 368 18 M student 92113 True 0\n", - "374 375 17 M entertainment 37777 True 0\n", - "392 393 19 M student 83686 True 0\n", - "396 397 17 M student 27514 True 0\n", - "424 425 19 M student 58644 True 0\n", - "450 451 16 M student 48446 True 0\n", - "452 453 18 M student 06333 True 0\n", - "460 461 15 M student 98102 True 0\n", - "470 471 10 M student 77459 True 0\n", - "520 521 19 M student 02146 True 0\n", - "527 528 18 M student 55104 True 0\n", - "579 580 16 M student 17961 True 0\n", - "581 582 17 M student 93003 True 0\n", - "591 592 18 M student 97520 True 0\n", - "618 619 17 M student 44134 True 0\n", - "620 621 17 M student 60402 True 0\n", - "623 624 19 M student 30067 True 0\n", - "627 628 13 M none 94306 True 0\n", - "631 632 18 M student 55454 True 0\n", - "699 700 17 M student 76309 True 0\n", - "709 710 19 M student 92020 True 0\n", - "728 729 19 M student 56567 True 0\n", - "746 747 19 M other 93612 True 0\n", - "760 761 17 M student 97302 True 0\n", - "816 817 19 M student 60152 True 0\n", - "850 851 18 M other 29646 True 0\n", - "862 863 17 M student 60089 True 0\n", - "879 880 13 M student 83702 True 0" + " country beer spirit wine liters continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU\n", + "2 Algeria 25 0 14 0.7 AF\n", + "3 Andorra 245 138 312 12.4 EU\n", + "4 Angola 217 57 45 5.9 AF\n", + ".. ... ... ... ... ... ...\n", + "188 Venezuela 333 100 3 7.7 SA\n", + "189 Vietnam 111 2 1 2.0 AS\n", + "190 Yemen 6 0 0 0.1 AS\n", + "191 Zambia 32 19 4 2.5 AF\n", + "192 Zimbabwe 64 18 4 4.7 AF\n", + "\n", + "[193 rows x 6 columns]" ] }, - "execution_count": 50, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Ampersand for `AND` condition. (This is a \"bitwise\" `AND`.)\n", - "# Important: You MUST put parentheses around each expression because `&` has a higher precedence than `<`.\n", - "users[(users.age < 20) & (users.gender=='M')]" + "# Drop multiple columns.\n", + "drinks.drop(['mL', 'servings'],axis=1)" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -4198,315 +2547,767 @@ " \n", " \n", " \n", - " user_id\n", - " age\n", - " gender\n", - " occupation\n", - " zip_code\n", - " boolage\n", - " is_young\n", + " country\n", + " beer\n", + " spirit\n", + " wine\n", + " liters\n", + " continent\n", + " servings\n", + " mL\n", " \n", " \n", " \n", " \n", - " 29\n", - " 30\n", - " 7\n", - " M\n", - " student\n", - " 55436\n", - " True\n", + " 0\n", + " Afghanistan\n", " 0\n", - " \n", - " \n", - " 35\n", - " 36\n", - " 19\n", - " F\n", - " student\n", - " 93117\n", - " True\n", " 0\n", + " 0\n", + " 0.0\n", + " AS\n", + " 0\n", + " 0.0\n", " \n", " \n", - " 51\n", - " 52\n", - " 18\n", - " F\n", - " student\n", - " 55105\n", - " True\n", - " 0\n", - " \n", - " \n", - " 56\n", - " 57\n", - " 16\n", - " M\n", - " none\n", - " 84010\n", - " True\n", - " 0\n", + " 1\n", + " Albania\n", + " 89\n", + " 132\n", + " 54\n", + " 4.9\n", + " EU\n", + " 275\n", + " 4900.0\n", " \n", " \n", - " 66\n", - " 67\n", - " 17\n", - " M\n", - " student\n", - " 60402\n", - " True\n", + " 2\n", + " Algeria\n", + " 25\n", " 0\n", + " 14\n", + " 0.7\n", + " AF\n", + " 39\n", + " 700.0\n", " \n", - " \n", - " 67\n", - " 68\n", - " 19\n", - " M\n", - " student\n", - " 22904\n", - " True\n", - " 0\n", + " \n", + "\n", + "" + ], + "text/plain": [ + " country beer spirit wine liters continent servings mL\n", + "0 Afghanistan 0 0 0 0.0 AS 0 0.0\n", + "1 Albania 89 132 54 4.9 EU 275 4900.0\n", + "2 Algeria 25 0 14 0.7 AF 39 700.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop on the original DataFrame rather than returning a new one.\n", + "drinks.drop(['mL', 'servings'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "drinks.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Handling Missing Values\n", + "\n", + "\n", + "\n", + "\n", + "- **Objective:** Know how to handle null and missing values.\n", + "\n", + "Sometimes, values will be missing from the source data or as a byproduct of manipulations. It is very important to detect missing data. Missing data can:\n", + "\n", + "- Make the entire row ineligible to be training data for a model.\n", + "- Hint at data-collection errors.\n", + "- Indicate improper conversion or manipulation.\n", + "- Actually not be missing — it sometimes means \"zero,\" \"false,\" \"not applicable,\" or \"entered an empty string.\"\n", + "\n", + "For example, a `.csv` file might have a missing value in some data fields:\n", + "\n", + "```\n", + "tool_name,material,cost\n", + "hammer,wood,8\n", + "chainsaw,,\n", + "wrench,metal,5\n", + "```\n", + "\n", + "When this data is imported, \"null\" values will be stored in the second row (in the \"material\" and \"cost\" columns).\n", + "\n", + "> In Pandas, a \"null\" value is either `None` or `np.NaN` (Not a Number). Many fixed-size numeric datatypes (such as integers) do not have a way of representing `np.NaN`. So, numeric columns will be promoted to floating-point datatypes that do support it. For example, when importing the `.csv` file above:\n", + "\n", + "> - **For the second row:** `None` will be stored in the \"material\" column and `np.NaN` will be stored in the \"cost\" column. The entire \"cost\" column (stored as a single `ndarray`) must be stored as floating-point values to accommodate the `np.NaN`, even though an integer `8` is in the first row." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Missing values are usually excluded in calculations by default.\n", + "drinks.continent.value_counts() # Excludes missing values in the calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "NaN 23\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Includes missing values\n", + "drinks.continent.value_counts(dropna=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "188 False\n", + "189 False\n", + "190 False\n", + "191 False\n", + "192 False\n", + "Name: continent, Length: 193, dtype: bool" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count the missing values — sum() works because True is 1 and False is 0.\n", + "drinks.continent.isnull()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "23" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find missing values in a Series.\n", + "# True if missing, False if not missing\n", + "drinks.continent.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinentservingsmL
10010115Mstudent05146True0
10510661Mretired55125False05Antigua & Barbuda102128454.9NaN2754900.0
10911019Mstudent77840True011Bahamas122176516.3NaN3496300.0
14114213Mother48118True014Barbados143173366.3NaN3526300.0
17817915Mentertainment20755True017Belize26311486.8NaN3856800.0
20520614Fstudent53115True032Canada2401221008.2NaN4628200.0
21021166Msalesman32605False041Costa Rica14987114.4NaN2474400.0
22022119Mstudent20685True043Cuba9313754.2NaN2354200.0
22222319Fstudent47906True050Dominica52286266.6NaN3646600.0
24524619Mstudent28734True051Dominican Republic19314796.2NaN3496200.0
25625717Mstudent77005True054El Salvador526922.2NaN1232200.0
25725819Fstudent77801True068Grenada1994382811.9NaN66511900.0
26126219Fstudent78264True069Guatemala536922.2NaN1242200.0
26526662Fadministrator78756False073Haiti132615.9NaN3285900.0
26927018Fstudent63119True074Honduras699823.0NaN1693000.0
28028115Fstudent06059True084Jamaica829793.4NaN1883400.0
28828911Mnone94619True0109Mexico2386855.5NaN3115500.0
29029119Mstudent44106True0122Nicaragua7811813.5NaN1973500.0
30230319Mstudent14853True0130Panama285104187.2NaN4077200.0
31731865Mretired06518False0143St. Kitts & Nevis194205327.7NaN4317700.0
31932019Mstudent24060True0144St. Lucia1713157110.1NaN55710100.0
34034117Fstudent44405True0145St. Vincent & the Grenadines120221116.3NaN3526300.0
34634718Mstudent90210True174Trinidad & Tobago19715676.4NaN3606400.0
184USA249158848.7NaN4918700.0
\n", + "
" + ], + "text/plain": [ + " country beer spirit wine liters continent \\\n", + "5 Antigua & Barbuda 102 128 45 4.9 NaN \n", + "11 Bahamas 122 176 51 6.3 NaN \n", + "14 Barbados 143 173 36 6.3 NaN \n", + "17 Belize 263 114 8 6.8 NaN \n", + "32 Canada 240 122 100 8.2 NaN \n", + "41 Costa Rica 149 87 11 4.4 NaN \n", + "43 Cuba 93 137 5 4.2 NaN \n", + "50 Dominica 52 286 26 6.6 NaN \n", + "51 Dominican Republic 193 147 9 6.2 NaN \n", + "54 El Salvador 52 69 2 2.2 NaN \n", + "68 Grenada 199 438 28 11.9 NaN \n", + "69 Guatemala 53 69 2 2.2 NaN \n", + "73 Haiti 1 326 1 5.9 NaN \n", + "74 Honduras 69 98 2 3.0 NaN \n", + "84 Jamaica 82 97 9 3.4 NaN \n", + "109 Mexico 238 68 5 5.5 NaN \n", + "122 Nicaragua 78 118 1 3.5 NaN \n", + "130 Panama 285 104 18 7.2 NaN \n", + "143 St. Kitts & Nevis 194 205 32 7.7 NaN \n", + "144 St. Lucia 171 315 71 10.1 NaN \n", + "145 St. Vincent & the Grenadines 120 221 11 6.3 NaN \n", + "174 Trinidad & Tobago 197 156 7 6.4 NaN \n", + "184 USA 249 158 84 8.7 NaN \n", + "\n", + " servings mL \n", + "5 275 4900.0 \n", + "11 349 6300.0 \n", + "14 352 6300.0 \n", + "17 385 6800.0 \n", + "32 462 8200.0 \n", + "41 247 4400.0 \n", + "43 235 4200.0 \n", + "50 364 6600.0 \n", + "51 349 6200.0 \n", + "54 123 2200.0 \n", + "68 665 11900.0 \n", + "69 124 2200.0 \n", + "73 328 5900.0 \n", + "74 169 3000.0 \n", + "84 188 3400.0 \n", + "109 311 5500.0 \n", + "122 197 3500.0 \n", + "130 407 7200.0 \n", + "143 431 7700.0 \n", + "144 557 10100.0 \n", + "145 352 6300.0 \n", + "174 360 6400.0 \n", + "184 491 8700.0 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Only show rows where continent is not missing.\n", + "drinks[drinks.continent.isnull()]\n", + "#drinks[drinks.continent.notnull()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Find missing values in a `DataFrame`.**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "country 0\n", + "beer 0\n", + "spirit 0\n", + "wine 0\n", + "liters 0\n", + "continent 23\n", + "servings 0\n", + "mL 0\n", + "dtype: int64\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'plt' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdrinks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0;31m# visually\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of null values per column'\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 'plt' is not defined" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Count the missing values in each column — remember by default, axis=0.\n", + "print((drinks.isnull().sum()))\n", + "\n", + "drinks.isnull().sum().plot(kind='bar'); # visually\n", + "plt.title('Number of null values per column');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Dropping Missing Values**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(193, 8)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinentservingsmL
0Afghanistan0000.0AS00.0
34834968Mretired61455False1Albania89132544.9EU2754900.0
2Algeria250140.7AF39700.0
3Andorra24513831212.4EU69512400.0
4Angola21757455.9AF3195900.0
...............
63063118Fstudent38866True0188Venezuela33310037.7SA4367700.0
63163218Mstudent55454True0189Vietnam111212.0AS1142000.0
64164218Fstudent95521True190Yemen6000.1AS6100.0
64564617Fstudent51250True0191Zambia321942.5AF552500.0
65065165Mretired02903False0
67367413Fstudent55337True0
69970017Mstudent76309True0
70971019Mstudent92020True0
72872919Mstudent56567True0
74674719Mother93612True0
76076117Mstudent97302True0
76676770Mengineer00000False0
77677763Mprogrammer01810False0
78678718Fstudent98620True0
80280370Madministrator78212False0
81281314Fstudent02136True0
81681719Mstudent60152True0
844845192Zimbabwe64Mdoctor97405False0
84884915Fstudent25652True0
85085118Mother29646True0
85785863Meducator09645False0
85885918Fother06492True0
85986070Fretired48322False0
86286317Mstudent60089True0
87187219Fstudent74078True0
87988013Mstudent83702True0
88688714Fstudent27249True0
90390417Fstudent61073True0
92492518Fsalesman49036True0
93393461Mengineer22902False044.7AF864700.0
\n", - "

99 rows × 7 columns

\n", + "

170 rows × 8 columns

\n", "
" ], "text/plain": [ - " user_id age gender occupation zip_code boolage is_young\n", - "29 30 7 M student 55436 True 0\n", - "35 36 19 F student 93117 True 0\n", - "51 52 18 F student 55105 True 0\n", - "56 57 16 M none 84010 True 0\n", - "66 67 17 M student 60402 True 0\n", - "67 68 19 M student 22904 True 0\n", - "100 101 15 M student 05146 True 0\n", - "105 106 61 M retired 55125 False 0\n", - "109 110 19 M student 77840 True 0\n", - "141 142 13 M other 48118 True 0\n", - "178 179 15 M entertainment 20755 True 0\n", - "205 206 14 F student 53115 True 0\n", - "210 211 66 M salesman 32605 False 0\n", - "220 221 19 M student 20685 True 0\n", - "222 223 19 F student 47906 True 0\n", - "245 246 19 M student 28734 True 0\n", - "256 257 17 M student 77005 True 0\n", - "257 258 19 F student 77801 True 0\n", - "261 262 19 F student 78264 True 0\n", - "265 266 62 F administrator 78756 False 0\n", - "269 270 18 F student 63119 True 0\n", - "280 281 15 F student 06059 True 0\n", - "288 289 11 M none 94619 True 0\n", - "290 291 19 M student 44106 True 0\n", - "302 303 19 M student 14853 True 0\n", - "317 318 65 M retired 06518 False 0\n", - "319 320 19 M student 24060 True 0\n", - "340 341 17 F student 44405 True 0\n", - "346 347 18 M student 90210 True 0\n", - "348 349 68 M retired 61455 False 0\n", - ".. ... ... ... ... ... ... ...\n", - "630 631 18 F student 38866 True 0\n", - "631 632 18 M student 55454 True 0\n", - "641 642 18 F student 95521 True 0\n", - "645 646 17 F student 51250 True 0\n", - "650 651 65 M retired 02903 False 0\n", - "673 674 13 F student 55337 True 0\n", - "699 700 17 M student 76309 True 0\n", - "709 710 19 M student 92020 True 0\n", - "728 729 19 M student 56567 True 0\n", - "746 747 19 M other 93612 True 0\n", - "760 761 17 M student 97302 True 0\n", - "766 767 70 M engineer 00000 False 0\n", - "776 777 63 M programmer 01810 False 0\n", - "786 787 18 F student 98620 True 0\n", - "802 803 70 M administrator 78212 False 0\n", - "812 813 14 F student 02136 True 0\n", - "816 817 19 M student 60152 True 0\n", - "844 845 64 M doctor 97405 False 0\n", - "848 849 15 F student 25652 True 0\n", - "850 851 18 M other 29646 True 0\n", - "857 858 63 M educator 09645 False 0\n", - "858 859 18 F other 06492 True 0\n", - "859 860 70 F retired 48322 False 0\n", - "862 863 17 M student 60089 True 0\n", - "871 872 19 F student 74078 True 0\n", - "879 880 13 M student 83702 True 0\n", - "886 887 14 F student 27249 True 0\n", - "903 904 17 F student 61073 True 0\n", - "924 925 18 F salesman 49036 True 0\n", - "933 934 61 M engineer 22902 False 0\n", + " country beer spirit wine liters continent servings mL\n", + "0 Afghanistan 0 0 0 0.0 AS 0 0.0\n", + "1 Albania 89 132 54 4.9 EU 275 4900.0\n", + "2 Algeria 25 0 14 0.7 AF 39 700.0\n", + "3 Andorra 245 138 312 12.4 EU 695 12400.0\n", + "4 Angola 217 57 45 5.9 AF 319 5900.0\n", + ".. ... ... ... ... ... ... ... ...\n", + "188 Venezuela 333 100 3 7.7 SA 436 7700.0\n", + "189 Vietnam 111 2 1 2.0 AS 114 2000.0\n", + "190 Yemen 6 0 0 0.1 AS 6 100.0\n", + "191 Zambia 32 19 4 2.5 AF 55 2500.0\n", + "192 Zimbabwe 64 18 4 4.7 AF 86 4700.0\n", "\n", - "[99 rows x 7 columns]" + "[170 rows x 8 columns]" ] }, - "execution_count": 51, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Pipe for `OR` condition. (This is a \"bitwise\" `OR`.)\n", - "# Important: You MUST put parentheses around each expression because `|` has a higher precedence than `<`.\n", - "users[(users.age < 20) | (users.age > 60)]" + "# Drop a row if ANY values are missing from any column — can be dangerous!\n", + "drinks.dropna()" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -4927,337 +3433,234 @@ " \n", " \n", " \n", - " user_id\n", - " age\n", - " gender\n", - " occupation\n", - " zip_code\n", - " boolage\n", - " is_young\n", + " country\n", + " beer\n", + " spirit\n", + " wine\n", + " liters\n", + " continent\n", + " servings\n", + " mL\n", " \n", " \n", " \n", " \n", - " 9\n", - " 10\n", - " 53\n", - " M\n", - " lawyer\n", - " 90703\n", - " False\n", + " 0\n", + " Afghanistan\n", " 0\n", - " \n", - " \n", - " 124\n", - " 125\n", - " 30\n", - " M\n", - " lawyer\n", - " 22202\n", - " False\n", " 0\n", - " \n", - " \n", - " 125\n", - " 126\n", - " 28\n", - " F\n", - " lawyer\n", - " 20015\n", - " True\n", " 0\n", - " \n", - " \n", - " 137\n", - " 138\n", - " 46\n", - " M\n", - " doctor\n", - " 53211\n", - " False\n", + " 0.0\n", + " AS\n", " 0\n", + " 0.0\n", " \n", " \n", - " 160\n", - " 161\n", - " 50\n", - " M\n", - " lawyer\n", - " 55104\n", - " False\n", - " 0\n", + " 1\n", + " Albania\n", + " 89\n", + " 132\n", + " 54\n", + " 4.9\n", + " EU\n", + " 275\n", + " 4900.0\n", " \n", " \n", - " 204\n", - " 205\n", - " 47\n", - " M\n", - " lawyer\n", - " 06371\n", - " False\n", + " 2\n", + " Algeria\n", + " 25\n", " 0\n", + " 14\n", + " 0.7\n", + " AF\n", + " 39\n", + " 700.0\n", " \n", " \n", - " 250\n", - " 251\n", - " 28\n", - " M\n", - " doctor\n", - " 85032\n", - " True\n", - " 0\n", + " 3\n", + " Andorra\n", + " 245\n", + " 138\n", + " 312\n", + " 12.4\n", + " EU\n", + " 695\n", + " 12400.0\n", " \n", " \n", - " 298\n", - " 299\n", - " 29\n", - " M\n", - " doctor\n", - " 63108\n", - " True\n", - " 0\n", + " 4\n", + " Angola\n", + " 217\n", + " 57\n", + " 45\n", + " 5.9\n", + " AF\n", + " 319\n", + " 5900.0\n", " \n", " \n", - " 338\n", - " 339\n", - " 35\n", - " M\n", - " lawyer\n", - " 37901\n", - " False\n", - " 0\n", - " \n", - " \n", - " 364\n", - " 365\n", - " 29\n", - " M\n", - " lawyer\n", - " 20009\n", - " True\n", - " 0\n", - " \n", - " \n", - " 418\n", - " 419\n", - " 37\n", - " M\n", - " lawyer\n", - " 43215\n", - " False\n", - " 0\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 426\n", - " 427\n", - " 51\n", - " M\n", - " doctor\n", - " 85258\n", - " False\n", - " 0\n", + " 188\n", + " Venezuela\n", + " 333\n", + " 100\n", + " 3\n", + " 7.7\n", + " SA\n", + " 436\n", + " 7700.0\n", " \n", " \n", - " 443\n", - " 444\n", - " 51\n", - " F\n", - " lawyer\n", - " 53202\n", - " False\n", - " 0\n", + " 189\n", + " Vietnam\n", + " 111\n", + " 2\n", + " 1\n", + " 2.0\n", + " AS\n", + " 114\n", + " 2000.0\n", " \n", " \n", - " 588\n", - " 589\n", - " 21\n", - " M\n", - " lawyer\n", - " 90034\n", - " True\n", + " 190\n", + " Yemen\n", + " 6\n", " 0\n", - " \n", - " \n", - " 679\n", - " 680\n", - " 33\n", - " M\n", - " lawyer\n", - " 90405\n", - " False\n", " 0\n", + " 0.1\n", + " AS\n", + " 6\n", + " 100.0\n", " \n", " \n", - " 840\n", - " 841\n", - " 45\n", - " M\n", - " doctor\n", - " 47401\n", - " False\n", - " 0\n", + " 191\n", + " Zambia\n", + " 32\n", + " 19\n", + " 4\n", + " 2.5\n", + " AF\n", + " 55\n", + " 2500.0\n", " \n", " \n", - " 844\n", - " 845\n", + " 192\n", + " Zimbabwe\n", " 64\n", - " M\n", - " doctor\n", - " 97405\n", - " False\n", - " 0\n", - " \n", - " \n", - " 845\n", - " 846\n", - " 27\n", - " M\n", - " lawyer\n", - " 47130\n", - " True\n", - " 0\n", - " \n", - " \n", - " 934\n", - " 935\n", - " 42\n", - " M\n", - " doctor\n", - " 66221\n", - " False\n", - " 0\n", + " 18\n", + " 4\n", + " 4.7\n", + " AF\n", + " 86\n", + " 4700.0\n", " \n", " \n", "\n", + "

193 rows × 8 columns

\n", "" ], "text/plain": [ - " user_id age gender occupation zip_code boolage is_young\n", - "9 10 53 M lawyer 90703 False 0\n", - "124 125 30 M lawyer 22202 False 0\n", - "125 126 28 F lawyer 20015 True 0\n", - "137 138 46 M doctor 53211 False 0\n", - "160 161 50 M lawyer 55104 False 0\n", - "204 205 47 M lawyer 06371 False 0\n", - "250 251 28 M doctor 85032 True 0\n", - "298 299 29 M doctor 63108 True 0\n", - "338 339 35 M lawyer 37901 False 0\n", - "364 365 29 M lawyer 20009 True 0\n", - "418 419 37 M lawyer 43215 False 0\n", - "426 427 51 M doctor 85258 False 0\n", - "443 444 51 F lawyer 53202 False 0\n", - "588 589 21 M lawyer 90034 True 0\n", - "679 680 33 M lawyer 90405 False 0\n", - "840 841 45 M doctor 47401 False 0\n", - "844 845 64 M doctor 97405 False 0\n", - "845 846 27 M lawyer 47130 True 0\n", - "934 935 42 M doctor 66221 False 0" + " country beer spirit wine liters continent servings mL\n", + "0 Afghanistan 0 0 0 0.0 AS 0 0.0\n", + "1 Albania 89 132 54 4.9 EU 275 4900.0\n", + "2 Algeria 25 0 14 0.7 AF 39 700.0\n", + "3 Andorra 245 138 312 12.4 EU 695 12400.0\n", + "4 Angola 217 57 45 5.9 AF 319 5900.0\n", + ".. ... ... ... ... ... ... ... ...\n", + "188 Venezuela 333 100 3 7.7 SA 436 7700.0\n", + "189 Vietnam 111 2 1 2.0 AS 114 2000.0\n", + "190 Yemen 6 0 0 0.1 AS 6 100.0\n", + "191 Zambia 32 19 4 2.5 AF 55 2500.0\n", + "192 Zimbabwe 64 18 4 4.7 AF 86 4700.0\n", + "\n", + "[193 rows x 8 columns]" ] }, - "execution_count": 52, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Preferred alternative to multiple `OR` conditions\n", - "\n", - "users[(users['occupation']=='doctor') | (users['occupation']=='lawyer')]\n", - "#users[users.occupation.isin(['doctor', 'lawyer'])]" + "# Drop a row only if ALL values are missing.\n", + "drinks.dropna(how='all')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Sorting**" + "**Filling Missing Values**
\n", + "You may have noticed that the continent North America (NA) does not appear in the `continent` column. Pandas read in the original data and saw \"NA\", thought it was a missing value, and converted it to a `NaN`, missing value." ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "29 7\n", - "470 10\n", - "288 11\n", - "879 13\n", - "608 13\n", - "141 13\n", - "673 13\n", - "627 13\n", - "812 14\n", - "205 14\n", - "886 14\n", - "848 15\n", - "280 15\n", - "460 15\n", - "617 15\n", - "178 15\n", - "100 15\n", - "56 16\n", - "579 16\n", - "549 16\n", - "450 16\n", - "433 16\n", - "620 17\n", - "618 17\n", - "760 17\n", - "374 17\n", - "903 17\n", - "645 17\n", - "581 17\n", - "256 17\n", + "0 AS\n", + "1 EU\n", + "2 AF\n", + "3 EU\n", + "4 AF\n", " ..\n", - "89 60\n", - "307 60\n", - "930 60\n", - "751 60\n", - "468 60\n", - "463 60\n", - "233 60\n", - "693 60\n", - "933 61\n", - "350 61\n", - "105 61\n", - "519 62\n", - "265 62\n", - "857 63\n", - "776 63\n", - "363 63\n", - "844 64\n", - "422 64\n", - "317 65\n", - "650 65\n", - "563 65\n", - "210 66\n", - "348 68\n", - "572 68\n", - "558 69\n", - "584 69\n", - "766 70\n", - "802 70\n", - "859 70\n", - "480 73\n", - "Name: age, Length: 943, dtype: int64" + "188 SA\n", + "189 AS\n", + "190 AS\n", + "191 AF\n", + "192 AF\n", + "Name: continent, Length: 193, dtype: object" ] }, - "execution_count": 39, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Sort a Series.\n", - "users.age.sort_values()" + "# Fill in missing values with \"NA\" — this is dangerous to do without manually verifying them!\n", + "drinks.continent.fillna(value='NA')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Modifies \"drinks\" in-place\n", + "drinks.continent.fillna(value='NA', inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 3\n", + "\n", + "\n", + "" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -5281,11164 +3684,972 @@ " \n", " \n", " \n", - " user_id\n", - " age\n", - " gender\n", - " occupation\n", - " zip_code\n", - " boolage\n", - " is_young\n", + " City\n", + " Colors Reported\n", + " Shape Reported\n", + " State\n", + " Time\n", " \n", " \n", " \n", " \n", - " 29\n", - " 30\n", - " 7\n", - " M\n", - " student\n", - " 55436\n", - " True\n", - " 0\n", - " \n", - " \n", - " 470\n", - " 471\n", - " 10\n", - " M\n", - " student\n", - " 77459\n", - " True\n", - " 0\n", - " \n", - " \n", - " 288\n", - " 289\n", - " 11\n", - " M\n", - " none\n", - " 94619\n", - " True\n", - " 0\n", - " \n", - " \n", - " 879\n", - " 880\n", - " 13\n", - " M\n", - " student\n", - " 83702\n", - " True\n", - " 0\n", - " \n", - " \n", - " 608\n", - " 609\n", - " 13\n", - " F\n", - " student\n", - " 55106\n", - " True\n", - " 0\n", - " \n", - " \n", - " 141\n", - " 142\n", - " 13\n", - " M\n", - " other\n", - " 48118\n", - " True\n", - " 0\n", - " \n", - " \n", - " 673\n", - " 674\n", - " 13\n", - " F\n", - " student\n", - " 55337\n", - " True\n", - " 0\n", - " \n", - " \n", - " 627\n", - " 628\n", - " 13\n", - " M\n", - " none\n", - " 94306\n", - " True\n", - " 0\n", - " \n", - " \n", - " 812\n", - " 813\n", - " 14\n", - " F\n", - " student\n", - " 02136\n", - " True\n", - " 0\n", - " \n", - " \n", - " 205\n", - " 206\n", - " 14\n", - " F\n", - " student\n", - " 53115\n", - " True\n", - " 0\n", - " \n", - " \n", - " 886\n", - " 887\n", - " 14\n", - " F\n", - " student\n", - " 27249\n", - " True\n", - " 0\n", - " \n", - " \n", - " 848\n", - " 849\n", - " 15\n", - " F\n", - " student\n", - " 25652\n", - " True\n", - " 0\n", - " \n", - " \n", - " 280\n", - " 281\n", - " 15\n", - " F\n", - " student\n", - " 06059\n", - " True\n", - " 0\n", - " \n", - " \n", - " 460\n", - " 461\n", - " 15\n", - " M\n", - " student\n", - " 98102\n", - " True\n", - " 0\n", + " 0\n", + " Ithaca\n", + " NaN\n", + " TRIANGLE\n", + " NY\n", + " 6/1/1930 22:00\n", " \n", " \n", - " 617\n", - " 618\n", - " 15\n", - " F\n", - " student\n", - " 44212\n", - " True\n", - " 0\n", + " 1\n", + " Willingboro\n", + " NaN\n", + " OTHER\n", + " NJ\n", + " 6/30/1930 20:00\n", " \n", - " \n", - " 178\n", - " 179\n", - " 15\n", - " M\n", - " entertainment\n", - " 20755\n", - " True\n", - " 0\n", + " \n", + "\n", + "" + ], + "text/plain": [ + " City Colors Reported Shape Reported State Time\n", + "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", + "1 Willingboro NaN OTHER NJ 6/30/1930 20:00" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read ufo.csv into a DataFrame called \"ufo\".\n", + "ufo= pd.read_csv('./data/ufo.csv')\n", + "ufo.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(80543, 5)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the shape of the DataFrame.\n", + "ufo.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ORANGE 5216\n", + "RED 4809\n", + "GREEN 1897\n", + "Name: Colors Reported, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What are the three most common colors reported?\n", + "ufo['Colors Reported'].value_counts()[0:3]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename any columns with spaces so that they don't contain spaces.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Virginia Beach'" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For reports in VA, what's the most common city?\n", + "ufo[ufo.State=='VA']['City'].value_counts().index.tolist()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedStateTime
10010115Mstudent05146True0202ArlingtonGREENOVALVA7/13/1952 21:00
565716Mnone84010True06300ArlingtonNaNCHEVRONVA5/5/1990 21:40
57958016Mstudent17961True010278ArlingtonNaNDISKVA5/27/1997 15:30
54955016Fstudent95453True014527ArlingtonNaNOTHERVA9/10/1999 21:41
45045116Mstudent48446True017984ArlingtonREDDISKVA11/19/2000 22:00
43343416Fstudent49705True021201ArlingtonGREENFIREBALLVA1/7/2002 17:45
62062117Mstudent60402True022633ArlingtonNaNLIGHTVA7/26/2002 1:15
61861917Mstudent44134True022780ArlingtonNaNLIGHTVA8/7/2002 21:00
76076117Mstudent97302True025066ArlingtonNaNCIGARVA6/1/2003 22:34
37437517Mentertainment37777True027398ArlingtonNaNVARIOUSVA12/13/2003 2:00
90390417Fstudent61073True030608ArlingtonNaNCIRCLEVA10/9/2004 22:30
64564617Fstudent51250True031316ArlingtonNaNFORMATIONVA12/13/2004 0:30
58158217Mstudent93003True035787ArlingtonNaNLIGHTVA3/15/2006 22:30
25625717Mstudent77005True037036ArlingtonNaNTRIANGLEVA8/1/2006 22:00
........................39673ArlingtonNaNLIGHTVA4/7/2007 18:45
899060Meducator78155False041083ArlingtonNaNFLASHVA8/15/2007 1:30
30730860Mretired95076False044562ArlingtonNaNTRIANGLEVA6/6/2008 21:55
93093160Meducator33556False045450ArlingtonNaNOVALVA8/2/2008 20:30
75175260Mretired21201False048679ArlingtonNaNSPHEREVA4/24/2009 17:45
46846960Meducator78628False054613ArlingtonNaNLIGHTVA9/17/2010 19:00
46346460Mwriter94583False0
23323460Mretired94702False0
69369460Mprogrammer06365False0
93393461Mengineer22902False0
35035161Meducator49938False0
10510661Mretired55125False0
51952062Mhealthcare12603False0
26526662Fadministrator78756False0
85785863Meducator09645False0
77677763Mprogrammer01810False0
36336463Mengineer01810False0
84484564Mdoctor97405False0
42242364Mother91606False0
31731865Mretired06518False0
65065165Mretired02903False0
56356465Mretired94591False054944ArlingtonNaNSPHEREVA10/12/2010 19:00
21021166Msalesman32605False070462ArlingtonNaNFIREBALLVA6/25/2013 23:00
34834968Mretired61455False072856ArlingtonREDSPHEREVA9/26/2013 21:20
57257368Mretired48911False073408ArlingtonNaNCIRCLEVA10/19/2013 0:17
55855969Mexecutive10022False073747ArlingtonNaNLIGHTVA11/1/2013 22:42
58458569Mlibrarian98501False076144ArlingtonNaNCIGARVA2/21/2014 14:30
76676770Mengineer00000False076325ArlingtonNaNNaNVA3/1/2014 20:00
80280370Madministrator78212False076337ArlingtonNaNFORMATIONVA3/1/2014 23:30
85986070Fretired48322False077807ArlingtonRED ORANGELIGHTVA5/24/2014 22:30
48048173Mretired37771False079122ArlingtonNaNCIRCLEVA7/7/2014 21:00
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943 rows × 7 columns

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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
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" - ], - "text/plain": [ - " country beer_servings spirit_servings wine_servings \\\n", - "0 Afghanistan 0 0 0 \n", - "1 Albania 89 132 54 \n", - "\n", - " total_litres_of_pure_alcohol continent \n", - "0 0.0 AS \n", - "1 4.9 EU " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drinks.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Filter DataFrame to only include European countries.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Filter DataFrame to only include European countries with wine_servings > 300.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the average beer_servings for all of Europe.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Determine which 10 countries have the highest total_litres_of_pure_alcohol.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Renaming, Adding, and Removing Columns\n", - "\n", - "\n", - "\n", - "- **Objective:** Manipulate `DataFrame` columns." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeerspiritwinetotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
5Antigua & Barbuda102128454.9NaN
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
11Bahamas122176516.3NaN
12Bahrain426372.0AS
13Bangladesh0000.0AS
14Barbados143173366.3NaN
15Belarus1423734214.4EU
16Belgium2958421210.5EU
17Belize26311486.8NaN
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
.....................
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
174Trinidad & Tobago19715676.4NaN
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
184USA249158848.7NaN
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
\n", - "

193 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " country beer spirit wine total_litres_of_pure_alcohol \\\n", - "0 Afghanistan 0 0 0 0.0 \n", - "1 Albania 89 132 54 4.9 \n", - "2 Algeria 25 0 14 0.7 \n", - "3 Andorra 245 138 312 12.4 \n", - "4 Angola 217 57 45 5.9 \n", - "5 Antigua & Barbuda 102 128 45 4.9 \n", - "6 Argentina 193 25 221 8.3 \n", - "7 Armenia 21 179 11 3.8 \n", - "8 Australia 261 72 212 10.4 \n", - "9 Austria 279 75 191 9.7 \n", - "10 Azerbaijan 21 46 5 1.3 \n", - "11 Bahamas 122 176 51 6.3 \n", - "12 Bahrain 42 63 7 2.0 \n", - "13 Bangladesh 0 0 0 0.0 \n", - "14 Barbados 143 173 36 6.3 \n", - "15 Belarus 142 373 42 14.4 \n", - "16 Belgium 295 84 212 10.5 \n", - "17 Belize 263 114 8 6.8 \n", - "18 Benin 34 4 13 1.1 \n", - "19 Bhutan 23 0 0 0.4 \n", - "20 Bolivia 167 41 8 3.8 \n", - "21 Bosnia-Herzegovina 76 173 8 4.6 \n", - "22 Botswana 173 35 35 5.4 \n", - "23 Brazil 245 145 16 7.2 \n", - "24 Brunei 31 2 1 0.6 \n", - "25 Bulgaria 231 252 94 10.3 \n", - "26 Burkina Faso 25 7 7 4.3 \n", - "27 Burundi 88 0 0 6.3 \n", - "28 Cote d'Ivoire 37 1 7 4.0 \n", - "29 Cabo Verde 144 56 16 4.0 \n", - ".. ... ... ... ... ... \n", - "163 Suriname 128 178 7 5.6 \n", - "164 Swaziland 90 2 2 4.7 \n", - "165 Sweden 152 60 186 7.2 \n", - "166 Switzerland 185 100 280 10.2 \n", - "167 Syria 5 35 16 1.0 \n", - "168 Tajikistan 2 15 0 0.3 \n", - "169 Thailand 99 258 1 6.4 \n", - "170 Macedonia 106 27 86 3.9 \n", - "171 Timor-Leste 1 1 4 0.1 \n", - "172 Togo 36 2 19 1.3 \n", - "173 Tonga 36 21 5 1.1 \n", - "174 Trinidad & Tobago 197 156 7 6.4 \n", - "175 Tunisia 51 3 20 1.3 \n", - "176 Turkey 51 22 7 1.4 \n", - "177 Turkmenistan 19 71 32 2.2 \n", - "178 Tuvalu 6 41 9 1.0 \n", - "179 Uganda 45 9 0 8.3 \n", - "180 Ukraine 206 237 45 8.9 \n", - "181 United Arab Emirates 16 135 5 2.8 \n", - "182 United Kingdom 219 126 195 10.4 \n", - "183 Tanzania 36 6 1 5.7 \n", - "184 USA 249 158 84 8.7 \n", - "185 Uruguay 115 35 220 6.6 \n", - "186 Uzbekistan 25 101 8 2.4 \n", - "187 Vanuatu 21 18 11 0.9 \n", - "188 Venezuela 333 100 3 7.7 \n", - "189 Vietnam 111 2 1 2.0 \n", - "190 Yemen 6 0 0 0.1 \n", - "191 Zambia 32 19 4 2.5 \n", - "192 Zimbabwe 64 18 4 4.7 \n", - "\n", - " continent \n", - "0 AS \n", - "1 EU \n", - "2 AF \n", - "3 EU \n", - "4 AF \n", - "5 NaN \n", - "6 SA \n", - "7 EU \n", - "8 OC \n", - "9 EU \n", - "10 EU \n", - "11 NaN \n", - "12 AS \n", - "13 AS \n", - "14 NaN \n", - "15 EU \n", - "16 EU \n", - "17 NaN \n", - "18 AF \n", - "19 AS \n", - "20 SA \n", - "21 EU \n", - "22 AF \n", - "23 SA \n", - "24 AS \n", - "25 EU \n", - "26 AF \n", - "27 AF \n", - "28 AF \n", - "29 AF \n", - ".. ... \n", - "163 SA \n", - "164 AF \n", - "165 EU \n", - "166 EU \n", - "167 AS \n", - "168 AS \n", - "169 AS \n", - "170 EU \n", - "171 AS \n", - "172 AF \n", - "173 OC \n", - "174 NaN \n", - "175 AF \n", - "176 AS \n", - "177 AS \n", - "178 OC \n", - "179 AF \n", - "180 EU \n", - "181 AS \n", - "182 EU \n", - "183 AF \n", - "184 NaN \n", - "185 SA \n", - "186 AS \n", - "187 OC \n", - "188 SA \n", - "189 AS \n", - "190 AS \n", - "191 AF \n", - "192 AF \n", - "\n", - "[193 rows x 6 columns]" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Rename one or more columns in a single output using value mapping.\n", - "drinks.rename(columns={'beer_servings':'beer', 'wine_servings':'wine','spirit_servings':'spirit'})" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
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" - ], - "text/plain": [ - " country beer_servings spirit_servings wine_servings \\\n", - "0 Afghanistan 0 0 0 \n", - "1 Albania 89 132 54 \n", - "\n", - " total_litres_of_pure_alcohol continent \n", - "0 0.0 AS \n", - "1 4.9 EU " - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drinks.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "# Rename one or more columns in the original DataFrame.\n", - "drinks.rename(columns={'beer_servings':'beer', 'wine_servings':'wine'\n", - " ,'spirit_servings':'spirit','total_litres_of_pure_alcohol':'liters'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
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" - ], - "text/plain": [ - " country beer spirit wine liters continent\n", - "0 Afghanistan 0 0 0 0.0 AS\n", - "1 Albania 89 132 54 4.9 EU" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drinks.head(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Easy Column Operations**
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countrybeerspiritwineliterscontinentservingsmL
0Afghanistan0000.0AS00.0
1Albania89132544.9EU2754900.0
2Algeria250140.7AF39700.0
3Andorra24513831212.4EU69512400.0
4Angola21757455.9AF3195900.0
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" - ], - "text/plain": [ - " country beer spirit wine liters continent servings mL\n", - "0 Afghanistan 0 0 0 0.0 AS 0 0.0\n", - "1 Albania 89 132 54 4.9 EU 275 4900.0\n", - "2 Algeria 25 0 14 0.7 AF 39 700.0\n", - "3 Andorra 245 138 312 12.4 EU 695 12400.0\n", - "4 Angola 217 57 45 5.9 AF 319 5900.0" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Add a new column as a function of existing columns.\n", - "drinks['servings'] = drinks.beer + drinks.spirit + drinks.wine\n", - "drinks['mL'] = drinks.liters * 1000\n", - "\n", - "drinks.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Removing Columns**" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
5Antigua & Barbuda102128454.9NaN
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
11Bahamas122176516.3NaN
12Bahrain426372.0AS
13Bangladesh0000.0AS
14Barbados143173366.3NaN
15Belarus1423734214.4EU
16Belgium2958421210.5EU
17Belize26311486.8NaN
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
.....................
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
174Trinidad & Tobago19715676.4NaN
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
184USA249158848.7NaN
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
\n", - "

193 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " country beer spirit wine liters continent\n", - "0 Afghanistan 0 0 0 0.0 AS\n", - "1 Albania 89 132 54 4.9 EU\n", - "2 Algeria 25 0 14 0.7 AF\n", - "3 Andorra 245 138 312 12.4 EU\n", - "4 Angola 217 57 45 5.9 AF\n", - "5 Antigua & Barbuda 102 128 45 4.9 NaN\n", - "6 Argentina 193 25 221 8.3 SA\n", - "7 Armenia 21 179 11 3.8 EU\n", - "8 Australia 261 72 212 10.4 OC\n", - "9 Austria 279 75 191 9.7 EU\n", - "10 Azerbaijan 21 46 5 1.3 EU\n", - "11 Bahamas 122 176 51 6.3 NaN\n", - "12 Bahrain 42 63 7 2.0 AS\n", - "13 Bangladesh 0 0 0 0.0 AS\n", - "14 Barbados 143 173 36 6.3 NaN\n", - "15 Belarus 142 373 42 14.4 EU\n", - "16 Belgium 295 84 212 10.5 EU\n", - "17 Belize 263 114 8 6.8 NaN\n", - "18 Benin 34 4 13 1.1 AF\n", - "19 Bhutan 23 0 0 0.4 AS\n", - "20 Bolivia 167 41 8 3.8 SA\n", - "21 Bosnia-Herzegovina 76 173 8 4.6 EU\n", - "22 Botswana 173 35 35 5.4 AF\n", - "23 Brazil 245 145 16 7.2 SA\n", - "24 Brunei 31 2 1 0.6 AS\n", - "25 Bulgaria 231 252 94 10.3 EU\n", - "26 Burkina Faso 25 7 7 4.3 AF\n", - "27 Burundi 88 0 0 6.3 AF\n", - "28 Cote d'Ivoire 37 1 7 4.0 AF\n", - "29 Cabo Verde 144 56 16 4.0 AF\n", - ".. ... ... ... ... ... ...\n", - "163 Suriname 128 178 7 5.6 SA\n", - "164 Swaziland 90 2 2 4.7 AF\n", - "165 Sweden 152 60 186 7.2 EU\n", - "166 Switzerland 185 100 280 10.2 EU\n", - "167 Syria 5 35 16 1.0 AS\n", - "168 Tajikistan 2 15 0 0.3 AS\n", - "169 Thailand 99 258 1 6.4 AS\n", - "170 Macedonia 106 27 86 3.9 EU\n", - "171 Timor-Leste 1 1 4 0.1 AS\n", - "172 Togo 36 2 19 1.3 AF\n", - "173 Tonga 36 21 5 1.1 OC\n", - "174 Trinidad & Tobago 197 156 7 6.4 NaN\n", - "175 Tunisia 51 3 20 1.3 AF\n", - "176 Turkey 51 22 7 1.4 AS\n", - "177 Turkmenistan 19 71 32 2.2 AS\n", - "178 Tuvalu 6 41 9 1.0 OC\n", - "179 Uganda 45 9 0 8.3 AF\n", - "180 Ukraine 206 237 45 8.9 EU\n", - "181 United Arab Emirates 16 135 5 2.8 AS\n", - "182 United Kingdom 219 126 195 10.4 EU\n", - "183 Tanzania 36 6 1 5.7 AF\n", - "184 USA 249 158 84 8.7 NaN\n", - "185 Uruguay 115 35 220 6.6 SA\n", - "186 Uzbekistan 25 101 8 2.4 AS\n", - "187 Vanuatu 21 18 11 0.9 OC\n", - "188 Venezuela 333 100 3 7.7 SA\n", - "189 Vietnam 111 2 1 2.0 AS\n", - "190 Yemen 6 0 0 0.1 AS\n", - "191 Zambia 32 19 4 2.5 AF\n", - "192 Zimbabwe 64 18 4 4.7 AF\n", - "\n", - "[193 rows x 6 columns]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Drop multiple columns.\n", - "drinks.drop(['mL', 'servings'], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeerspiritwineliterscontinentservingsmL
0Afghanistan0000.0AS00.0
1Albania89132544.9EU2754900.0
2Algeria250140.7AF39700.0
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" - ], - "text/plain": [ - " country beer spirit wine liters continent servings mL\n", - "0 Afghanistan 0 0 0 0.0 AS 0 0.0\n", - "1 Albania 89 132 54 4.9 EU 275 4900.0\n", - "2 Algeria 25 0 14 0.7 AF 39 700.0" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drinks.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop on the original DataFrame rather than returning a new one.\n", - "drinks.drop(['mL', 'servings'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
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" - ], - "text/plain": [ - " country beer spirit wine liters continent\n", - "0 Afghanistan 0 0 0 0.0 AS\n", - "1 Albania 89 132 54 4.9 EU\n", - "2 Algeria 25 0 14 0.7 AF" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drinks.head(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Handling Missing Values\n", - "\n", - "\n", - "\n", - "\n", - "- **Objective:** Know how to handle null and missing values.\n", - "\n", - "Sometimes, values will be missing from the source data or as a byproduct of manipulations. It is very important to detect missing data. Missing data can:\n", - "\n", - "- Make the entire row ineligible to be training data for a model.\n", - "- Hint at data-collection errors.\n", - "- Indicate improper conversion or manipulation.\n", - "- Actually not be missing — it sometimes means \"zero,\" \"false,\" \"not applicable,\" or \"entered an empty string.\"\n", - "\n", - "For example, a `.csv` file might have a missing value in some data fields:\n", - "\n", - "```\n", - "tool_name,material,cost\n", - "hammer,wood,8\n", - "chainsaw,,\n", - "wrench,metal,5\n", - "```\n", - "\n", - "When this data is imported, \"null\" values will be stored in the second row (in the \"material\" and \"cost\" columns).\n", - "\n", - "> In Pandas, a \"null\" value is either `None` or `np.NaN` (Not a Number). Many fixed-size numeric datatypes (such as integers) do not have a way of representing `np.NaN`. So, numeric columns will be promoted to floating-point datatypes that do support it. For example, when importing the `.csv` file above:\n", - "\n", - "> - **For the second row:** `None` will be stored in the \"material\" column and `np.NaN` will be stored in the \"cost\" column. The entire \"cost\" column (stored as a single `ndarray`) must be stored as floating-point values to accommodate the `np.NaN`, even though an integer `8` is in the first row." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "AF 53\n", - "EU 45\n", - "AS 44\n", - "OC 16\n", - "SA 12\n", - "Name: continent, dtype: int64" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Missing values are usually excluded in calculations by default.\n", - "drinks.continent.value_counts() # Excludes missing values in the calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AF 53\n", - "EU 45\n", - "AS 44\n", - "NaN 23\n", - "OC 16\n", - "SA 12\n", - "Name: continent, dtype: int64" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Includes missing values\n", - "drinks.continent.value_counts(dropna=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "5 True\n", - "6 False\n", - "7 False\n", - "8 False\n", - "9 False\n", - "10 False\n", - "11 True\n", - "12 False\n", - "13 False\n", - "14 True\n", - "15 False\n", - "16 False\n", - "17 True\n", - "18 False\n", - "19 False\n", - "20 False\n", - "21 False\n", - "22 False\n", - "23 False\n", - "24 False\n", - "25 False\n", - "26 False\n", - "27 False\n", - "28 False\n", - "29 False\n", - " ... \n", - "163 False\n", - "164 False\n", - "165 False\n", - "166 False\n", - "167 False\n", - "168 False\n", - "169 False\n", - "170 False\n", - "171 False\n", - "172 False\n", - "173 False\n", - "174 True\n", - "175 False\n", - "176 False\n", - "177 False\n", - "178 False\n", - "179 False\n", - "180 False\n", - "181 False\n", - "182 False\n", - "183 False\n", - "184 True\n", - "185 False\n", - "186 False\n", - "187 False\n", - "188 False\n", - "189 False\n", - "190 False\n", - "191 False\n", - "192 False\n", - "Name: continent, Length: 193, dtype: bool" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Count the missing values — sum() works because True is 1 and False is 0.\n", - "drinks.continent.isnull()" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "23" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Find missing values in a Series.\n", - "# True if missing, False if not missing\n", - "drinks.continent.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeerspiritwineliterscontinent
5Antigua & Barbuda102128454.9NaN
11Bahamas122176516.3NaN
14Barbados143173366.3NaN
17Belize26311486.8NaN
32Canada2401221008.2NaN
41Costa Rica14987114.4NaN
43Cuba9313754.2NaN
50Dominica52286266.6NaN
51Dominican Republic19314796.2NaN
54El Salvador526922.2NaN
68Grenada1994382811.9NaN
69Guatemala536922.2NaN
73Haiti132615.9NaN
74Honduras699823.0NaN
84Jamaica829793.4NaN
109Mexico2386855.5NaN
122Nicaragua7811813.5NaN
130Panama285104187.2NaN
143St. Kitts & Nevis194205327.7NaN
144St. Lucia1713157110.1NaN
145St. Vincent & the Grenadines120221116.3NaN
174Trinidad & Tobago19715676.4NaN
184USA249158848.7NaN
\n", - "
" - ], - "text/plain": [ - " country beer spirit wine liters continent\n", - "5 Antigua & Barbuda 102 128 45 4.9 NaN\n", - "11 Bahamas 122 176 51 6.3 NaN\n", - "14 Barbados 143 173 36 6.3 NaN\n", - "17 Belize 263 114 8 6.8 NaN\n", - "32 Canada 240 122 100 8.2 NaN\n", - "41 Costa Rica 149 87 11 4.4 NaN\n", - "43 Cuba 93 137 5 4.2 NaN\n", - "50 Dominica 52 286 26 6.6 NaN\n", - "51 Dominican Republic 193 147 9 6.2 NaN\n", - "54 El Salvador 52 69 2 2.2 NaN\n", - "68 Grenada 199 438 28 11.9 NaN\n", - "69 Guatemala 53 69 2 2.2 NaN\n", - "73 Haiti 1 326 1 5.9 NaN\n", - "74 Honduras 69 98 2 3.0 NaN\n", - "84 Jamaica 82 97 9 3.4 NaN\n", - "109 Mexico 238 68 5 5.5 NaN\n", - "122 Nicaragua 78 118 1 3.5 NaN\n", - "130 Panama 285 104 18 7.2 NaN\n", - "143 St. Kitts & Nevis 194 205 32 7.7 NaN\n", - "144 St. Lucia 171 315 71 10.1 NaN\n", - "145 St. Vincent & the Grenadines 120 221 11 6.3 NaN\n", - "174 Trinidad & Tobago 197 156 7 6.4 NaN\n", - "184 USA 249 158 84 8.7 NaN" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Only show rows where continent is not missing.\n", - "drinks[drinks.continent.isnull()]\n", - "#drinks[drinks.continent.notnull()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Find missing values in a `DataFrame`.**" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "country 0\n", - "beer 0\n", - "spirit 0\n", - "wine 0\n", - "liters 0\n", - "continent 23\n", - "dtype: int64\n" - ] - }, - { - "data": { - "image/png": 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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
12Bahrain426372.0AS
13Bangladesh0000.0AS
15Belarus1423734214.4EU
16Belgium2958421210.5EU
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
30Cambodia576512.2AS
31Cameroon147145.8AF
33Central African Republic17211.8AF
34Chad15110.4AF
.....................
161Sri Lanka1610402.2AS
162Sudan81301.7AF
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
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170 rows × 6 columns

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" - ], - "text/plain": [ - " country beer spirit wine liters continent\n", - "0 Afghanistan 0 0 0 0.0 AS\n", - "1 Albania 89 132 54 4.9 EU\n", - "2 Algeria 25 0 14 0.7 AF\n", - "3 Andorra 245 138 312 12.4 EU\n", - "4 Angola 217 57 45 5.9 AF\n", - "6 Argentina 193 25 221 8.3 SA\n", - "7 Armenia 21 179 11 3.8 EU\n", - "8 Australia 261 72 212 10.4 OC\n", - "9 Austria 279 75 191 9.7 EU\n", - "10 Azerbaijan 21 46 5 1.3 EU\n", - "12 Bahrain 42 63 7 2.0 AS\n", - "13 Bangladesh 0 0 0 0.0 AS\n", - "15 Belarus 142 373 42 14.4 EU\n", - "16 Belgium 295 84 212 10.5 EU\n", - "18 Benin 34 4 13 1.1 AF\n", - "19 Bhutan 23 0 0 0.4 AS\n", - "20 Bolivia 167 41 8 3.8 SA\n", - "21 Bosnia-Herzegovina 76 173 8 4.6 EU\n", - "22 Botswana 173 35 35 5.4 AF\n", - "23 Brazil 245 145 16 7.2 SA\n", - "24 Brunei 31 2 1 0.6 AS\n", - "25 Bulgaria 231 252 94 10.3 EU\n", - "26 Burkina Faso 25 7 7 4.3 AF\n", - "27 Burundi 88 0 0 6.3 AF\n", - "28 Cote d'Ivoire 37 1 7 4.0 AF\n", - "29 Cabo Verde 144 56 16 4.0 AF\n", - "30 Cambodia 57 65 1 2.2 AS\n", - "31 Cameroon 147 1 4 5.8 AF\n", - "33 Central African Republic 17 2 1 1.8 AF\n", - "34 Chad 15 1 1 0.4 AF\n", - ".. ... ... ... ... ... ...\n", - "161 Sri Lanka 16 104 0 2.2 AS\n", - "162 Sudan 8 13 0 1.7 AF\n", - "163 Suriname 128 178 7 5.6 SA\n", - "164 Swaziland 90 2 2 4.7 AF\n", - "165 Sweden 152 60 186 7.2 EU\n", - "166 Switzerland 185 100 280 10.2 EU\n", - "167 Syria 5 35 16 1.0 AS\n", - "168 Tajikistan 2 15 0 0.3 AS\n", - "169 Thailand 99 258 1 6.4 AS\n", - "170 Macedonia 106 27 86 3.9 EU\n", - "171 Timor-Leste 1 1 4 0.1 AS\n", - "172 Togo 36 2 19 1.3 AF\n", - "173 Tonga 36 21 5 1.1 OC\n", - "175 Tunisia 51 3 20 1.3 AF\n", - "176 Turkey 51 22 7 1.4 AS\n", - "177 Turkmenistan 19 71 32 2.2 AS\n", - "178 Tuvalu 6 41 9 1.0 OC\n", - "179 Uganda 45 9 0 8.3 AF\n", - "180 Ukraine 206 237 45 8.9 EU\n", - "181 United Arab Emirates 16 135 5 2.8 AS\n", - "182 United Kingdom 219 126 195 10.4 EU\n", - "183 Tanzania 36 6 1 5.7 AF\n", - "185 Uruguay 115 35 220 6.6 SA\n", - "186 Uzbekistan 25 101 8 2.4 AS\n", - "187 Vanuatu 21 18 11 0.9 OC\n", - "188 Venezuela 333 100 3 7.7 SA\n", - "189 Vietnam 111 2 1 2.0 AS\n", - "190 Yemen 6 0 0 0.1 AS\n", - "191 Zambia 32 19 4 2.5 AF\n", - "192 Zimbabwe 64 18 4 4.7 AF\n", - "\n", - "[170 rows x 6 columns]" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Drop a row if ANY values are missing from any column — can be dangerous!\n", - "drinks.dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
5Antigua & Barbuda102128454.9NaN
6Argentina193252218.3SA
7Armenia21179113.8EU
8Australia2617221210.4OC
9Austria279751919.7EU
10Azerbaijan214651.3EU
11Bahamas122176516.3NaN
12Bahrain426372.0AS
13Bangladesh0000.0AS
14Barbados143173366.3NaN
15Belarus1423734214.4EU
16Belgium2958421210.5EU
17Belize26311486.8NaN
18Benin344131.1AF
19Bhutan23000.4AS
20Bolivia1674183.8SA
21Bosnia-Herzegovina7617384.6EU
22Botswana17335355.4AF
23Brazil245145167.2SA
24Brunei31210.6AS
25Bulgaria2312529410.3EU
26Burkina Faso25774.3AF
27Burundi88006.3AF
28Cote d'Ivoire37174.0AF
29Cabo Verde14456164.0AF
.....................
163Suriname12817875.6SA
164Swaziland90224.7AF
165Sweden152601867.2EU
166Switzerland18510028010.2EU
167Syria535161.0AS
168Tajikistan21500.3AS
169Thailand9925816.4AS
170Macedonia10627863.9EU
171Timor-Leste1140.1AS
172Togo362191.3AF
173Tonga362151.1OC
174Trinidad & Tobago19715676.4NaN
175Tunisia513201.3AF
176Turkey512271.4AS
177Turkmenistan1971322.2AS
178Tuvalu64191.0OC
179Uganda45908.3AF
180Ukraine206237458.9EU
181United Arab Emirates1613552.8AS
182United Kingdom21912619510.4EU
183Tanzania36615.7AF
184USA249158848.7NaN
185Uruguay115352206.6SA
186Uzbekistan2510182.4AS
187Vanuatu2118110.9OC
188Venezuela33310037.7SA
189Vietnam111212.0AS
190Yemen6000.1AS
191Zambia321942.5AF
192Zimbabwe641844.7AF
\n", - "

193 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " country beer spirit wine liters continent\n", - "0 Afghanistan 0 0 0 0.0 AS\n", - "1 Albania 89 132 54 4.9 EU\n", - "2 Algeria 25 0 14 0.7 AF\n", - "3 Andorra 245 138 312 12.4 EU\n", - "4 Angola 217 57 45 5.9 AF\n", - "5 Antigua & Barbuda 102 128 45 4.9 NaN\n", - "6 Argentina 193 25 221 8.3 SA\n", - "7 Armenia 21 179 11 3.8 EU\n", - "8 Australia 261 72 212 10.4 OC\n", - "9 Austria 279 75 191 9.7 EU\n", - "10 Azerbaijan 21 46 5 1.3 EU\n", - "11 Bahamas 122 176 51 6.3 NaN\n", - "12 Bahrain 42 63 7 2.0 AS\n", - "13 Bangladesh 0 0 0 0.0 AS\n", - "14 Barbados 143 173 36 6.3 NaN\n", - "15 Belarus 142 373 42 14.4 EU\n", - "16 Belgium 295 84 212 10.5 EU\n", - "17 Belize 263 114 8 6.8 NaN\n", - "18 Benin 34 4 13 1.1 AF\n", - "19 Bhutan 23 0 0 0.4 AS\n", - "20 Bolivia 167 41 8 3.8 SA\n", - "21 Bosnia-Herzegovina 76 173 8 4.6 EU\n", - "22 Botswana 173 35 35 5.4 AF\n", - "23 Brazil 245 145 16 7.2 SA\n", - "24 Brunei 31 2 1 0.6 AS\n", - "25 Bulgaria 231 252 94 10.3 EU\n", - "26 Burkina Faso 25 7 7 4.3 AF\n", - "27 Burundi 88 0 0 6.3 AF\n", - "28 Cote d'Ivoire 37 1 7 4.0 AF\n", - "29 Cabo Verde 144 56 16 4.0 AF\n", - ".. ... ... ... ... ... ...\n", - "163 Suriname 128 178 7 5.6 SA\n", - "164 Swaziland 90 2 2 4.7 AF\n", - "165 Sweden 152 60 186 7.2 EU\n", - "166 Switzerland 185 100 280 10.2 EU\n", - "167 Syria 5 35 16 1.0 AS\n", - "168 Tajikistan 2 15 0 0.3 AS\n", - "169 Thailand 99 258 1 6.4 AS\n", - "170 Macedonia 106 27 86 3.9 EU\n", - "171 Timor-Leste 1 1 4 0.1 AS\n", - "172 Togo 36 2 19 1.3 AF\n", - "173 Tonga 36 21 5 1.1 OC\n", - "174 Trinidad & Tobago 197 156 7 6.4 NaN\n", - "175 Tunisia 51 3 20 1.3 AF\n", - "176 Turkey 51 22 7 1.4 AS\n", - "177 Turkmenistan 19 71 32 2.2 AS\n", - "178 Tuvalu 6 41 9 1.0 OC\n", - "179 Uganda 45 9 0 8.3 AF\n", - "180 Ukraine 206 237 45 8.9 EU\n", - "181 United Arab Emirates 16 135 5 2.8 AS\n", - "182 United Kingdom 219 126 195 10.4 EU\n", - "183 Tanzania 36 6 1 5.7 AF\n", - "184 USA 249 158 84 8.7 NaN\n", - "185 Uruguay 115 35 220 6.6 SA\n", - "186 Uzbekistan 25 101 8 2.4 AS\n", - "187 Vanuatu 21 18 11 0.9 OC\n", - "188 Venezuela 333 100 3 7.7 SA\n", - "189 Vietnam 111 2 1 2.0 AS\n", - "190 Yemen 6 0 0 0.1 AS\n", - "191 Zambia 32 19 4 2.5 AF\n", - "192 Zimbabwe 64 18 4 4.7 AF\n", - "\n", - "[193 rows x 6 columns]" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Drop a row only if ALL values are missing.\n", - "drinks.dropna(how='all')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Filling Missing Values**
\n", - "You may have noticed that the continent North America (NA) does not appear in the `continent` column. Pandas read in the original data and saw \"NA\", thought it was a missing value, and converted it to a `NaN`, missing value." - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 AS\n", - "1 EU\n", - "2 AF\n", - "3 EU\n", - "4 AF\n", - "5 NA\n", - "6 SA\n", - "7 EU\n", - "8 OC\n", - "9 EU\n", - "10 EU\n", - "11 NA\n", - "12 AS\n", - "13 AS\n", - "14 NA\n", - "15 EU\n", - "16 EU\n", - "17 NA\n", - "18 AF\n", - "19 AS\n", - "20 SA\n", - "21 EU\n", - "22 AF\n", - "23 SA\n", - "24 AS\n", - "25 EU\n", - "26 AF\n", - "27 AF\n", - "28 AF\n", - "29 AF\n", - " ..\n", - "163 SA\n", - "164 AF\n", - "165 EU\n", - "166 EU\n", - "167 AS\n", - "168 AS\n", - "169 AS\n", - "170 EU\n", - "171 AS\n", - "172 AF\n", - "173 OC\n", - "174 NA\n", - "175 AF\n", - "176 AS\n", - "177 AS\n", - "178 OC\n", - "179 AF\n", - "180 EU\n", - "181 AS\n", - "182 EU\n", - "183 AF\n", - "184 NA\n", - "185 SA\n", - "186 AS\n", - "187 OC\n", - "188 SA\n", - "189 AS\n", - "190 AS\n", - "191 AF\n", - "192 AF\n", - "Name: continent, Length: 193, dtype: object" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Fill in missing values with \"NA\" — this is dangerous to do without manually verifying them!\n", - "drinks.continent.fillna(value='NA')" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "# Modifies \"drinks\" in-place\n", - "drinks.continent.fillna(value='NA', inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Exercise 3\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
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" - ], - "text/plain": [ - " City Colors Reported Shape Reported State Time\n", - "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", - "1 Willingboro NaN OTHER NJ 6/30/1930 20:00" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Read ufo.csv into a DataFrame called \"ufo\".\n", - "ufo= pd.read_csv('./data/ufo.csv')\n", - "ufo.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(80543, 5)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the shape of the DataFrame.\n", - "ufo.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "# What are the three most common colors reported?\n" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# Rename any columns with spaces so that they don't contain spaces.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "# For reports in VA, what's the most common city?\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# Print a DataFrame containing only reports from Arlington, VA.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# Count the number of missing values in each column.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the original size: 80543\n" - ] - }, - { - "data": { - "text/plain": [ - "15510" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# How many rows remain if you drop all rows with any missing values?\n", - "print('the original size:', ufo.shape[0])\n", - "ufo.dropna().shape[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Split-Apply-Combine\n", - "\n", - "\n", - "\n", - "Split-apply-combine is a pattern for analyzing data. Suppose we want to find mean beer consumption per country. Then:\n", - "\n", - "- **Split:** We group data by continent.\n", - "- **Apply:** For each group, we apply the `mean()` function to find the average beer consumption.\n", - "- **Combine:** We now combine the continent names with the `mean()`s to produce a summary of our findings." - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeerspiritwineliterscontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
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" - ], - "text/plain": [ - " country beer spirit wine liters continent\n", - "0 Afghanistan 0 0 0 0.0 AS\n", - "1 Albania 89 132 54 4.9 EU\n", - "2 Algeria 25 0 14 0.7 AF" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drinks.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "continent\n", - "AF 61.471698\n", - "AS 37.045455\n", - "EU 193.777778\n", - "NA 145.434783\n", - "OC 89.687500\n", - "SA 175.083333\n", - "Name: beer, dtype: float64" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For each continent, calculate the mean beer servings.\n", - "drinks.groupby('continent').beer.mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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beerspiritwineliters
continent
AF61.47169816.33962316.2641513.007547
AS37.04545560.8409099.0681822.170455
EU193.777778132.555556142.2222228.617778
NA145.434783165.73913024.5217395.995652
OC89.68750058.43750035.6250003.381250
SA175.083333114.75000062.4166676.308333
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" - ], - "text/plain": [ - " beer spirit wine liters\n", - "continent \n", - "AF 61.471698 16.339623 16.264151 3.007547\n", - "AS 37.045455 60.840909 9.068182 2.170455\n", - "EU 193.777778 132.555556 142.222222 8.617778\n", - "NA 145.434783 165.739130 24.521739 5.995652\n", - "OC 89.687500 58.437500 35.625000 3.381250\n", - "SA 175.083333 114.750000 62.416667 6.308333" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For each continent, calculate the mean of all numeric columns.\n", - "drinks.groupby('continent').mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countmeanstdmin25%50%75%max
continent
AF53.061.47169880.5578160.015.0032.076.00376.0
AS44.037.04545549.4697250.04.2517.560.50247.0
EU45.0193.77777899.6315690.0127.00219.0270.00361.0
NA23.0145.43478379.6211631.080.00143.0198.00285.0
OC16.089.68750096.6414120.021.0052.5125.75306.0
SA12.0175.08333365.24284593.0129.50162.5198.00333.0
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" - ], - "text/plain": [ - " count mean std min 25% 50% 75% max\n", - "continent \n", - "AF 53.0 61.471698 80.557816 0.0 15.00 32.0 76.00 376.0\n", - "AS 44.0 37.045455 49.469725 0.0 4.25 17.5 60.50 247.0\n", - "EU 45.0 193.777778 99.631569 0.0 127.00 219.0 270.00 361.0\n", - "NA 23.0 145.434783 79.621163 1.0 80.00 143.0 198.00 285.0\n", - "OC 16.0 89.687500 96.641412 0.0 21.00 52.5 125.75 306.0\n", - "SA 12.0 175.083333 65.242845 93.0 129.50 162.5 198.00 333.0" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For each continent, describe beer servings.\n", - "drinks.groupby('continent').beer.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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beerspirit...wineliters
countmeanstdmin25%50%75%maxcountmean...75%maxcountmeanstdmin25%50%75%max
continent
AF53.061.47169880.5578160.015.0032.076.00376.053.016.339623...13.00233.053.03.0075472.6475570.00.702.304.7009.1
AS44.037.04545549.4697250.04.2517.560.50247.044.060.840909...8.00123.044.02.1704552.7702390.00.101.202.42511.5
EU45.0193.77777899.6315690.0127.00219.0270.00361.045.0132.555556...195.00370.045.08.6177783.3584550.06.6010.0010.90014.4
NA23.0145.43478379.6211631.080.00143.0198.00285.023.0165.739130...34.00100.023.05.9956522.4093532.24.306.307.00011.9
OC16.089.68750096.6414120.021.0052.5125.75306.016.058.437500...23.25212.016.03.3812503.3456880.01.001.756.15010.4
SA12.0175.08333365.24284593.0129.50162.5198.00333.012.0114.750000...98.50221.012.06.3083331.5311663.85.256.857.3758.3
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6 rows × 32 columns

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" - ], - "text/plain": [ - " beer \\\n", - " count mean std min 25% 50% 75% max \n", - "continent \n", - "AF 53.0 61.471698 80.557816 0.0 15.00 32.0 76.00 376.0 \n", - "AS 44.0 37.045455 49.469725 0.0 4.25 17.5 60.50 247.0 \n", - "EU 45.0 193.777778 99.631569 0.0 127.00 219.0 270.00 361.0 \n", - "NA 23.0 145.434783 79.621163 1.0 80.00 143.0 198.00 285.0 \n", - "OC 16.0 89.687500 96.641412 0.0 21.00 52.5 125.75 306.0 \n", - "SA 12.0 175.083333 65.242845 93.0 129.50 162.5 198.00 333.0 \n", - "\n", - " spirit ... wine liters \\\n", - " count mean ... 75% max count mean std \n", - "continent ... \n", - "AF 53.0 16.339623 ... 13.00 233.0 53.0 3.007547 2.647557 \n", - "AS 44.0 60.840909 ... 8.00 123.0 44.0 2.170455 2.770239 \n", - "EU 45.0 132.555556 ... 195.00 370.0 45.0 8.617778 3.358455 \n", - "NA 23.0 165.739130 ... 34.00 100.0 23.0 5.995652 2.409353 \n", - "OC 16.0 58.437500 ... 23.25 212.0 16.0 3.381250 3.345688 \n", - "SA 12.0 114.750000 ... 98.50 221.0 12.0 6.308333 1.531166 \n", - "\n", - " \n", - " min 25% 50% 75% max \n", - "continent \n", - "AF 0.0 0.70 2.30 4.700 9.1 \n", - "AS 0.0 0.10 1.20 2.425 11.5 \n", - "EU 0.0 6.60 10.00 10.900 14.4 \n", - "NA 2.2 4.30 6.30 7.000 11.9 \n", - "OC 0.0 1.00 1.75 6.150 10.4 \n", - "SA 3.8 5.25 6.85 7.375 8.3 \n", - "\n", - "[6 rows x 32 columns]" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For each continent, describe all numeric columns.\n", - "drinks.groupby('continent').describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AF 53\n", - "EU 45\n", - "AS 44\n", - "NA 23\n", - "OC 16\n", - "SA 12\n", - "Name: continent, dtype: int64\n" - ] - } - ], - "source": [ - "# For each continent, count the number of rows.\n", - "print((drinks.continent.value_counts())) # should be the same" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Exercise 4 : TAKE HOME\n", - "\n", - "\n", - "\n", - "Use the \"users\" `DataFrame` or \"users\" file in the Data folder to complete the following." - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idagegenderoccupationzip_codeboolageis_young
0124Mtechnician85711True0
1253Fother94043False0
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" - ], - "text/plain": [ - " user_id age gender occupation zip_code boolage is_young\n", - "0 1 24 M technician 85711 True 0\n", - "1 2 53 F other 94043 False 0" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "users.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [], - "source": [ - "# For each occupation in \"users\", count the number of occurrences.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "# For each occupation, calculate the mean age.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# For each occupation, calculate the minimum and maximum ages.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "# For each combination of occupation and gender, calculate the mean age.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Selecting Multiple Columns and Filtering Rows" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
3AbileneNaNDISKKS6/1/1931 13:00
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00
\n", - "
" - ], - "text/plain": [ - " City Colors Reported Shape Reported State Time\n", - "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", - "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", - "2 Holyoke NaN OVAL CO 2/15/1931 14:00\n", - "3 Abilene NaN DISK KS 6/1/1931 13:00\n", - "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ufo.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CityState
0IthacaNY
1WillingboroNJ
2HolyokeCO
3AbileneKS
4New York Worlds FairNY
\n", - "
" - ], - "text/plain": [ - " City State\n", - "0 Ithaca NY\n", - "1 Willingboro NJ\n", - "2 Holyoke CO\n", - "3 Abilene KS\n", - "4 New York Worlds Fair NY" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Or, combine into a single step (this is a Python list inside of the Python index operator!).\n", - "ufo[['City', 'State']].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Use `loc` to select columns by name.**" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Ithaca\n", - "1 Willingboro\n", - "2 Holyoke\n", - "3 Abilene\n", - "4 New York Worlds Fair\n", - "5 Valley City\n", - "6 Crater Lake\n", - "7 Alma\n", - "8 Eklutna\n", - "9 Hubbard\n", - "10 Fontana\n", - "11 Waterloo\n", - "12 Belton\n", - "13 Keokuk\n", - "14 Ludington\n", - "15 Forest Home\n", - "16 Los Angeles\n", - "17 Hapeville\n", - "18 Oneida\n", - "19 Bering Sea\n", - "20 Nebraska\n", - "21 NaN\n", - "22 NaN\n", - "23 Owensboro\n", - "24 Wilderness\n", - "25 San Diego\n", - "26 Wilderness\n", - "27 Clovis\n", - "28 Los Alamos\n", - "29 Ft. Duschene\n", - " ... \n", - "80513 Manahawkin\n", - "80514 New Bedford\n", - "80515 Woodbridge\n", - "80516 Glendale\n", - "80517 Laconia\n", - "80518 Langhorne\n", - "80519 Glen Ellyn\n", - "80520 Waynesburg\n", - "80521 Canal Winchester\n", - "80522 Lawrence\n", - "80523 Ellicott City\n", - "80524 Olympia\n", - "80525 Iowa City\n", - "80526 Marshfield\n", - "80527 Seattle\n", - "80528 North Royalton\n", - "80529 Deer Park\n", - "80530 Glen St. Mary\n", - "80531 Abingdon\n", - "80532 Lawrence\n", - "80533 Melbourne\n", - "80534 Burleson\n", - "80535 Elizabethtown\n", - "80536 Wyoming\n", - "80537 Neligh\n", - "80538 Neligh\n", - "80539 Uhrichsville\n", - "80540 Tucson\n", - "80541 Orland park\n", - "80542 Loughman\n", - "Name: City, Length: 80543, dtype: object" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# \"loc\" locates the values from the first parameter (colon means \"all rows\"), and the column \"City\".\n", - "ufo.loc[:, 'City'] " - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CityState
0IthacaNY
1WillingboroNJ
2HolyokeCO
3AbileneKS
4New York Worlds FairNY
5Valley CityND
6Crater LakeCA
7AlmaMI
8EklutnaAK
9HubbardOR
10FontanaCA
11WaterlooAL
12BeltonSC
13KeokukIA
14LudingtonMI
15Forest HomeCA
16Los AngelesCA
17HapevilleGA
18OneidaTN
19Bering SeaAK
20NebraskaNE
21NaNLA
22NaNLA
23OwensboroKY
24WildernessWV
25San DiegoCA
26WildernessWV
27ClovisNM
28Los AlamosNM
29Ft. DuscheneUT
.........
80513ManahawkinNJ
80514New BedfordMA
80515WoodbridgeVA
80516GlendaleCA
80517LaconiaNH
80518LanghornePA
80519Glen EllynIL
80520WaynesburgPA
80521Canal WinchesterOH
80522LawrenceMA
80523Ellicott CityMD
80524OlympiaWA
80525Iowa CityIA
80526MarshfieldMA
80527SeattleWA
80528North RoyaltonOH
80529Deer ParkWA
80530Glen St. MaryFL
80531AbingdonVA
80532LawrenceMA
80533MelbourneIA
80534BurlesonTX
80535ElizabethtownKY
80536WyomingPA
80537NelighNE
80538NelighNE
80539UhrichsvilleOH
80540TucsonAZ
80541Orland parkIL
80542LoughmanFL
\n", - "

80543 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " City State\n", - "0 Ithaca NY\n", - "1 Willingboro NJ\n", - "2 Holyoke CO\n", - "3 Abilene KS\n", - "4 New York Worlds Fair NY\n", - "5 Valley City ND\n", - "6 Crater Lake CA\n", - "7 Alma MI\n", - "8 Eklutna AK\n", - "9 Hubbard OR\n", - "10 Fontana CA\n", - "11 Waterloo AL\n", - "12 Belton SC\n", - "13 Keokuk IA\n", - "14 Ludington MI\n", - "15 Forest Home CA\n", - "16 Los Angeles CA\n", - "17 Hapeville GA\n", - "18 Oneida TN\n", - "19 Bering Sea AK\n", - "20 Nebraska NE\n", - "21 NaN LA\n", - "22 NaN LA\n", - "23 Owensboro KY\n", - "24 Wilderness WV\n", - "25 San Diego CA\n", - "26 Wilderness WV\n", - "27 Clovis NM\n", - "28 Los Alamos NM\n", - "29 Ft. Duschene UT\n", - "... ... ...\n", - "80513 Manahawkin NJ\n", - "80514 New Bedford MA\n", - "80515 Woodbridge VA\n", - "80516 Glendale CA\n", - "80517 Laconia NH\n", - "80518 Langhorne PA\n", - "80519 Glen Ellyn IL\n", - "80520 Waynesburg PA\n", - "80521 Canal Winchester OH\n", - "80522 Lawrence MA\n", - "80523 Ellicott City MD\n", - "80524 Olympia WA\n", - "80525 Iowa City IA\n", - "80526 Marshfield MA\n", - "80527 Seattle WA\n", - "80528 North Royalton OH\n", - "80529 Deer Park WA\n", - "80530 Glen St. Mary FL\n", - "80531 Abingdon VA\n", - "80532 Lawrence MA\n", - "80533 Melbourne IA\n", - "80534 Burleson TX\n", - "80535 Elizabethtown KY\n", - "80536 Wyoming PA\n", - "80537 Neligh NE\n", - "80538 Neligh NE\n", - "80539 Uhrichsville OH\n", - "80540 Tucson AZ\n", - "80541 Orland park IL\n", - "80542 Loughman FL\n", - "\n", - "[80543 rows x 2 columns]" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Select two columns.\n", - "ufo.loc[:, ['City', 'State']]" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CityColors ReportedShape ReportedState
0IthacaNaNTRIANGLENY
1WillingboroNaNOTHERNJ
2HolyokeNaNOVALCO
3AbileneNaNDISKKS
4New York Worlds FairNaNLIGHTNY
5Valley CityNaNDISKND
6Crater LakeNaNCIRCLECA
7AlmaNaNDISKMI
8EklutnaNaNCIGARAK
9HubbardNaNCYLINDEROR
10FontanaNaNLIGHTCA
11WaterlooNaNFIREBALLAL
12BeltonREDSPHERESC
13KeokukNaNOVALIA
14LudingtonNaNDISKMI
15Forest HomeNaNCIRCLECA
16Los AngelesNaNNaNCA
17HapevilleNaNNaNGA
18OneidaNaNRECTANGLETN
19Bering SeaREDOTHERAK
20NebraskaNaNDISKNE
21NaNNaNNaNLA
22NaNNaNLIGHTLA
23OwensboroNaNRECTANGLEKY
24WildernessNaNDISKWV
25San DiegoNaNCIGARCA
26WildernessNaNDISKWV
27ClovisNaNDISKNM
28Los AlamosNaNDISKNM
29Ft. DuscheneNaNDISKUT
...............
80513ManahawkinNaNCIRCLENJ
80514New BedfordNaNLIGHTMA
80515WoodbridgeNaNTRIANGLEVA
80516GlendaleNaNCYLINDERCA
80517LaconiaNaNCIGARNH
80518LanghorneNaNOTHERPA
80519Glen EllynREDCIRCLEIL
80520WaynesburgNaNFLASHPA
80521Canal WinchesterNaNTRIANGLEOH
80522LawrenceORANGELIGHTMA
80523Ellicott CityNaNLIGHTMD
80524OlympiaREDLIGHTWA
80525Iowa CityBLUELIGHTIA
80526MarshfieldNaNLIGHTMA
80527SeattleNaNDIAMONDWA
80528North RoyaltonREDTRIANGLEOH
80529Deer ParkNaNLIGHTWA
80530Glen St. MaryNaNDISKFL
80531AbingdonNaNCIRCLEVA
80532LawrenceNaNLIGHTMA
80533MelbourneNaNOVALIA
80534BurlesonNaNLIGHTTX
80535ElizabethtownNaNCIRCLEKY
80536WyomingREDDISKPA
80537NelighNaNCIRCLENE
80538NelighNaNCIRCLENE
80539UhrichsvilleNaNLIGHTOH
80540TucsonRED BLUENaNAZ
80541Orland parkREDLIGHTIL
80542LoughmanNaNLIGHTFL
\n", - "

80543 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " City Colors Reported Shape Reported State\n", - "0 Ithaca NaN TRIANGLE NY\n", - "1 Willingboro NaN OTHER NJ\n", - "2 Holyoke NaN OVAL CO\n", - "3 Abilene NaN DISK KS\n", - "4 New York Worlds Fair NaN LIGHT NY\n", - "5 Valley City NaN DISK ND\n", - "6 Crater Lake NaN CIRCLE CA\n", - "7 Alma NaN DISK MI\n", - "8 Eklutna NaN CIGAR AK\n", - "9 Hubbard NaN CYLINDER OR\n", - "10 Fontana NaN LIGHT CA\n", - "11 Waterloo NaN FIREBALL AL\n", - "12 Belton RED SPHERE SC\n", - "13 Keokuk NaN OVAL IA\n", - "14 Ludington NaN DISK MI\n", - "15 Forest Home NaN CIRCLE CA\n", - "16 Los Angeles NaN NaN CA\n", - "17 Hapeville NaN NaN GA\n", - "18 Oneida NaN RECTANGLE TN\n", - "19 Bering Sea RED OTHER AK\n", - "20 Nebraska NaN DISK NE\n", - "21 NaN NaN NaN LA\n", - "22 NaN NaN LIGHT LA\n", - "23 Owensboro NaN RECTANGLE KY\n", - "24 Wilderness NaN DISK WV\n", - "25 San Diego NaN CIGAR CA\n", - "26 Wilderness NaN DISK WV\n", - "27 Clovis NaN DISK NM\n", - "28 Los Alamos NaN DISK NM\n", - "29 Ft. Duschene NaN DISK UT\n", - "... ... ... ... ...\n", - "80513 Manahawkin NaN CIRCLE NJ\n", - "80514 New Bedford NaN LIGHT MA\n", - "80515 Woodbridge NaN TRIANGLE VA\n", - "80516 Glendale NaN CYLINDER CA\n", - "80517 Laconia NaN CIGAR NH\n", - "80518 Langhorne NaN OTHER PA\n", - "80519 Glen Ellyn RED CIRCLE IL\n", - "80520 Waynesburg NaN FLASH PA\n", - "80521 Canal Winchester NaN TRIANGLE OH\n", - "80522 Lawrence ORANGE LIGHT MA\n", - "80523 Ellicott City NaN LIGHT MD\n", - "80524 Olympia RED LIGHT WA\n", - "80525 Iowa City BLUE LIGHT IA\n", - "80526 Marshfield NaN LIGHT MA\n", - "80527 Seattle NaN DIAMOND WA\n", - "80528 North Royalton RED TRIANGLE OH\n", - "80529 Deer Park NaN LIGHT WA\n", - "80530 Glen St. Mary NaN DISK FL\n", - "80531 Abingdon NaN CIRCLE VA\n", - "80532 Lawrence NaN LIGHT MA\n", - "80533 Melbourne NaN OVAL IA\n", - "80534 Burleson NaN LIGHT TX\n", - "80535 Elizabethtown NaN CIRCLE KY\n", - "80536 Wyoming RED DISK PA\n", - "80537 Neligh NaN CIRCLE NE\n", - "80538 Neligh NaN CIRCLE NE\n", - "80539 Uhrichsville NaN LIGHT OH\n", - "80540 Tucson RED BLUE NaN AZ\n", - "80541 Orland park RED LIGHT IL\n", - "80542 Loughman NaN LIGHT FL\n", - "\n", - "[80543 rows x 4 columns]" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Select a range of columns — unlike Python ranges, Pandas index ranges INCLUDE the final column in the range.\n", - "ufo.loc[:, 'City':'State']\n" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "City Ithaca\n", - "Colors Reported NaN\n", - "Shape Reported TRIANGLE\n", - "State NY\n", - "Time 6/1/1930 22:00\n", - "Name: 0, dtype: object" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# \"loc\" can also filter rows by \"name\" (the index).\n", - "# Row 0, all columns\n", - "ufo.loc[0, :]" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
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" - ], - "text/plain": [ - " City Colors Reported Shape Reported State Time\n", - "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", - "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", - "2 Holyoke NaN OVAL CO 2/15/1931 14:00" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Rows 0/1/2, all columns\n", - "ufo.loc[0:2, :]" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CityColors ReportedShape ReportedState
0IthacaNaNTRIANGLENY
1WillingboroNaNOTHERNJ
2HolyokeNaNOVALCO
\n", - "
" - ], - "text/plain": [ - " City Colors Reported Shape Reported State\n", - "0 Ithaca NaN TRIANGLE NY\n", - "1 Willingboro NaN OTHER NJ\n", - "2 Holyoke NaN OVAL CO" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Rows 0/1/2, range of columns\n", - "ufo.loc[0:2, 'City':'State'] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Joining (Merging) `DataFrames`\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitle
01Toy Story (1995)
12GoldenEye (1995)
23Four Rooms (1995)
34Get Shorty (1995)
45Copycat (1995)
\n", - "
" - ], - "text/plain": [ - " movie_id title\n", - "0 1 Toy Story (1995)\n", - "1 2 GoldenEye (1995)\n", - "2 3 Four Rooms (1995)\n", - "3 4 Get Shorty (1995)\n", - "4 5 Copycat (1995)" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "movie_cols = ['movie_id', 'title']\n", - "u_item = './data/movies.tbl'\n", - "movies = pd.read_csv(u_item, sep='|', header=None, names=movie_cols, usecols=[0, 1], encoding='latin-1')\n", - "movies.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamp
01962423881250949
11863023891717742
2223771878887116
3244512880606923
41663461886397596
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" - ], - "text/plain": [ - " user_id movie_id rating timestamp\n", - "0 196 242 3 881250949\n", - "1 186 302 3 891717742\n", - "2 22 377 1 878887116\n", - "3 244 51 2 880606923\n", - "4 166 346 1 886397596" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rating_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings_filename = './data/movie_ratings.tsv'\n", - "\n", - "ratings = pd.read_csv(ratings_filename, sep='\\t', header=None, names=rating_cols)\n", - "ratings.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
movie_idtitleuser_idratingtimestamp
01Toy Story (1995)3084887736532
11Toy Story (1995)2875875334088
21Toy Story (1995)1484877019411
31Toy Story (1995)2804891700426
41Toy Story (1995)663883601324
\n", - "
" - ], - "text/plain": [ - " movie_id title user_id rating timestamp\n", - "0 1 Toy Story (1995) 308 4 887736532\n", - "1 1 Toy Story (1995) 287 5 875334088\n", - "2 1 Toy Story (1995) 148 4 877019411\n", - "3 1 Toy Story (1995) 280 4 891700426\n", - "4 1 Toy Story (1995) 66 3 883601324" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Merge \"movies\" and \"ratings\" (inner join on \"movie_id\").\n", - "movie_ratings = pd.merge(movies, ratings)\n", - "movie_ratings.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1682, 2)\n", - "(100000, 4)\n", - "(100000, 5)\n" - ] - } - ], - "source": [ - "print(movies.shape)\n", - "print(ratings.shape)\n", - "print(movie_ratings.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How to export your file?" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "movie_ratings.to_csv('new_movies.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### More Details about Merging \n", - "https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pandas CheatSheet\n", - "https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### OPTIONAL: Other Commonly Used Features" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "# Apply an arbitrary function to each value of a Pandas column, storing the result in a new column.\n", - "users['under30'] = users.age.apply(lambda age: age < 30)" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [], - "source": [ - "# Apply an arbitrary function to each row of a DataFrame, storing the result in a new column.\n", - "# (Remember that, by default, axis=0. Since we want to go row by row, we set axis=1.)\n", - "users['under30male'] = users.apply(lambda row: row.age < 30 and row.gender == 'M', axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "# Map existing values to a different set of values.\n", - "users['is_male'] = users.gender.map({'F':0, 'M':1})" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [], - "source": [ - "# Replace all instances of a value in a column (must match entire value).\n", - "ufo.State.replace('Fl', 'FL', inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "5 False\n", - "6 False\n", - "7 False\n", - "8 False\n", - "9 False\n", - "10 False\n", - "11 False\n", - "12 True\n", - "13 False\n", - "14 False\n", - "15 False\n", - "16 False\n", - "17 False\n", - "18 False\n", - "19 True\n", - "20 False\n", - "21 False\n", - "22 False\n", - "23 False\n", - "24 False\n", - "25 False\n", - "26 False\n", - "27 False\n", - "28 False\n", - "29 False\n", - " ... \n", - "80513 False\n", - "80514 False\n", - "80515 False\n", - "80516 False\n", - "80517 False\n", - "80518 False\n", - "80519 True\n", - "80520 False\n", - "80521 False\n", - "80522 False\n", - "80523 False\n", - "80524 True\n", - "80525 False\n", - "80526 False\n", - "80527 False\n", - "80528 True\n", - "80529 False\n", - "80530 False\n", - "80531 False\n", - "80532 False\n", - "80533 False\n", - "80534 False\n", - "80535 False\n", - "80536 True\n", - "80537 False\n", - "80538 False\n", - "80539 False\n", - "80540 True\n", - "80541 True\n", - "80542 False\n", - "Name: Colors Reported, Length: 80543, dtype: object" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# String methods are accessed via \"str\".\n", - "ufo.State.str.upper() # Converts to upper case\n", - "# checks for a substring\n", - "ufo['Colors Reported'].str.contains('RED', na='False') " - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "30776" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Convert a string to the datetime format (this is often slow — consider doing it in the \"read_csv()\" method.)\n", - "ufo['Time'] = pd.to_datetime(ufo.Time)\n", - "ufo.Time.dt.hour # Datetime format exposes convenient attributes\n", - "(ufo.Time.max() - ufo.Time.min()).days # Also allows you to do datetime \"math\"" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "# Set and then remove an index.\n", - "ufo.set_index('Time', inplace=True)\n", - "ufo.reset_index(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], - "source": [ - "# Change the datatype of a column.\n", - "drinks['beer'] = drinks.beer.astype('float')" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "# Create dummy variables for \"continent\" and exclude first dummy column.\n", - "continent_dummies = pd.get_dummies(drinks.continent, prefix='cont').iloc[:, 1:]" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [], - "source": [ - "# Concatenate two DataFrames (axis=0 for rows, axis=1 for columns).\n", - "drinks = pd.concat([drinks, continent_dummies], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### OPTIONAL: Other Less-Used Features of Pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "13" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Detecting duplicate rows\n", - "users.duplicated() # True if a row is identical to a previous row\n", - "users.duplicated().sum() # Count of duplicates\n", - "users[users.duplicated()] # Only show duplicates\n", - "users.drop_duplicates() # Drop duplicate rows\n", - "users.age.duplicated() # Check a single column for duplicates\n", - "users.duplicated(['age', 'gender', 'zip_code']).sum() # Specify columns for finding duplicates" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert a range of values into descriptive groups.\n", - "drinks['beer_level'] = 'low' # Initially set all values to \"low\"\n", - "drinks.loc[drinks.beer.between(101, 200), 'beer_level'] = 'med' # Change 101-200 to \"med\"\n", - "drinks.loc[drinks.beer.between(201, 400), 'beer_level'] = 'high' # Change 201-400 to \"high\"" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - 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106Marshall Islands0.0000.0OC00010low
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110Micronesia62.050182.3OC00010low
112Mongolia77.018984.9AS10000low
113Montenegro31.01141284.9EU01000low
114Morocco12.06100.5AF00000low
115Mozambique47.01851.3AF00000low
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118Nauru49.0081.0OC00010low
111Monaco0.0000.0EU01000low
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97Libya0.0000.0AF00000low
191Zambia32.01942.5AF00000low
78Indonesia5.0100.1AS10000low
79Iran0.0000.0AS10000low
80Iraq9.0300.2AS10000low
82Israel63.06992.5AS10000low
83Italy85.0422376.5EU01000low
84Jamaica82.09793.4NA00100low
85Japan77.0202167.0AS10000low
86Jordan6.02110.5AS10000low
88Kenya58.02221.8AF00000low
89Kiribati21.03411.0OC00010low
90Kuwait0.0000.0AS10000low
.......................................
3Andorra245.013831212.4EU01000high
76Iceland233.061786.6EU01000high
188Venezuela333.010037.7SA00001high
99Luxembourg236.013327111.4EU01000high
75Hungary234.021518511.3EU01000high
109Mexico238.06855.5NA00100high
57Estonia224.0194599.5EU01000high
140Romania297.012216710.4EU01000high
93Latvia281.02166210.5EU01000high
141Russian Federation247.03267311.5AS10000high
135Poland343.02155610.9EU01000high
62Gabon347.098598.9AF00000high
132Paraguay213.0117747.3SA00001high
25Bulgaria231.02529410.3EU01000high
65Germany346.011717511.3EU01000high
151Serbia283.01311279.6EU01000high
130Panama285.0104187.2NA00100high
23Brazil245.0145167.2SA00001high
129Palau306.063236.9OC00010high
32Canada240.01221008.2NA00100high
156Slovenia270.05127610.6EU01000high
60Finland263.01339710.0EU01000high
159South Africa225.076818.2AF00000high
160Spain284.015711210.0EU01000high
81Ireland313.011816511.4EU01000high
17Belize263.011486.8NA00100high
121New Zealand203.0791759.3OC00010high
16Belgium295.08421210.5EU01000high
117Namibia376.0316.8AF00000high
120Netherlands251.0881909.4EU01000high
\n", - "

193 rows × 12 columns

\n", - "
" - ], - "text/plain": [ - " country beer spirit wine liters continent cont_AS \\\n", - "0 Afghanistan 0.0 0 0 0.0 AS 1 \n", - "102 Malaysia 13.0 4 0 0.3 AS 1 \n", - "103 Maldives 0.0 0 0 0.0 AS 1 \n", - "104 Mali 5.0 1 1 0.6 AF 0 \n", - "106 Marshall Islands 0.0 0 0 0.0 OC 0 \n", - "107 Mauritania 0.0 0 0 0.0 AF 0 \n", - "108 Mauritius 98.0 31 18 2.6 AF 0 \n", - "101 Malawi 8.0 11 1 1.5 AF 0 \n", - "110 Micronesia 62.0 50 18 2.3 OC 0 \n", - "112 Mongolia 77.0 189 8 4.9 AS 1 \n", - "113 Montenegro 31.0 114 128 4.9 EU 0 \n", - "114 Morocco 12.0 6 10 0.5 AF 0 \n", - "115 Mozambique 47.0 18 5 1.3 AF 0 \n", - "116 Myanmar 5.0 1 0 0.1 AS 1 \n", - "118 Nauru 49.0 0 8 1.0 OC 0 \n", - "111 Monaco 0.0 0 0 0.0 EU 0 \n", - "100 Madagascar 26.0 15 4 0.8 AF 0 \n", - "97 Libya 0.0 0 0 0.0 AF 0 \n", - "191 Zambia 32.0 19 4 2.5 AF 0 \n", - "78 Indonesia 5.0 1 0 0.1 AS 1 \n", - "79 Iran 0.0 0 0 0.0 AS 1 \n", - "80 Iraq 9.0 3 0 0.2 AS 1 \n", - "82 Israel 63.0 69 9 2.5 AS 1 \n", - "83 Italy 85.0 42 237 6.5 EU 0 \n", - "84 Jamaica 82.0 97 9 3.4 NA 0 \n", - "85 Japan 77.0 202 16 7.0 AS 1 \n", - "86 Jordan 6.0 21 1 0.5 AS 1 \n", - "88 Kenya 58.0 22 2 1.8 AF 0 \n", - "89 Kiribati 21.0 34 1 1.0 OC 0 \n", - "90 Kuwait 0.0 0 0 0.0 AS 1 \n", - ".. ... ... ... ... ... ... ... \n", - "3 Andorra 245.0 138 312 12.4 EU 0 \n", - "76 Iceland 233.0 61 78 6.6 EU 0 \n", - "188 Venezuela 333.0 100 3 7.7 SA 0 \n", - "99 Luxembourg 236.0 133 271 11.4 EU 0 \n", - "75 Hungary 234.0 215 185 11.3 EU 0 \n", - "109 Mexico 238.0 68 5 5.5 NA 0 \n", - "57 Estonia 224.0 194 59 9.5 EU 0 \n", - "140 Romania 297.0 122 167 10.4 EU 0 \n", - "93 Latvia 281.0 216 62 10.5 EU 0 \n", - "141 Russian Federation 247.0 326 73 11.5 AS 1 \n", - "135 Poland 343.0 215 56 10.9 EU 0 \n", - "62 Gabon 347.0 98 59 8.9 AF 0 \n", - "132 Paraguay 213.0 117 74 7.3 SA 0 \n", - "25 Bulgaria 231.0 252 94 10.3 EU 0 \n", - "65 Germany 346.0 117 175 11.3 EU 0 \n", - "151 Serbia 283.0 131 127 9.6 EU 0 \n", - "130 Panama 285.0 104 18 7.2 NA 0 \n", - "23 Brazil 245.0 145 16 7.2 SA 0 \n", - "129 Palau 306.0 63 23 6.9 OC 0 \n", - "32 Canada 240.0 122 100 8.2 NA 0 \n", - "156 Slovenia 270.0 51 276 10.6 EU 0 \n", - "60 Finland 263.0 133 97 10.0 EU 0 \n", - "159 South Africa 225.0 76 81 8.2 AF 0 \n", - "160 Spain 284.0 157 112 10.0 EU 0 \n", - "81 Ireland 313.0 118 165 11.4 EU 0 \n", - "17 Belize 263.0 114 8 6.8 NA 0 \n", - "121 New Zealand 203.0 79 175 9.3 OC 0 \n", - "16 Belgium 295.0 84 212 10.5 EU 0 \n", - "117 Namibia 376.0 3 1 6.8 AF 0 \n", - "120 Netherlands 251.0 88 190 9.4 EU 0 \n", - "\n", - " cont_EU cont_NA cont_OC cont_SA beer_level \n", - "0 0 0 0 0 low \n", - "102 0 0 0 0 low \n", - "103 0 0 0 0 low \n", - "104 0 0 0 0 low \n", - "106 0 0 1 0 low \n", - "107 0 0 0 0 low \n", - "108 0 0 0 0 low \n", - "101 0 0 0 0 low \n", - "110 0 0 1 0 low \n", - "112 0 0 0 0 low \n", - "113 1 0 0 0 low \n", - "114 0 0 0 0 low \n", - "115 0 0 0 0 low \n", - "116 0 0 0 0 low \n", - "118 0 0 1 0 low \n", - "111 1 0 0 0 low \n", - "100 0 0 0 0 low \n", - "97 0 0 0 0 low \n", - "191 0 0 0 0 low \n", - "78 0 0 0 0 low \n", - "79 0 0 0 0 low \n", - "80 0 0 0 0 low \n", - "82 0 0 0 0 low \n", - "83 1 0 0 0 low \n", - "84 0 1 0 0 low \n", - "85 0 0 0 0 low \n", - "86 0 0 0 0 low \n", - "88 0 0 0 0 low \n", - "89 0 0 1 0 low \n", - "90 0 0 0 0 low \n", - ".. ... ... ... ... ... \n", - "3 1 0 0 0 high \n", - "76 1 0 0 0 high \n", - "188 0 0 0 1 high \n", - "99 1 0 0 0 high \n", - "75 1 0 0 0 high \n", - "109 0 1 0 0 high \n", - "57 1 0 0 0 high \n", - "140 1 0 0 0 high \n", - "93 1 0 0 0 high \n", - "141 0 0 0 0 high \n", - "135 1 0 0 0 high \n", - "62 0 0 0 0 high \n", - "132 0 0 0 1 high \n", - "25 1 0 0 0 high \n", - "65 1 0 0 0 high \n", - "151 1 0 0 0 high \n", - "130 0 1 0 0 high \n", - "23 0 0 0 1 high \n", - "129 0 0 1 0 high \n", - "32 0 1 0 0 high \n", - "156 1 0 0 0 high \n", - "60 1 0 0 0 high \n", - "159 0 0 0 0 high \n", - "160 1 0 0 0 high \n", - "81 1 0 0 0 high \n", - "17 0 1 0 0 high \n", - "121 0 0 1 0 high \n", - "16 1 0 0 0 high \n", - "117 0 0 0 0 high \n", - "120 1 0 0 0 high \n", - "\n", - "[193 rows x 12 columns]" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Convert \"beer_level\" into the \"category\" datatype.\n", - "drinks['beer_level'] = pd.Categorical(drinks.beer_level, categories=['low', 'med', 'high'])\n", - "drinks.sort_values('beer_level') # Sorts by the categorical ordering (low to high)" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Algeria250140.7AF
1Andorra24513831212.4EU
2Angola21757455.9AF
3Antigua & Barbuda102128454.9NaN
4Argentina193252218.3SA
5Armenia21179113.8EU
6Australia2617221210.4OC
7Austria279751919.7EU
8Azerbaijan214651.3EU
9Bahamas122176516.3NaN
10Bahrain426372.0AS
11Bangladesh0000.0AS
12Barbados143173366.3NaN
13Belarus1423734214.4EU
14Belgium2958421210.5EU
15Belize26311486.8NaN
16Benin344131.1AF
17Bhutan23000.4AS
18Bolivia1674183.8SA
19Bosnia-Herzegovina7617384.6EU
20Botswana17335355.4AF
21Brazil245145167.2SA
22Brunei31210.6AS
23Bulgaria2312529410.3EU
24Burkina Faso25774.3AF
25Burundi88006.3AF
26Cote d'Ivoire37174.0AF
27Cabo Verde14456164.0AF
28Cambodia576512.2AS
29Cameroon147145.8AF
.....................
161Suriname12817875.6SA
162Swaziland90224.7AF
163Sweden152601867.2EU
164Switzerland18510028010.2EU
165Syria535161.0AS
166Tajikistan21500.3AS
167Thailand9925816.4AS
168Macedonia10627863.9EU
169Timor-Leste1140.1AS
170Togo362191.3AF
171Tonga362151.1OC
172Trinidad & Tobago19715676.4NaN
173Tunisia513201.3AF
174Turkey512271.4AS
175Turkmenistan1971322.2AS
176Tuvalu64191.0OC
177Uganda45908.3AF
178Ukraine206237458.9EU
179United Arab Emirates1613552.8AS
180United Kingdom21912619510.4EU
181Tanzania36615.7AF
182USA249158848.7NaN
183Uruguay115352206.6SA
184Uzbekistan2510182.4AS
185Vanuatu2118110.9OC
186Venezuela33310037.7SA
187Vietnam111212.0AS
188Yemen6000.1AS
189Zambia321942.5AF
190Zimbabwe641844.7AF
\n", - "

191 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " country beer_servings spirit_servings wine_servings \\\n", - "0 Algeria 25 0 14 \n", - "1 Andorra 245 138 312 \n", - "2 Angola 217 57 45 \n", - "3 Antigua & Barbuda 102 128 45 \n", - "4 Argentina 193 25 221 \n", - "5 Armenia 21 179 11 \n", - "6 Australia 261 72 212 \n", - "7 Austria 279 75 191 \n", - "8 Azerbaijan 21 46 5 \n", - "9 Bahamas 122 176 51 \n", - "10 Bahrain 42 63 7 \n", - "11 Bangladesh 0 0 0 \n", - "12 Barbados 143 173 36 \n", - "13 Belarus 142 373 42 \n", - "14 Belgium 295 84 212 \n", - "15 Belize 263 114 8 \n", - "16 Benin 34 4 13 \n", - "17 Bhutan 23 0 0 \n", - "18 Bolivia 167 41 8 \n", - "19 Bosnia-Herzegovina 76 173 8 \n", - "20 Botswana 173 35 35 \n", - "21 Brazil 245 145 16 \n", - "22 Brunei 31 2 1 \n", - "23 Bulgaria 231 252 94 \n", - "24 Burkina Faso 25 7 7 \n", - "25 Burundi 88 0 0 \n", - "26 Cote d'Ivoire 37 1 7 \n", - "27 Cabo Verde 144 56 16 \n", - "28 Cambodia 57 65 1 \n", - "29 Cameroon 147 1 4 \n", - ".. ... ... ... ... \n", - "161 Suriname 128 178 7 \n", - "162 Swaziland 90 2 2 \n", - "163 Sweden 152 60 186 \n", - "164 Switzerland 185 100 280 \n", - "165 Syria 5 35 16 \n", - "166 Tajikistan 2 15 0 \n", - "167 Thailand 99 258 1 \n", - "168 Macedonia 106 27 86 \n", - "169 Timor-Leste 1 1 4 \n", - "170 Togo 36 2 19 \n", - "171 Tonga 36 21 5 \n", - "172 Trinidad & Tobago 197 156 7 \n", - "173 Tunisia 51 3 20 \n", - "174 Turkey 51 22 7 \n", - "175 Turkmenistan 19 71 32 \n", - "176 Tuvalu 6 41 9 \n", - "177 Uganda 45 9 0 \n", - "178 Ukraine 206 237 45 \n", - "179 United Arab Emirates 16 135 5 \n", - "180 United Kingdom 219 126 195 \n", - "181 Tanzania 36 6 1 \n", - "182 USA 249 158 84 \n", - "183 Uruguay 115 35 220 \n", - "184 Uzbekistan 25 101 8 \n", - "185 Vanuatu 21 18 11 \n", - "186 Venezuela 333 100 3 \n", - "187 Vietnam 111 2 1 \n", - "188 Yemen 6 0 0 \n", - "189 Zambia 32 19 4 \n", - "190 Zimbabwe 64 18 4 \n", - "\n", - " total_litres_of_pure_alcohol continent \n", - "0 0.7 AF \n", - "1 12.4 EU \n", - "2 5.9 AF \n", - "3 4.9 NaN \n", - "4 8.3 SA \n", - "5 3.8 EU \n", - "6 10.4 OC \n", - "7 9.7 EU \n", - "8 1.3 EU \n", - "9 6.3 NaN \n", - "10 2.0 AS \n", - "11 0.0 AS \n", - "12 6.3 NaN \n", - "13 14.4 EU \n", - "14 10.5 EU \n", - "15 6.8 NaN \n", - "16 1.1 AF \n", - "17 0.4 AS \n", - "18 3.8 SA \n", - "19 4.6 EU \n", - "20 5.4 AF \n", - "21 7.2 SA \n", - "22 0.6 AS \n", - "23 10.3 EU \n", - "24 4.3 AF \n", - "25 6.3 AF \n", - "26 4.0 AF \n", - "27 4.0 AF \n", - "28 2.2 AS \n", - "29 5.8 AF \n", - ".. ... ... \n", - "161 5.6 SA \n", - "162 4.7 AF \n", - "163 7.2 EU \n", - "164 10.2 EU \n", - "165 1.0 AS \n", - "166 0.3 AS \n", - "167 6.4 AS \n", - "168 3.9 EU \n", - "169 0.1 AS \n", - "170 1.3 AF \n", - "171 1.1 OC \n", - "172 6.4 NaN \n", - "173 1.3 AF \n", - "174 1.4 AS \n", - "175 2.2 AS \n", - "176 1.0 OC \n", - "177 8.3 AF \n", - "178 8.9 EU \n", - "179 2.8 AS \n", - "180 10.4 EU \n", - "181 5.7 AF \n", - "182 8.7 NaN \n", - "183 6.6 SA \n", - "184 2.4 AS \n", - "185 0.9 OC \n", - "186 7.7 SA \n", - "187 2.0 AS \n", - "188 0.1 AS \n", - "189 2.5 AF \n", - "190 4.7 AF \n", - "\n", - "[191 rows x 6 columns]" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "# Count the number of missing values in each column.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# How many rows remain if you drop all rows with any missing values?\n", + "print('the original size:', ufo.shape[0])\n", + "ufo.dropna().shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Split-Apply-Combine\n", + "\n", + "\n", + "\n", + "Split-apply-combine is a pattern for analyzing data. Suppose we want to find mean beer consumption per country. Then:\n", + "\n", + "- **Split:** We group data by continent.\n", + "- **Apply:** For each group, we apply the `mean()` function to find the average beer consumption.\n", + "- **Combine:** We now combine the continent names with the `mean()`s to produce a summary of our findings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "drinks.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each continent, calculate the mean beer servings.\n", + "drinks.groupby('continent').beer.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each continent, calculate the mean of all numeric columns.\n", + "drinks.groupby('continent').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each continent, describe beer servings.\n", + "drinks.groupby('continent').beer.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each continent, describe all numeric columns.\n", + "drinks.groupby('continent').describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# For each continent, count the number of rows.\n", + "print((drinks.continent.value_counts())) # should be the same" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise 4 : TAKE HOME\n", + "\n", + "\n", + "\n", + "Use the \"users\" `DataFrame` or \"users\" file in the Data folder to complete the following." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "users.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each occupation in \"users\", count the number of occurrences.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each occupation, calculate the mean age.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# For each occupation, calculate the minimum and maximum ages.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For each combination of occupation and gender, calculate the mean age.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Selecting Multiple Columns and Filtering Rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ufo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Or, combine into a single step (this is a Python list inside of the Python index operator!).\n", + "ufo[['City', 'State']].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Use `loc` to select columns by name.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \"loc\" locates the values from the first parameter (colon means \"all rows\"), and the column \"City\".\n", + "ufo.loc[:, 'City'] " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select two columns.\n", + "ufo.loc[:, ['City', 'State']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select a range of columns — unlike Python ranges, Pandas index ranges INCLUDE the final column in the range.\n", + "ufo.loc[:, 'City':'State']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \"loc\" can also filter rows by \"name\" (the index).\n", + "# Row 0, all columns\n", + "ufo.loc[0, :]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Rows 0/1/2, all columns\n", + "ufo.loc[0:2, :]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Rows 0/1/2, range of columns\n", + "ufo.loc[0:2, 'City':'State'] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Joining (Merging) `DataFrames`\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "movie_cols = ['movie_id', 'title']\n", + "u_item = './data/movies.tbl'\n", + "movies = pd.read_csv(u_item, sep='|', header=None, names=movie_cols, usecols=[0, 1], encoding='latin-1')\n", + "movies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rating_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings_filename = './data/movie_ratings.tsv'\n", + "\n", + "ratings = pd.read_csv(ratings_filename, sep='\\t', header=None, names=rating_cols)\n", + "ratings.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge \"movies\" and \"ratings\" (inner join on \"movie_id\").\n", + "movie_ratings = pd.merge(movies, ratings)\n", + "movie_ratings.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(movies.shape)\n", + "print(ratings.shape)\n", + "print(movie_ratings.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to export your file?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "movie_ratings.to_csv('new_movies.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More Details about Merging \n", + "https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas CheatSheet\n", + "https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### OPTIONAL: Other Commonly Used Features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply an arbitrary function to each value of a Pandas column, storing the result in a new column.\n", + "users['under30'] = users.age.apply(lambda age: age < 30)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply an arbitrary function to each row of a DataFrame, storing the result in a new column.\n", + "# (Remember that, by default, axis=0. Since we want to go row by row, we set axis=1.)\n", + "users['under30male'] = users.apply(lambda row: row.age < 30 and row.gender == 'M', axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Map existing values to a different set of values.\n", + "users['is_male'] = users.gender.map({'F':0, 'M':1})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Replace all instances of a value in a column (must match entire value).\n", + "ufo.State.replace('Fl', 'FL', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# String methods are accessed via \"str\".\n", + "ufo.State.str.upper() # Converts to upper case\n", + "# checks for a substring\n", + "ufo['Colors Reported'].str.contains('RED', na='False') " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert a string to the datetime format (this is often slow — consider doing it in the \"read_csv()\" method.)\n", + "ufo['Time'] = pd.to_datetime(ufo.Time)\n", + "ufo.Time.dt.hour # Datetime format exposes convenient attributes\n", + "(ufo.Time.max() - ufo.Time.min()).days # Also allows you to do datetime \"math\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set and then remove an index.\n", + "ufo.set_index('Time', inplace=True)\n", + "ufo.reset_index(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Change the datatype of a column.\n", + "drinks['beer'] = drinks.beer.astype('float')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create dummy variables for \"continent\" and exclude first dummy column.\n", + "continent_dummies = pd.get_dummies(drinks.continent, prefix='cont').iloc[:, 1:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Concatenate two DataFrames (axis=0 for rows, axis=1 for columns).\n", + "drinks = pd.concat([drinks, continent_dummies], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### OPTIONAL: Other Less-Used Features of Pandas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Detecting duplicate rows\n", + "users.duplicated() # True if a row is identical to a previous row\n", + "users.duplicated().sum() # Count of duplicates\n", + "users[users.duplicated()] # Only show duplicates\n", + "users.drop_duplicates() # Drop duplicate rows\n", + "users.age.duplicated() # Check a single column for duplicates\n", + "users.duplicated(['age', 'gender', 'zip_code']).sum() # Specify columns for finding duplicates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert a range of values into descriptive groups.\n", + "drinks['beer_level'] = 'low' # Initially set all values to \"low\"\n", + "drinks.loc[drinks.beer.between(101, 200), 'beer_level'] = 'med' # Change 101-200 to \"med\"\n", + "drinks.loc[drinks.beer.between(201, 400), 'beer_level'] = 'high' # Change 201-400 to \"high\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display a cross-tabulation of two Series.\n", + "pd.crosstab(drinks.continent, drinks.beer_level)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert \"beer_level\" into the \"category\" datatype.\n", + "drinks['beer_level'] = pd.Categorical(drinks.beer_level, categories=['low', 'med', 'high'])\n", + "drinks.sort_values('beer_level') # Sorts by the categorical ordering (low to high)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Limit which rows are read when reading in a file — useful for large files!\n", "pd.read_csv('./data/drinks.csv', nrows=10) # Only read first 10 rows\n", @@ -16447,7 +4658,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -16458,66 +4669,9 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"6 Argentina 193.0 25 221 8.3 SA \n", - "7 Armenia 21.0 179 11 3.8 EU \n", - "8 Australia 261.0 72 212 10.4 OC \n", - "9 Austria 279.0 75 191 9.7 EU \n", - "10 Azerbaijan 21.0 46 5 1.3 EU \n", - "11 Bahamas 122.0 176 51 6.3 NA \n", - "12 Bahrain 42.0 63 7 2.0 AS \n", - "13 Bangladesh 0.0 0 0 0.0 AS \n", - "14 Barbados 143.0 173 36 6.3 NA \n", - "15 Belarus 142.0 373 42 14.4 EU \n", - "16 Belgium 295.0 84 212 10.5 EU \n", - "17 Belize 263.0 114 8 6.8 NA \n", - "18 Benin 34.0 4 13 1.1 AF \n", - "19 Bhutan 23.0 0 0 0.4 AS \n", - "20 Bolivia 167.0 41 8 3.8 SA \n", - "21 Bosnia-Herzegovina 76.0 173 8 4.6 EU \n", - "22 Botswana 173.0 35 35 5.4 AF \n", - "23 Brazil 245.0 145 16 7.2 SA \n", - "24 Brunei 31.0 2 1 0.6 AS \n", - "25 Bulgaria 231.0 252 94 10.3 EU \n", - "26 Burkina Faso 25.0 7 7 4.3 AF \n", - "27 Burundi 88.0 0 0 6.3 AF \n", - "28 Cote d'Ivoire 37.0 1 7 4.0 AF \n", - "29 Cabo Verde 144.0 56 16 4.0 AF \n", - "30 Cambodia 57.0 65 1 2.2 AS \n", - "31 Cameroon 147.0 1 4 5.8 AF \n", - "32 Canada 240.0 122 100 8.2 NA \n", - "33 Central African Republic 17.0 2 1 1.8 AF \n", - "34 Chad 15.0 1 1 0.4 AF \n", - "35 Chile 130.0 124 172 7.6 SA \n", - "36 China 79.0 192 8 5.0 AS \n", - "37 Colombia 159.0 76 3 4.2 SA \n", - "38 Comoros 1.0 3 1 0.1 AF \n", - "39 Congo 76.0 1 9 1.7 AF \n", - "40 Cook Islands 0.0 254 74 5.9 OC \n", - "41 Costa Rica 149.0 87 11 4.4 NA \n", - "42 Croatia 230.0 87 254 10.2 EU \n", - "43 Cuba 93.0 137 5 4.2 NA \n", - "44 Cyprus 192.0 154 113 8.2 EU \n", - "45 Czech Republic 361.0 170 134 11.8 EU \n", - "46 North Korea 0.0 0 0 0.0 AS \n", - "47 DR Congo 32.0 3 1 2.3 AF \n", - "48 Denmark 224.0 81 278 10.4 EU \n", - "49 Djibouti 15.0 44 3 1.1 AF \n", - "50 Dominica 52.0 286 26 6.6 NA \n", - "51 Dominican Republic 193.0 147 9 6.2 NA \n", - "52 Ecuador 162.0 74 3 4.2 SA \n", - "53 Egypt 6.0 4 1 0.2 AF \n", - "54 El Salvador 52.0 69 2 2.2 NA \n", - "55 Equatorial Guinea 92.0 0 233 5.8 AF \n", - "56 Eritrea 18.0 0 0 0.5 AF \n", - "57 Estonia 224.0 194 59 9.5 EU \n", - "58 Ethiopia 20.0 3 0 0.7 AF \n", - "59 Fiji 77.0 35 1 2.0 OC \n", - "60 Finland 263.0 133 97 10.0 EU \n", - "61 France 127.0 151 370 11.8 EU \n", - "62 Gabon 347.0 98 59 8.9 AF \n", - "63 Gambia 8.0 0 1 2.4 AF \n", - "64 Georgia 52.0 100 149 5.4 EU \n", - "65 Germany 346.0 117 175 11.3 EU \n", - "66 Ghana 31.0 3 10 1.8 AF \n", - "67 Greece 133.0 112 218 8.3 EU \n", - "68 Grenada 199.0 438 28 11.9 NA \n", - "69 Guatemala 53.0 69 2 2.2 NA \n", - "70 Guinea 9.0 0 2 0.2 AF \n", - "71 Guinea-Bissau 28.0 31 21 2.5 AF \n", - "72 Guyana 93.0 302 1 7.1 SA \n", - "73 Haiti 1.0 326 1 5.9 NA \n", - "74 Honduras 69.0 98 2 3.0 NA \n", - "75 Hungary 234.0 215 185 11.3 EU \n", - "76 Iceland 233.0 61 78 6.6 EU \n", - "77 India 9.0 114 0 2.2 AS \n", - "78 Indonesia 5.0 1 0 0.1 AS \n", - "79 Iran 0.0 0 0 0.0 AS \n", - "80 Iraq 9.0 3 0 0.2 AS \n", - "81 Ireland 313.0 118 165 11.4 EU \n", - "82 Israel 63.0 69 9 2.5 AS \n", - "83 Italy 85.0 42 237 6.5 EU \n", - "84 Jamaica 82.0 97 9 3.4 NA \n", - "85 Japan 77.0 202 16 7.0 AS \n", - "86 Jordan 6.0 21 1 0.5 AS \n", - "87 Kazakhstan 124.0 246 12 6.8 AS \n", - "88 Kenya 58.0 22 2 1.8 AF \n", - "89 Kiribati 21.0 34 1 1.0 OC \n", - "90 Kuwait 0.0 0 0 0.0 AS \n", - "91 Kyrgyzstan 31.0 97 6 2.4 AS \n", - "92 Laos 62.0 0 123 6.2 AS \n", - "93 Latvia 281.0 216 62 10.5 EU \n", - "94 Lebanon 20.0 55 31 1.9 AS \n", - "95 Lesotho 82.0 29 0 2.8 AF \n", - "96 Liberia 19.0 152 2 3.1 AF \n", - "97 Libya 0.0 0 0 0.0 AF \n", - "98 Lithuania 343.0 244 56 12.9 EU \n", - "99 Luxembourg 236.0 133 271 11.4 EU \n", - "100 Madagascar 26.0 15 4 0.8 AF \n", - "101 Malawi 8.0 11 1 1.5 AF \n", - "102 Malaysia 13.0 4 0 0.3 AS \n", - "103 Maldives 0.0 0 0 0.0 AS \n", - "104 Mali 5.0 1 1 0.6 AF \n", - "105 Malta 149.0 100 120 6.6 EU \n", - "106 Marshall Islands 0.0 0 0 0.0 OC \n", - "107 Mauritania 0.0 0 0 0.0 AF \n", - "108 Mauritius 98.0 31 18 2.6 AF \n", - "109 Mexico 238.0 68 5 5.5 NA \n", - "110 Micronesia 62.0 50 18 2.3 OC \n", - "111 Monaco 0.0 0 0 0.0 EU \n", - "112 Mongolia 77.0 189 8 4.9 AS \n", - "113 Montenegro 31.0 114 128 4.9 EU \n", - "114 Morocco 12.0 6 10 0.5 AF \n", - "115 Mozambique 47.0 18 5 1.3 AF \n", - "116 Myanmar 5.0 1 0 0.1 AS \n", - "117 Namibia 376.0 3 1 6.8 AF \n", - "118 Nauru 49.0 0 8 1.0 OC \n", - "119 Nepal 5.0 6 0 0.2 AS \n", - "120 Netherlands 251.0 88 190 9.4 EU \n", - "121 New Zealand 203.0 79 175 9.3 OC \n", - "122 Nicaragua 78.0 118 1 3.5 NA \n", - "123 Niger 3.0 2 1 0.1 AF \n", - "124 Nigeria 42.0 5 2 9.1 AF \n", - "125 Niue 188.0 200 7 7.0 OC \n", - "126 Norway 169.0 71 129 6.7 EU \n", - "127 Oman 22.0 16 1 0.7 AS \n", - "128 Pakistan 0.0 0 0 0.0 AS \n", - "129 Palau 306.0 63 23 6.9 OC \n", - "130 Panama 285.0 104 18 7.2 NA \n", - "131 Papua New Guinea 44.0 39 1 1.5 OC \n", - "132 Paraguay 213.0 117 74 7.3 SA \n", - "133 Peru 163.0 160 21 6.1 SA \n", - "134 Philippines 71.0 186 1 4.6 AS \n", - "135 Poland 343.0 215 56 10.9 EU \n", - "136 Portugal 194.0 67 339 11.0 EU \n", - "137 Qatar 1.0 42 7 0.9 AS \n", - "138 South Korea 140.0 16 9 9.8 AS \n", - "139 Moldova 109.0 226 18 6.3 EU \n", - "140 Romania 297.0 122 167 10.4 EU \n", - "141 Russian Federation 247.0 326 73 11.5 AS \n", - "142 Rwanda 43.0 2 0 6.8 AF \n", - "143 St. Kitts & Nevis 194.0 205 32 7.7 NA \n", - "144 St. Lucia 171.0 315 71 10.1 NA \n", - "145 St. Vincent & the Grenadines 120.0 221 11 6.3 NA \n", - "146 Samoa 105.0 18 24 2.6 OC \n", - "147 San Marino 0.0 0 0 0.0 EU \n", - "148 Sao Tome & Principe 56.0 38 140 4.2 AF \n", - "149 Saudi Arabia 0.0 5 0 0.1 AS \n", - "150 Senegal 9.0 1 7 0.3 AF \n", - "151 Serbia 283.0 131 127 9.6 EU \n", - "152 Seychelles 157.0 25 51 4.1 AF \n", - "153 Sierra Leone 25.0 3 2 6.7 AF \n", - "154 Singapore 60.0 12 11 1.5 AS \n", - "155 Slovakia 196.0 293 116 11.4 EU \n", - "156 Slovenia 270.0 51 276 10.6 EU \n", - "157 Solomon Islands 56.0 11 1 1.2 OC \n", - "158 Somalia 0.0 0 0 0.0 AF \n", - "159 South Africa 225.0 76 81 8.2 AF \n", - "160 Spain 284.0 157 112 10.0 EU \n", - "161 Sri Lanka 16.0 104 0 2.2 AS \n", - "162 Sudan 8.0 13 0 1.7 AF \n", - "163 Suriname 128.0 178 7 5.6 SA \n", - "164 Swaziland 90.0 2 2 4.7 AF \n", - "165 Sweden 152.0 60 186 7.2 EU \n", - "166 Switzerland 185.0 100 280 10.2 EU \n", - "167 Syria 5.0 35 16 1.0 AS \n", - "168 Tajikistan 2.0 15 0 0.3 AS \n", - "169 Thailand 99.0 258 1 6.4 AS \n", - "170 Macedonia 106.0 27 86 3.9 EU \n", - "171 Timor-Leste 1.0 1 4 0.1 AS \n", - "172 Togo 36.0 2 19 1.3 AF \n", - "173 Tonga 36.0 21 5 1.1 OC \n", - "174 Trinidad & Tobago 197.0 156 7 6.4 NA \n", - "175 Tunisia 51.0 3 20 1.3 AF \n", - "176 Turkey 51.0 22 7 1.4 AS \n", - "177 Turkmenistan 19.0 71 32 2.2 AS \n", - "178 Tuvalu 6.0 41 9 1.0 OC \n", - "179 Uganda 45.0 9 0 8.3 AF \n", - "180 Ukraine 206.0 237 45 8.9 EU \n", - "181 United Arab Emirates 16.0 135 5 2.8 AS \n", - "182 United Kingdom 219.0 126 195 10.4 EU \n", - "183 Tanzania 36.0 6 1 5.7 AF \n", - "184 USA 249.0 158 84 8.7 NA \n", - "185 Uruguay 115.0 35 220 6.6 SA \n", - "186 Uzbekistan 25.0 101 8 2.4 AS \n", - "187 Vanuatu 21.0 18 11 0.9 OC \n", - "188 Venezuela 333.0 100 3 7.7 SA \n", - "189 Vietnam 111.0 2 1 2.0 AS \n", - "190 Yemen 6.0 0 0 0.1 AS \n", - "191 Zambia 32.0 19 4 2.5 AF \n", - "192 Zimbabwe 64.0 18 4 4.7 AF \n", - "\n", - " cont_AS cont_EU cont_NA cont_OC cont_SA beer_level \n", - "0 1 0 0 0 0 low \n", - "1 0 1 0 0 0 low \n", - "2 0 0 0 0 0 low \n", - "3 0 1 0 0 0 high \n", - "4 0 0 0 0 0 high \n", - "5 0 0 1 0 0 med \n", - "6 0 0 0 0 1 med \n", - "7 0 1 0 0 0 low \n", - "8 0 0 0 1 0 high \n", - "9 0 1 0 0 0 high \n", - "10 0 1 0 0 0 low \n", - "11 0 0 1 0 0 med \n", - "12 1 0 0 0 0 low \n", - "13 1 0 0 0 0 low \n", - "14 0 0 1 0 0 med \n", - "15 0 1 0 0 0 med \n", - "16 0 1 0 0 0 high \n", - "17 0 0 1 0 0 high \n", - "18 0 0 0 0 0 low \n", - "19 1 0 0 0 0 low \n", - "20 0 0 0 0 1 med \n", - "21 0 1 0 0 0 low \n", - "22 0 0 0 0 0 med \n", - "23 0 0 0 0 1 high \n", - "24 1 0 0 0 0 low \n", - "25 0 1 0 0 0 high \n", - "26 0 0 0 0 0 low \n", - "27 0 0 0 0 0 low \n", - "28 0 0 0 0 0 low \n", - "29 0 0 0 0 0 med \n", - "30 1 0 0 0 0 low \n", - "31 0 0 0 0 0 med \n", - "32 0 0 1 0 0 high \n", - "33 0 0 0 0 0 low \n", - "34 0 0 0 0 0 low \n", - "35 0 0 0 0 1 med \n", - "36 1 0 0 0 0 low \n", - "37 0 0 0 0 1 med \n", - "38 0 0 0 0 0 low \n", - "39 0 0 0 0 0 low \n", - "40 0 0 0 1 0 low \n", - "41 0 0 1 0 0 med \n", - "42 0 1 0 0 0 high \n", - "43 0 0 1 0 0 low \n", - "44 0 1 0 0 0 med \n", - "45 0 1 0 0 0 high \n", - "46 1 0 0 0 0 low \n", - "47 0 0 0 0 0 low \n", - "48 0 1 0 0 0 high \n", - "49 0 0 0 0 0 low \n", - "50 0 0 1 0 0 low \n", - "51 0 0 1 0 0 med \n", - "52 0 0 0 0 1 med \n", - "53 0 0 0 0 0 low \n", - "54 0 0 1 0 0 low \n", - "55 0 0 0 0 0 low \n", - "56 0 0 0 0 0 low \n", - "57 0 1 0 0 0 high \n", - "58 0 0 0 0 0 low \n", - "59 0 0 0 1 0 low \n", - "60 0 1 0 0 0 high \n", - "61 0 1 0 0 0 med \n", - "62 0 0 0 0 0 high \n", - "63 0 0 0 0 0 low \n", - "64 0 1 0 0 0 low \n", - "65 0 1 0 0 0 high \n", - "66 0 0 0 0 0 low \n", - "67 0 1 0 0 0 med \n", - "68 0 0 1 0 0 med \n", - "69 0 0 1 0 0 low \n", - "70 0 0 0 0 0 low \n", - "71 0 0 0 0 0 low \n", - "72 0 0 0 0 1 low \n", - "73 0 0 1 0 0 low \n", - "74 0 0 1 0 0 low \n", - "75 0 1 0 0 0 high \n", - "76 0 1 0 0 0 high \n", - "77 1 0 0 0 0 low \n", - "78 1 0 0 0 0 low \n", - "79 1 0 0 0 0 low \n", - "80 1 0 0 0 0 low \n", - "81 0 1 0 0 0 high \n", - "82 1 0 0 0 0 low \n", - "83 0 1 0 0 0 low \n", - "84 0 0 1 0 0 low \n", - "85 1 0 0 0 0 low \n", - "86 1 0 0 0 0 low \n", - "87 1 0 0 0 0 med \n", - "88 0 0 0 0 0 low \n", - "89 0 0 0 1 0 low \n", - "90 1 0 0 0 0 low \n", - "91 1 0 0 0 0 low \n", - "92 1 0 0 0 0 low \n", - "93 0 1 0 0 0 high \n", - "94 1 0 0 0 0 low \n", - "95 0 0 0 0 0 low \n", - "96 0 0 0 0 0 low \n", - "97 0 0 0 0 0 low \n", - "98 0 1 0 0 0 high \n", - "99 0 1 0 0 0 high \n", - "100 0 0 0 0 0 low \n", - "101 0 0 0 0 0 low \n", - "102 1 0 0 0 0 low \n", - "103 1 0 0 0 0 low \n", - "104 0 0 0 0 0 low \n", - "105 0 1 0 0 0 med \n", - "106 0 0 0 1 0 low \n", - "107 0 0 0 0 0 low \n", - "108 0 0 0 0 0 low \n", - "109 0 0 1 0 0 high \n", - "110 0 0 0 1 0 low \n", - "111 0 1 0 0 0 low \n", - "112 1 0 0 0 0 low \n", - "113 0 1 0 0 0 low \n", - "114 0 0 0 0 0 low \n", - "115 0 0 0 0 0 low \n", - "116 1 0 0 0 0 low \n", - "117 0 0 0 0 0 high \n", - "118 0 0 0 1 0 low \n", - "119 1 0 0 0 0 low \n", - "120 0 1 0 0 0 high \n", - "121 0 0 0 1 0 high \n", - "122 0 0 1 0 0 low \n", - "123 0 0 0 0 0 low \n", - "124 0 0 0 0 0 low \n", - "125 0 0 0 1 0 med \n", - "126 0 1 0 0 0 med \n", - "127 1 0 0 0 0 low \n", - "128 1 0 0 0 0 low \n", - "129 0 0 0 1 0 high \n", - "130 0 0 1 0 0 high \n", - "131 0 0 0 1 0 low \n", - "132 0 0 0 0 1 high \n", - "133 0 0 0 0 1 med \n", - "134 1 0 0 0 0 low \n", - "135 0 1 0 0 0 high \n", - "136 0 1 0 0 0 med \n", - "137 1 0 0 0 0 low \n", - "138 1 0 0 0 0 med \n", - "139 0 1 0 0 0 med \n", - "140 0 1 0 0 0 high \n", - "141 1 0 0 0 0 high \n", - "142 0 0 0 0 0 low \n", - "143 0 0 1 0 0 med \n", - "144 0 0 1 0 0 med \n", - "145 0 0 1 0 0 med \n", - "146 0 0 0 1 0 med \n", - "147 0 1 0 0 0 low \n", - "148 0 0 0 0 0 low \n", - "149 1 0 0 0 0 low \n", - "150 0 0 0 0 0 low \n", - "151 0 1 0 0 0 high \n", - "152 0 0 0 0 0 med \n", - "153 0 0 0 0 0 low \n", - "154 1 0 0 0 0 low \n", - "155 0 1 0 0 0 med \n", - "156 0 1 0 0 0 high \n", - "157 0 0 0 1 0 low \n", - "158 0 0 0 0 0 low \n", - "159 0 0 0 0 0 high \n", - "160 0 1 0 0 0 high \n", - "161 1 0 0 0 0 low \n", - "162 0 0 0 0 0 low \n", - "163 0 0 0 0 1 med \n", - "164 0 0 0 0 0 low \n", - "165 0 1 0 0 0 med \n", - "166 0 1 0 0 0 med \n", - "167 1 0 0 0 0 low \n", - "168 1 0 0 0 0 low \n", - "169 1 0 0 0 0 low \n", - "170 0 1 0 0 0 med \n", - "171 1 0 0 0 0 low \n", - "172 0 0 0 0 0 low \n", - "173 0 0 0 1 0 low \n", - "174 0 0 1 0 0 med \n", - "175 0 0 0 0 0 low \n", - "176 1 0 0 0 0 low \n", - "177 1 0 0 0 0 low \n", - "178 0 0 0 1 0 low \n", - "179 0 0 0 0 0 low \n", - "180 0 1 0 0 0 high \n", - "181 1 0 0 0 0 low \n", - "182 0 1 0 0 0 high \n", - "183 0 0 0 0 0 low \n", - "184 0 0 1 0 0 high \n", - "185 0 0 0 0 1 med \n", - "186 1 0 0 0 0 low \n", - "187 0 0 0 1 0 low \n", - "188 0 0 0 0 1 high \n", - "189 1 0 0 0 0 med \n", - "190 1 0 0 0 0 low \n", - "191 0 0 0 0 0 low \n", - "192 0 0 0 0 0 low \n" - ] - } - ], + "outputs": [], "source": [ "# Change the maximum number of rows and columns printed ('None' means unlimited).\n", "pd.set_option('max_rows', None) # Default is 60 rows\n", @@ -17013,7 +4714,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -17024,405 +4725,9 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " country beer spirit wine liters continent \\\n", - "0 Afghanistan 0.0 0 0 0.0 AS \n", - "1 Albania 89.0 132 54 4.9 EU \n", - "2 Algeria 25.0 0 14 0.7 AF \n", - "3 Andorra 245.0 138 312 12.4 EU \n", - "4 Angola 217.0 57 45 5.9 AF \n", - "5 Antigua & Barbuda 102.0 128 45 4.9 NA \n", - "6 Argentina 193.0 25 221 8.3 SA \n", - "7 Armenia 21.0 179 11 3.8 EU \n", - "8 Australia 261.0 72 212 10.4 OC \n", - "9 Austria 279.0 75 191 9.7 EU \n", - "10 Azerbaijan 21.0 46 5 1.3 EU \n", - "11 Bahamas 122.0 176 51 6.3 NA \n", - "12 Bahrain 42.0 63 7 2.0 AS \n", - "13 Bangladesh 0.0 0 0 0.0 AS \n", - "14 Barbados 143.0 173 36 6.3 NA \n", - "15 Belarus 142.0 373 42 14.4 EU \n", - "16 Belgium 295.0 84 212 10.5 EU \n", - "17 Belize 263.0 114 8 6.8 NA \n", - "18 Benin 34.0 4 13 1.1 AF \n", - "19 Bhutan 23.0 0 0 0.4 AS \n", - "20 Bolivia 167.0 41 8 3.8 SA \n", - "21 Bosnia-Herzegovina 76.0 173 8 4.6 EU \n", - "22 Botswana 173.0 35 35 5.4 AF \n", - "23 Brazil 245.0 145 16 7.2 SA \n", - "24 Brunei 31.0 2 1 0.6 AS \n", - "25 Bulgaria 231.0 252 94 10.3 EU \n", - "26 Burkina Faso 25.0 7 7 4.3 AF \n", - "27 Burundi 88.0 0 0 6.3 AF \n", - "28 Cote d'Ivoire 37.0 1 7 4.0 AF \n", - "29 Cabo Verde 144.0 56 16 4.0 AF \n", - "30 Cambodia 57.0 65 1 2.2 AS \n", - "31 Cameroon 147.0 1 4 5.8 AF \n", - "32 Canada 240.0 122 100 8.2 NA \n", - "33 Central African Republic 17.0 2 1 1.8 AF \n", - "34 Chad 15.0 1 1 0.4 AF \n", - "35 Chile 130.0 124 172 7.6 SA \n", - "36 China 79.0 192 8 5.0 AS \n", - "37 Colombia 159.0 76 3 4.2 SA \n", - "38 Comoros 1.0 3 1 0.1 AF \n", - "39 Congo 76.0 1 9 1.7 AF \n", - "40 Cook Islands 0.0 254 74 5.9 OC \n", - "41 Costa Rica 149.0 87 11 4.4 NA \n", - "42 Croatia 230.0 87 254 10.2 EU \n", - "43 Cuba 93.0 137 5 4.2 NA \n", - "44 Cyprus 192.0 154 113 8.2 EU \n", - "45 Czech Republic 361.0 170 134 11.8 EU \n", - "46 North Korea 0.0 0 0 0.0 AS \n", - "47 DR Congo 32.0 3 1 2.3 AF \n", - "48 Denmark 224.0 81 278 10.4 EU \n", - "49 Djibouti 15.0 44 3 1.1 AF \n", - "50 Dominica 52.0 286 26 6.6 NA \n", - "51 Dominican Republic 193.0 147 9 6.2 NA \n", - "52 Ecuador 162.0 74 3 4.2 SA \n", - "53 Egypt 6.0 4 1 0.2 AF \n", - "54 El Salvador 52.0 69 2 2.2 NA \n", - "55 Equatorial Guinea 92.0 0 233 5.8 AF \n", - "56 Eritrea 18.0 0 0 0.5 AF \n", - "57 Estonia 224.0 194 59 9.5 EU \n", - "58 Ethiopia 20.0 3 0 0.7 AF \n", - "59 Fiji 77.0 35 1 2.0 OC \n", - "60 Finland 263.0 133 97 10.0 EU \n", - "61 France 127.0 151 370 11.8 EU \n", - "62 Gabon 347.0 98 59 8.9 AF \n", - "63 Gambia 8.0 0 1 2.4 AF \n", - "64 Georgia 52.0 100 149 5.4 EU \n", - "65 Germany 346.0 117 175 11.3 EU \n", - "66 Ghana 31.0 3 10 1.8 AF \n", - "67 Greece 133.0 112 218 8.3 EU \n", - "68 Grenada 199.0 438 28 11.9 NA \n", - "69 Guatemala 53.0 69 2 2.2 NA \n", - "70 Guinea 9.0 0 2 0.2 AF \n", - "71 Guinea-Bissau 28.0 31 21 2.5 AF \n", - "72 Guyana 93.0 302 1 7.1 SA \n", - "73 Haiti 1.0 326 1 5.9 NA \n", - "74 Honduras 69.0 98 2 3.0 NA \n", - "75 Hungary 234.0 215 185 11.3 EU \n", - "76 Iceland 233.0 61 78 6.6 EU \n", - "77 India 9.0 114 0 2.2 AS \n", - "78 Indonesia 5.0 1 0 0.1 AS \n", - "79 Iran 0.0 0 0 0.0 AS \n", - "80 Iraq 9.0 3 0 0.2 AS \n", - "81 Ireland 313.0 118 165 11.4 EU \n", - "82 Israel 63.0 69 9 2.5 AS \n", - "83 Italy 85.0 42 237 6.5 EU \n", - "84 Jamaica 82.0 97 9 3.4 NA \n", - "85 Japan 77.0 202 16 7.0 AS \n", - "86 Jordan 6.0 21 1 0.5 AS \n", - "87 Kazakhstan 124.0 246 12 6.8 AS \n", - "88 Kenya 58.0 22 2 1.8 AF \n", - "89 Kiribati 21.0 34 1 1.0 OC \n", - "90 Kuwait 0.0 0 0 0.0 AS \n", - "91 Kyrgyzstan 31.0 97 6 2.4 AS \n", - "92 Laos 62.0 0 123 6.2 AS \n", - "93 Latvia 281.0 216 62 10.5 EU \n", - "94 Lebanon 20.0 55 31 1.9 AS \n", - "95 Lesotho 82.0 29 0 2.8 AF \n", - "96 Liberia 19.0 152 2 3.1 AF \n", - "97 Libya 0.0 0 0 0.0 AF \n", - "98 Lithuania 343.0 244 56 12.9 EU \n", - "99 Luxembourg 236.0 133 271 11.4 EU \n", - "100 Madagascar 26.0 15 4 0.8 AF \n", - "101 Malawi 8.0 11 1 1.5 AF \n", - "102 Malaysia 13.0 4 0 0.3 AS \n", - "103 Maldives 0.0 0 0 0.0 AS \n", - "104 Mali 5.0 1 1 0.6 AF \n", - "105 Malta 149.0 100 120 6.6 EU \n", - "106 Marshall Islands 0.0 0 0 0.0 OC \n", - "107 Mauritania 0.0 0 0 0.0 AF \n", - "108 Mauritius 98.0 31 18 2.6 AF \n", - "109 Mexico 238.0 68 5 5.5 NA \n", - "110 Micronesia 62.0 50 18 2.3 OC \n", - "111 Monaco 0.0 0 0 0.0 EU \n", - "112 Mongolia 77.0 189 8 4.9 AS \n", - "113 Montenegro 31.0 114 128 4.9 EU \n", - "114 Morocco 12.0 6 10 0.5 AF \n", - "115 Mozambique 47.0 18 5 1.3 AF \n", - "116 Myanmar 5.0 1 0 0.1 AS \n", - "117 Namibia 376.0 3 1 6.8 AF \n", - "118 Nauru 49.0 0 8 1.0 OC \n", - "119 Nepal 5.0 6 0 0.2 AS \n", - "120 Netherlands 251.0 88 190 9.4 EU \n", - "121 New Zealand 203.0 79 175 9.3 OC \n", - "122 Nicaragua 78.0 118 1 3.5 NA \n", - "123 Niger 3.0 2 1 0.1 AF \n", - "124 Nigeria 42.0 5 2 9.1 AF \n", - "125 Niue 188.0 200 7 7.0 OC \n", - "126 Norway 169.0 71 129 6.7 EU \n", - "127 Oman 22.0 16 1 0.7 AS \n", - "128 Pakistan 0.0 0 0 0.0 AS \n", - "129 Palau 306.0 63 23 6.9 OC \n", - "130 Panama 285.0 104 18 7.2 NA \n", - "131 Papua New Guinea 44.0 39 1 1.5 OC \n", - "132 Paraguay 213.0 117 74 7.3 SA \n", - "133 Peru 163.0 160 21 6.1 SA \n", - "134 Philippines 71.0 186 1 4.6 AS \n", - "135 Poland 343.0 215 56 10.9 EU \n", - "136 Portugal 194.0 67 339 11.0 EU \n", - "137 Qatar 1.0 42 7 0.9 AS \n", - "138 South Korea 140.0 16 9 9.8 AS \n", - "139 Moldova 109.0 226 18 6.3 EU \n", - "140 Romania 297.0 122 167 10.4 EU \n", - "141 Russian Federation 247.0 326 73 11.5 AS \n", - "142 Rwanda 43.0 2 0 6.8 AF \n", - "143 St. Kitts & Nevis 194.0 205 32 7.7 NA \n", - "144 St. Lucia 171.0 315 71 10.1 NA \n", - "145 St. Vincent & the Grenadines 120.0 221 11 6.3 NA \n", - "146 Samoa 105.0 18 24 2.6 OC \n", - "147 San Marino 0.0 0 0 0.0 EU \n", - "148 Sao Tome & Principe 56.0 38 140 4.2 AF \n", - "149 Saudi Arabia 0.0 5 0 0.1 AS \n", - "150 Senegal 9.0 1 7 0.3 AF \n", - "151 Serbia 283.0 131 127 9.6 EU \n", - "152 Seychelles 157.0 25 51 4.1 AF \n", - "153 Sierra Leone 25.0 3 2 6.7 AF \n", - "154 Singapore 60.0 12 11 1.5 AS \n", - "155 Slovakia 196.0 293 116 11.4 EU \n", - "156 Slovenia 270.0 51 276 10.6 EU \n", - "157 Solomon Islands 56.0 11 1 1.2 OC \n", - "158 Somalia 0.0 0 0 0.0 AF \n", - "159 South Africa 225.0 76 81 8.2 AF \n", - "160 Spain 284.0 157 112 10.0 EU \n", - "161 Sri Lanka 16.0 104 0 2.2 AS \n", - "162 Sudan 8.0 13 0 1.7 AF \n", - "163 Suriname 128.0 178 7 5.6 SA \n", - "164 Swaziland 90.0 2 2 4.7 AF \n", - "165 Sweden 152.0 60 186 7.2 EU \n", - "166 Switzerland 185.0 100 280 10.2 EU \n", - "167 Syria 5.0 35 16 1.0 AS \n", - "168 Tajikistan 2.0 15 0 0.3 AS \n", - "169 Thailand 99.0 258 1 6.4 AS \n", - "170 Macedonia 106.0 27 86 3.9 EU \n", - "171 Timor-Leste 1.0 1 4 0.1 AS \n", - "172 Togo 36.0 2 19 1.3 AF \n", - "173 Tonga 36.0 21 5 1.1 OC \n", - "174 Trinidad & Tobago 197.0 156 7 6.4 NA \n", - "175 Tunisia 51.0 3 20 1.3 AF \n", - "176 Turkey 51.0 22 7 1.4 AS \n", - "177 Turkmenistan 19.0 71 32 2.2 AS \n", - "178 Tuvalu 6.0 41 9 1.0 OC \n", - "179 Uganda 45.0 9 0 8.3 AF \n", - "180 Ukraine 206.0 237 45 8.9 EU \n", - "181 United Arab Emirates 16.0 135 5 2.8 AS \n", - "182 United Kingdom 219.0 126 195 10.4 EU \n", - "183 Tanzania 36.0 6 1 5.7 AF \n", - "184 USA 249.0 158 84 8.7 NA \n", - "185 Uruguay 115.0 35 220 6.6 SA \n", - "186 Uzbekistan 25.0 101 8 2.4 AS \n", - "187 Vanuatu 21.0 18 11 0.9 OC \n", - "188 Venezuela 333.0 100 3 7.7 SA \n", - "189 Vietnam 111.0 2 1 2.0 AS \n", - "190 Yemen 6.0 0 0 0.1 AS \n", - "191 Zambia 32.0 19 4 2.5 AF \n", - "192 Zimbabwe 64.0 18 4 4.7 AF \n", - "\n", - " cont_AS cont_EU cont_NA cont_OC cont_SA beer_level \n", - "0 1 0 0 0 0 low \n", - "1 0 1 0 0 0 low \n", - "2 0 0 0 0 0 low \n", - "3 0 1 0 0 0 high \n", - "4 0 0 0 0 0 high \n", - "5 0 0 1 0 0 med \n", - "6 0 0 0 0 1 med \n", - "7 0 1 0 0 0 low \n", - "8 0 0 0 1 0 high \n", - "9 0 1 0 0 0 high \n", - "10 0 1 0 0 0 low \n", - "11 0 0 1 0 0 med \n", - "12 1 0 0 0 0 low \n", - "13 1 0 0 0 0 low \n", - "14 0 0 1 0 0 med \n", - "15 0 1 0 0 0 med \n", - "16 0 1 0 0 0 high \n", - "17 0 0 1 0 0 high \n", - "18 0 0 0 0 0 low \n", - "19 1 0 0 0 0 low \n", - "20 0 0 0 0 1 med \n", - "21 0 1 0 0 0 low \n", - "22 0 0 0 0 0 med \n", - "23 0 0 0 0 1 high \n", - "24 1 0 0 0 0 low \n", - "25 0 1 0 0 0 high \n", - "26 0 0 0 0 0 low \n", - "27 0 0 0 0 0 low \n", - "28 0 0 0 0 0 low \n", - "29 0 0 0 0 0 med \n", - "30 1 0 0 0 0 low \n", - "31 0 0 0 0 0 med \n", - "32 0 0 1 0 0 high \n", - "33 0 0 0 0 0 low \n", - "34 0 0 0 0 0 low \n", - "35 0 0 0 0 1 med \n", - "36 1 0 0 0 0 low \n", - "37 0 0 0 0 1 med \n", - "38 0 0 0 0 0 low \n", - "39 0 0 0 0 0 low \n", - "40 0 0 0 1 0 low \n", - "41 0 0 1 0 0 med \n", - "42 0 1 0 0 0 high \n", - "43 0 0 1 0 0 low \n", - "44 0 1 0 0 0 med \n", - "45 0 1 0 0 0 high \n", - "46 1 0 0 0 0 low \n", - "47 0 0 0 0 0 low \n", - "48 0 1 0 0 0 high \n", - "49 0 0 0 0 0 low \n", - "50 0 0 1 0 0 low \n", - "51 0 0 1 0 0 med \n", - "52 0 0 0 0 1 med \n", - "53 0 0 0 0 0 low \n", - "54 0 0 1 0 0 low \n", - "55 0 0 0 0 0 low \n", - "56 0 0 0 0 0 low \n", - "57 0 1 0 0 0 high \n", - "58 0 0 0 0 0 low \n", - "59 0 0 0 1 0 low \n", - "60 0 1 0 0 0 high \n", - "61 0 1 0 0 0 med \n", - "62 0 0 0 0 0 high \n", - "63 0 0 0 0 0 low \n", - "64 0 1 0 0 0 low \n", - "65 0 1 0 0 0 high \n", - "66 0 0 0 0 0 low \n", - "67 0 1 0 0 0 med \n", - "68 0 0 1 0 0 med \n", - "69 0 0 1 0 0 low \n", - "70 0 0 0 0 0 low \n", - "71 0 0 0 0 0 low \n", - "72 0 0 0 0 1 low \n", - "73 0 0 1 0 0 low \n", - "74 0 0 1 0 0 low \n", - "75 0 1 0 0 0 high \n", - "76 0 1 0 0 0 high \n", - "77 1 0 0 0 0 low \n", - "78 1 0 0 0 0 low \n", - "79 1 0 0 0 0 low \n", - "80 1 0 0 0 0 low \n", - "81 0 1 0 0 0 high \n", - "82 1 0 0 0 0 low \n", - "83 0 1 0 0 0 low \n", - "84 0 0 1 0 0 low \n", - "85 1 0 0 0 0 low \n", - "86 1 0 0 0 0 low \n", - "87 1 0 0 0 0 med \n", - "88 0 0 0 0 0 low \n", - "89 0 0 0 1 0 low \n", - "90 1 0 0 0 0 low \n", - "91 1 0 0 0 0 low \n", - "92 1 0 0 0 0 low \n", - "93 0 1 0 0 0 high \n", - "94 1 0 0 0 0 low \n", - "95 0 0 0 0 0 low \n", - "96 0 0 0 0 0 low \n", - "97 0 0 0 0 0 low \n", - "98 0 1 0 0 0 high \n", - "99 0 1 0 0 0 high \n", - "100 0 0 0 0 0 low \n", - "101 0 0 0 0 0 low \n", - "102 1 0 0 0 0 low \n", - "103 1 0 0 0 0 low \n", - "104 0 0 0 0 0 low \n", - "105 0 1 0 0 0 med \n", - "106 0 0 0 1 0 low \n", - "107 0 0 0 0 0 low \n", - "108 0 0 0 0 0 low \n", - "109 0 0 1 0 0 high \n", - "110 0 0 0 1 0 low \n", - "111 0 1 0 0 0 low \n", - "112 1 0 0 0 0 low \n", - "113 0 1 0 0 0 low \n", - "114 0 0 0 0 0 low \n", - "115 0 0 0 0 0 low \n", - "116 1 0 0 0 0 low \n", - "117 0 0 0 0 0 high \n", - "118 0 0 0 1 0 low \n", - "119 1 0 0 0 0 low \n", - "120 0 1 0 0 0 high \n", - "121 0 0 0 1 0 high \n", - "122 0 0 1 0 0 low \n", - "123 0 0 0 0 0 low \n", - "124 0 0 0 0 0 low \n", - "125 0 0 0 1 0 med \n", - "126 0 1 0 0 0 med \n", - "127 1 0 0 0 0 low \n", - "128 1 0 0 0 0 low \n", - "129 0 0 0 1 0 high \n", - "130 0 0 1 0 0 high \n", - "131 0 0 0 1 0 low \n", - "132 0 0 0 0 1 high \n", - "133 0 0 0 0 1 med \n", - "134 1 0 0 0 0 low \n", - "135 0 1 0 0 0 high \n", - "136 0 1 0 0 0 med \n", - "137 1 0 0 0 0 low \n", - "138 1 0 0 0 0 med \n", - "139 0 1 0 0 0 med \n", - "140 0 1 0 0 0 high \n", - "141 1 0 0 0 0 high \n", - "142 0 0 0 0 0 low \n", - "143 0 0 1 0 0 med \n", - "144 0 0 1 0 0 med \n", - "145 0 0 1 0 0 med \n", - "146 0 0 0 1 0 med \n", - "147 0 1 0 0 0 low \n", - "148 0 0 0 0 0 low \n", - "149 1 0 0 0 0 low \n", - "150 0 0 0 0 0 low \n", - "151 0 1 0 0 0 high \n", - "152 0 0 0 0 0 med \n", - "153 0 0 0 0 0 low \n", - "154 1 0 0 0 0 low \n", - "155 0 1 0 0 0 med \n", - "156 0 1 0 0 0 high \n", - "157 0 0 0 1 0 low \n", - "158 0 0 0 0 0 low \n", - "159 0 0 0 0 0 high \n", - "160 0 1 0 0 0 high \n", - "161 1 0 0 0 0 low \n", - "162 0 0 0 0 0 low \n", - "163 0 0 0 0 1 med \n", - "164 0 0 0 0 0 low \n", - "165 0 1 0 0 0 med \n", - "166 0 1 0 0 0 med \n", - "167 1 0 0 0 0 low \n", - "168 1 0 0 0 0 low \n", - "169 1 0 0 0 0 low \n", - "170 0 1 0 0 0 med \n", - "171 1 0 0 0 0 low \n", - "172 0 0 0 0 0 low \n", - "173 0 0 0 1 0 low \n", - "174 0 0 1 0 0 med \n", - "175 0 0 0 0 0 low \n", - "176 1 0 0 0 0 low \n", - "177 1 0 0 0 0 low \n", - "178 0 0 0 1 0 low \n", - "179 0 0 0 0 0 low \n", - "180 0 1 0 0 0 high \n", - "181 1 0 0 0 0 low \n", - "182 0 1 0 0 0 high \n", - "183 0 0 0 0 0 low \n", - "184 0 0 1 0 0 high \n", - "185 0 0 0 0 1 med \n", - "186 1 0 0 0 0 low \n", - "187 0 0 0 1 0 low \n", - "188 0 0 0 0 1 high \n", - "189 1 0 0 0 0 med \n", - "190 1 0 0 0 0 low \n", - "191 0 0 0 0 0 low \n", - "192 0 0 0 0 0 low \n" - ] - } - ], + "outputs": [], "source": [ "# Change the options temporarily (settings are restored when you exit the \"with\" block).\n", "with pd.option_context('max_rows', None, 'max_columns', None):\n", @@ -17431,9 +4736,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "\n", "### Summary\n", @@ -17471,9 +4774,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.8.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Data Visualization and Pipelines/.ipynb_checkpoints/Data Visualization_and_Pipeline-checkpoint.ipynb b/Data Visualization and Pipelines/.ipynb_checkpoints/Data Visualization_and_Pipeline-checkpoint.ipynb new file mode 100644 index 0000000..ad870d3 --- /dev/null +++ b/Data Visualization and Pipelines/.ipynb_checkpoints/Data Visualization_and_Pipeline-checkpoint.ipynb @@ -0,0 +1,3762 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **Jupyter slideshow:** This notebook can be displayed as slides. To view it as a slideshow in your browser, run the following cell:\n", + "\n", + "> `> jupyter nbconvert [this_notebook.ipynb] --to slides --post serve`\n", + " \n", + "> To toggle off the slideshow cell formatting, click the `CellToolbar` button, then `View > Cell Toolbar > None`." + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "# ! jupyter nbconvert this_notebook.ipynb --to slides --post serve" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "# Data Visualization and Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "\n", + "- **Practice** using different types of plots.\n", + "- **Use** Pandas methods for plotting.\n", + "- **Create** line plots, bar plots, histograms, and box plots.\n", + "- **Know** when to use Seaborn or advanced Matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets review the data science lifecycle " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Science is an iterative process" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "## Data Visualization " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson Guide\n", + "\n", + "- [Line Plots](#line-plots)\n", + "- [Bar Plots](#bar-plots)\n", + "- [Histograms](#histograms)\n", + " - [Grouped Histograms](#grouped-histograms)\n", + " \n", + " \n", + "- [Box Plots](#box-plots)\n", + " - [Grouped Box Plots](#grouped-box-plots)\n", + " \n", + "- [Scatter Plots](#scatter-plots)\n", + "- [Using Seaborn](#using-seaborn)\n", + "- [OPTIONAL: Understanding Matplotlib (Figures, Subplots, and Axes)](#matplotlib)\n", + "- [OPTIONAL: Additional Topics](#additional-topics)\n", + "\n", + "- [Summary](#summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why Use Data Visualization?\n", + "\n", + "---\n", + "\n", + "Because of the way the human brain processes information, charts or graphs that visualize large amounts of complex data are easier to understand than spreadsheets or reports. \n", + "\n", + "Data visualization is a quick, easy way to convey concepts in a universal manner — and you can experiment with different scenarios by making slight adjustments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pandas Plotting Documentation\n", + "\n", + "[Link to Documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import HTML\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use('fivethirtyeight')\n", + "%matplotlib inline\n", + "\n", + "# Increase default figure and font sizes for easier viewing.\n", + "plt.rcParams['figure.figsize'] = (8, 6)\n", + "plt.rcParams['font.size'] = 14" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choosing the right type of visualization\n", + "\n", + "The choice of visualization should depend what you are trying to show. Here is a helpful flowchart that you can use to determine the best type of visualizations.\n", + "\n", + "![Chart Suggestions](images/chart_suggestions.jpg)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Importance of visualization \n", + "\n", + "- Given the same data, different visualization styles can convey different messages \n", + "- Examples here:\n", + "\n", + "https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load in data sets for visualization examples.\n", + "\n", + "The Boston data dictionary can be found [here](https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.names)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the Boston housing data.\n", + "housing_csv = 'data/boston_housing_data.csv'\n", + "housing = pd.read_csv(housing_csv)\n", + "\n", + "# Read in the drinks data.\n", + "drink_cols = ['country', 'beer', 'spirit', 'wine', 'liters', 'continent']\n", + "url = 'data/drinks.csv'\n", + "drinks = pd.read_csv(url, header=0, names=drink_cols, na_filter=False)\n", + "\n", + "# Read in the ufo data.\n", + "ufo = pd.read_csv('data/ufo.csv')\n", + "ufo['Time'] = pd.to_datetime(ufo.Time)\n", + "ufo['Year'] = ufo.Time.dt.year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understand each DataSet well before we try to visualize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", + "0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296.0 \n", + "1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242.0 \n", + "2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242.0 \n", + "3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222.0 \n", + "4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222.0 \n", + "\n", + " PTRATIO B LSTAT MEDV \n", + "0 15.3 396.90 4.98 24.0 \n", + "1 17.8 396.90 9.14 21.6 \n", + "2 17.8 392.83 4.03 34.7 \n", + "3 18.7 394.63 2.94 33.4 \n", + "4 18.7 396.90 5.33 36.2 " + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "housing.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeerspiritwineliterscontinent
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" + ], + "text/plain": [ + " country beer spirit wine liters continent\n", + "0 Afghanistan 0 0 0 0.0 AS\n", + "1 Albania 89 132 54 4.9 EU\n", + "2 Algeria 25 0 14 0.7 AF\n", + "3 Andorra 245 138 312 12.4 EU\n", + "4 Angola 217 57 45 5.9 AF" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityColors ReportedShape ReportedStateTimeYear
0IthacaNaNTRIANGLENY1930-06-01 22:00:001930
1WillingboroNaNOTHERNJ1930-06-30 20:00:001930
2HolyokeNaNOVALCO1931-02-15 14:00:001931
3AbileneNaNDISKKS1931-06-01 13:00:001931
4New York Worlds FairNaNLIGHTNY1933-04-18 19:00:001933
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" + ], + "text/plain": [ + " City Colors Reported Shape Reported State \\\n", + "0 Ithaca NaN TRIANGLE NY \n", + "1 Willingboro NaN OTHER NJ \n", + "2 Holyoke NaN OVAL CO \n", + "3 Abilene NaN DISK KS \n", + "4 New York Worlds Fair NaN LIGHT NY \n", + "\n", + " Time Year \n", + "0 1930-06-01 22:00:00 1930 \n", + "1 1930-06-30 20:00:00 1930 \n", + "2 1931-02-15 14:00:00 1931 \n", + "3 1931-06-01 13:00:00 1931 \n", + "4 1933-04-18 19:00:00 1933 " + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ufo.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Line plots: Show the trend of a numerical variable over time\n", + "---\n", + "\n", + "- **Objective:** **Use** Pandas methods for plotting.\n", + "- **Objective:** **Create** line plots, bar plots, histograms, and box plots." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1930 2\n", + "1931 2\n", + "1933 1\n", + "1934 1\n", + "1935 1\n", + "1936 2\n", + "1937 2\n", + "1939 3\n", + "1941 2\n", + "1942 3\n", + "1943 5\n", + "1944 8\n", + "1945 9\n", + "1946 8\n", + "1947 41\n", + "1948 9\n", + "1949 19\n", + "1950 31\n", + "1951 21\n", + "1952 52\n", + "1953 36\n", + "1954 55\n", + "1955 33\n", + "1956 46\n", + "1957 78\n", + "1958 53\n", + "1959 57\n", + "1960 67\n", + "1961 50\n", + "1962 72\n", + " ... \n", + "1985 211\n", + "1986 186\n", + "1987 210\n", + "1988 232\n", + "1989 247\n", + "1990 237\n", + "1991 220\n", + "1992 245\n", + "1993 292\n", + "1994 406\n", + "1995 1344\n", + "1996 851\n", + "1997 1237\n", + "1998 1743\n", + "1999 2774\n", + "2000 2635\n", + "2001 2925\n", + "2002 2933\n", + "2003 3507\n", + "2004 3850\n", + "2005 3787\n", + "2006 3445\n", + "2007 4058\n", + "2008 4655\n", + "2009 4251\n", + "2010 4154\n", + "2011 5089\n", + "2012 7263\n", + "2013 7003\n", + "2014 5382\n", + "Name: Year, Length: 82, dtype: int64" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count the number of ufo reports each year (and sort by year).\n", + "ufo.Year.value_counts().sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare with line plot -- UFO sightings by year. (Ordering by year makes sense.)\n", + "ufo.Year.value_counts().sort_index().plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "NA 23\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.continent.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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oZezrJVS6A7Mobi9MnVfFrPPytceJiCQk3cBOJEYUpNl5bGxu2NiSXc28sK4+ShWJiHQthRCRGHLFoDQm9fCEjT28vIaNNZ4OniEiEr8UQkRiiGEY3HNkCznOve2XRq+fqfMq8fm7eXc3EZEuphAiEmN6ufw8MiYnbGxBSTN/VltGRBKMQohIDLryqHTO7OcKG3tweQ1f16otIyKJQyFEJAYZhsEz43PJcuxty9R7/Nw0vwq/2jIikiAUQkRiVP/MFB4eHd6WmbvDzYtfNESpIhGRyFIIEYlhPxiczql9w9syv1hWzZY6tWVEJP4phIjEMMMweHZ8btjddWtb/NystoyIJACFEJEYd3hWCg+Oyg4b+3C7m38Uqy0jIvFNIUQkDvzomAwm9naGjf18aTXb671RqkhE5NAphIjEAZth8NyEPNJD2jI1zX5uXVCptoyIxC2FEJE4cUR2CvefGN6WeW+rm39/1RilikREDo1CiEgcue64DMYVhLdl7l5cRUmD2jIiEn8UQkTiiM0weH5iLqn2vWNVzX5uW6irZUQk/iiEiMSZo3Ic/HxEeFvm7c1NvL5JbRkRiS8KISJx6PohmYzq5Qgbu2NRNWVNasuISPxQCBGJQ3abwfMT83CGvILL3T7uWFgdvaJERA6QQohInDom18Fdw8PbMq9/3cibX6stIyLxQSFEJI7dNDSTE3qGt2VuW1hFhdoyIhIHFEJE4pjDZjBtYh6OkFfyriYfdy9WW0ZEYp9CiEicO76Hg9uGZYWNWRsbmbVZbRkRiW0pB/oE0zTvAR4BplmWNTU4ZgAPANcCecBi4AbLstZGsFYR6cD/DMvirW8aWVvp2TN264IqTip0kevSzxoiEpsO6N3JNM1xBILG6laH7gRuA24ERgOlwPumaWYhIl3OaQ+0Zex7by3DzkYf9y5RW0ZEYlenQ4hpmjnAy8CPgMqQcQO4BXjUsqzplmV9BlwFZAFXRrZcEenI8Hwntw4Nz/2vfNnA+1ubolSRiMi+HchMyB+B1yzL+qjV+BFAb2D27gHLshqBucD4Q65QRDrtjuFZHJsb3mW9ZX4V1c2+KFUkItKxTq0JMU3zJ8BRwPfaOdw7+GtJq/ESoF9HX7O4uLgz31q6ic5HbDmU83HX4TZ+VOXCR6A3s63By81ztvDzouZIlZd09PqILTofsaGoqOiQv8Z+Q4hpmkcTWIg60bKslkP+jkGRKF4io7i4WOcjhhzq+SgCPvVW8+xndXvGZpak8MPhhZzaNzUCFSYXvT5ii85HYulMO+YkIB9Ya5qmxzRND3AKcH3w9+XBxxW2el4hsDNilYpIp90zIpuinPCfMW6cX0Vti9oyIhI7OhNCZgJDgeEhH8uAfwV/v4FA2Dhz9xNM00wFJgELIlyviHRCaorB8xNyCblYhi11Xh5cVhO1mkREWttvO8ayrCqgKnTMNM16oCJ4JQymaf4WuNc0zfUEQsl9QB3wSsQrFpFOGVvo4mdDMvj92vo9Y39eX8/FA9OY1McVxcpERAIitYvR48AzwDQCsyR9gLMsy6qN0NcXkYNw34nZDMqyh43dOL+SerVlRCQGGH6/v9u+WXV1dfd9M+k0LfSKLZE+H/N3ujl/VlnY2M+Oy+A3Y3Mj9j0SmV4fsUXnIzbl5OQY+39UW9rPWSTBTejt4ifHZoSNvfB5PYtK3FGqSEQkQCFEJAk8MDKbwzP3tmX8wNR5VTR6NDkpItGjECKSBDIdNn43IS9s7MsaD7/5VFfLiEj0KISIJIlT+rq4+uj0sLHn19axbJd2UhWR6FAIEUkiD47KoX/G3raMzw9T51Xi9qotIyLdTyFEJIlkO208OyH8qpj1VR4eX6m2jIh0P4UQkSRzRr9UvlsU3pb57Zo6VpapLSMi3UshRCQJ/Xp0Dn3S9778vX64fl4lzWrLiEg3UggRSUK5LhvPjA9vy3xe6eGp1drkWES6j0KISJI657A0zCPTwsaeWlXLmoqWKFUkIslGIUQkiT02NpeCtL1vAx4/3PDfSlp8asuISNdTCBFJYnkuG0+dFN6WWV3RwrNr6qJUkYgkE4UQkSR34eFpXHZEeFvm8ZU1rKtUW0ZEupZCiIjw+Lgc8lP3vh00++CGeZV41JYRkS6kECIi5KfaeWJcTtjYirIWpq1VW0ZEuo5CiIgAcMnANC48PDVs7JFPa9hQpbaMiHQNhRARAcAwDJ46KZc8l7FnzO2FqfOq8KotIyJdQCFERPYoSLPz2Njwq2WW7GrmhXX1UapIRBKZQoiIhLliUBrnHBbelnl4eQ0bazxRqkhEEpVCiIiEMQyDZ8bnkuPc25Zp9PqZOq8Sn19tGRGJHIUQEWmjT7qdR8aEXy2zoKSZv6xXW0ZEIkchRETadeVR6ZzZzxU29stlNXxdq7aMiESGQoiItGt3WybLsbctU+/xc/P8Kvxqy4hIBCiEiEiH+mem8NDo8LbMJzvc/G1DQ5QqEpFEohAiIvt01eB0TukT3pa5f2k1W+rUlhGRQ6MQIiL7ZBgGv5uQS0bK3rZMbYufWxaoLSMih0YhRET26/CsFH45Kjts7INtbl7+Um0ZETl4CiEi0ik/PiaDCb2dYWP3Lqlme703ShWJSLxTCBGRTrEZBs9NyCPNvrctU9Ps59aFasuIyMFRCBGRThuUncL9I8PbMu9tacLa2BilikQknimEiMgBue7YDMYWhLdl7lpURUmD2jIicmAUQkTkgNhtBs9PzCXVvnesqtnPbWrLiMgBUggRkQNWlOPg3hHhbZm3Nzfx+ia1ZUSk8xRCROSg3DAkk5H5jrCxOxZVU9aktoyIdI5CiIgcFLvNYNqkPJwh7yLlbh93LKyOXlEiElcUQkTkoB2T6+Cu4eFtmde/buTNr9WWEZH9UwgRkUNy09BMTugZ3pa5bWEVFWrLiMh+KISIyCFx2AymTczDEfJusqvJx91L1JYRkX1TCBGRQ3Z8Dwe3DcsKG7O+auTdLWrLiEjHFEJEJCL+Z1gWQ/JSwsZuXVBFldsXpYpEJNYphIhIRDjtgbZMyK1l2NHg4+dL1ZYRkfYphIhIxAzPd3Lr0PC2zMvFDczZ2hSlikQklimEiEhE3TE8i2Nyw9syN8+voqZZbRkRCacQIiIR5Qq2ZWwhbZltDV5+obaMiLSiECIiETeyl5OpQzLDxl7c0MAn29WWEZG9FEJEpEvcMyKbo7LD2zI3zq+irkVtGREJUAgRkS6RlmIwbWIuIV0ZNtd5eXBZTdRqEpHYohAiIl1mbKGLnx6XETb2p/X1zNvpjlJFIhJLFEJEpEvdPzKbI7LsYWM3zqukwaO2jEiyUwgRkS6VnmLjuYl5YWObar08tFxtGZFkpxAiIl1uYm8XPzkmvC3zwuf1LCpRW0YkmSmEiEi3eGBUNgMy97Zl/MDUeVU0evzRK0pEokohRES6RabDxnMTcsPGvqzx8JtP1ZYRSVYKISLSbU7pm8oPB6eHjT2/to5lu5qjVJGIRJNCiIh0q1+NzqF/xt62jM8PU+dV4vaqLSOSbBRCRKRbZTttPNuqLbO+ysPjK9WWEUk2CiEi0u3O6JfKd4vC2zK/XVPHyjK1ZUSSiUKIiETFr0fn0Cd971uQ1w83zKukWW0ZkaShECIiUZHrsvHM+PC2zNpKD0+vro1SRSLS3RRCRCRqzjksDfPItLCxJ1fV8llFS5QqEpHupBAiIlH12NhcCtL2vhV5gm2ZFp/aMiKJLmV/DzBN8wbgOmBgcGgt8LBlWe8EjxvAA8C1QB6wGLjBsqy1XVGwiCSWPJeNJ8fl8oOPKvaMrSpv4Xdr6rjthKwoViYiXa0zMyFbgbuAE4FRwIfATNM0hwWP3wncBtwIjAZKgfdN09S7h4h0ykUD07h0YHhb5rGVNayrVFtGJJHtdybEsqw3Wg393DTNnwEnmaa5BrgFeNSyrOkApmleRSCIXAn8IcL1ikiCeuKkHObucFPu9gHQ7AtsYvbe+b1IsRlRrk5EusIBrQkxTdNumua3gUxgAXAE0BuYvfsxlmU1AnOB8RGsU0QSXH6qnSfG5YSNLS9r4fdr66JUkYh0tf3OhACYpjkUWAikAnXApZZlrTFNc3fQKGn1lBKg376+ZnFx8QGWKl1J5yO2JOv5ON4Pp/Z08nH53remh5dXc6yvhIHp0VuomqznI1bpfMSGoqKiQ/4anQohwBfAcCAHuBz4m2mapx7KN45E8RIZxcXFOh8xJNnPxx/6exk3s4RKdyB0NPsNntyaw3/OzccehbZMsp+PWKPzkVg6FUIsy2oGvgx+utw0zdHArcCvg2OFwOaQpxQCOyNVpIgkj8J0O4+OzeW6uZV7xhaXNvOHdfVcPyQzipWJSKQd7D4hNsAFbCIQNs7cfcA0zVRgEoE1IyIiB8wclMbZh6WGjT20vIaNNZ4oVSQiXaEz+4Q8CrwDbAGyCFz1cipwvmVZftM0fwvca5rmemADcB+BdSOvdFXRIpLYDMPgmZNyGVdSQk1zoC3T6PUzdV4lb5+bj83Q1TIiiaAzMyG9gX8QWBfyAYG9QM61LGtW8PjjwDPANGAZ0Ac4y7Is3QBCRA5a3ww7j4wJv1pmQUkzf1lfH6WKRCTSDL+/+1acV1dXax/mGKSFXrFF52Mvv9/P5e+X88E2956xjBSD+ZcUMDCrs+vqD43OR2zR+YhNOTk5BzU9qXvHiEjMMgyDZ8fnkuXY+/5W7/Fz8/wquvMHKBHpGgohIhLT+mem8NDo8LbMJzvc/G1DQ5QqEpFIUQgRkZh31eB0TunjChu7f2k1W+p0tYxIPFMIEZGYZxgGv5uQS0bK3rZMbYufWxeoLSMSzxRCRCQuHJ6Vwi9HZYeNzdnm5pUv1ZYRiVcKISISN358TAYTejvDxu5dUs2OBm+UKhKRQ6EQIiJxw2YYPDchjzT73rZMdbPaMiLxSiFEROLKoOwU7h8Z3pZ5d0sTr25sjFJFInKwFEJEJO5cd2wGY3qFt2XuWlxFidoyInFFIURE4o7dZvD8xFxc9r1jlW4/ty9SW0YkniiEiEhcGpzr4N4R4W2Zt75pYubXasuIxAuFEBGJWzcMyeTEfEfY2O0LqylrUltGJB4ohIhI3EqxGUybmIcz5J2s3O3jzkXV0StKRDpNIURE4tqxeQ7uHB7elpmxqZG3vlFbRiTWKYSISNy7eWgmw3qEt2VuW1hFhdoyIjFNIURE4p7DZjBtUh4ht5ahtNHH3UvUlhGJZQohIpIQhvZwcNsJWWFj1leNvLtFbRmRWKUQIiIJ47ZhWQzJSwkbu3VBFVVuX5QqEpF9UQgRkYThtAeulgm5tQw7Gnz8fKnaMiKxSCFERBLK8HwntwzNDBt7ubiBOVubolSRiHREIUREEs6dw7M5Jje8LXPz/CpqmtWWEYklCiEiknBcwbaMLaQts63Byy/UlhGJKQohIpKQRvZyMnVIeFvmxQ0NfLJdbRmRWKEQIiIJ654R2RyVHd6WuXF+FXUtasuIxAKFEBFJWGkpBtMm5hLSlWFznZcHl9dErSYR2UshREQS2thCFz89LiNs7E/r6pm/0x2likRkN4UQEUl494/M5ogse9jY1HmVNHjUlhGJJoUQEUl46Sk2fjchL2xsU62Xh1eoLSMSTQohIpIUJvVxcc0x4W2Z/11bz+IStWVEokUhRESSxi9HZXNY5t62jB+YOr+KRo8/ekWJJDGFEBFJGpkOG89NyA0bK6728OinasuIRINCiIgklVP7pnLV4PSwsefW1rF8V3OUKhJJXgohIpJ0fjU6h37pe9syPj/cMK8St1dtGZHupBAiIkknx2nj2VZtmfVVHp5YWRulikSSk0KIiCSlyf1TufKo8LbMM2tqWVmmtoxId1EIEZGk9ciYHHqn7X0b9AbbMs1qy4h0C4UQEUlauS4bz4wPb8usrfTw9Gq1ZUS6g0KIiCS1cwekYQ5KCxt7clUtn1W0RKkikeShECIiSe/RsTkUhLRlPMG2TItPbRmRrqQQIiJJr0eqnSfHhbdlVpW38Ls1dVGqSCQ5KISIiAAXDUzj0oHhbZnHVtbwVb0RpYpEEp9CiIhI0BMn5dDTtfdtsdkHDxU78aotI9IlFEJERILyU+08MS4nbGxtnZ2bFlQZ/9thAAAZBUlEQVRR0uCNUlUiiUshREQkxKVHpHHBgNSwsZeLGxj+Wgk/X1JNaaPCiEikKISIiIQwDIOnTsolzxW+FqTR62fa2jqGv1bCL5ZWU9akMCJyqBRCRERaKUy3Y03OD7vJ3W4NHj+/+6yOE14t4ZfLqilXGBE5aAohIiLtGF3gZMXlhdwxqJk+6W3fKus9fn67JhBGHlpeTaXbF4UqReKbQoiISAdcdgOzr4dPp/TmsbE5FKa1fcus8/h5anUdw17dycMraqhSGBHpNIUQEZH9SE0xuO64TFZe3ptHxoTvrrpbbYufJ1fVMuzVnfzmU4URkc5QCBER6aS0FIPrh2Sy8vJCHhqdTX5q27fQmhY/j62s5YTXdvL4yhpqmhVGRDqiECIicoDSU2zceHwWqy4v5MFR2WEbnO1W3eznkU8DMyNPrqqltkVhRKQ1hRARkYOU4bBx89AsVl1RyAMjs9tc1gtQ1ezn4RU1nPBqCc+srqVOYURkD4UQEZFDlOmwceuwLFZd3pv7Tswm19k2jFS4fTy4PBBGnl1TS73CiIhCiIhIpGQ7bdx+QharrujNvSOyyGknjJS7fTywrIbhr5Xw3Ge1NHgURiR5KYSIiERYjtPGncOzWXV5b+4ankW2o20Y2dXk4/6lgTDy+7V1NHp0kzxJPgohIiJdJNdl454R2ay+oje3n5BFVjthpLTRx71Lqhnx2k5e+LyOJoURSSIKISIiXSzXZeO+E7NZdXkhtw3LJDOlbRjZ2ejj7sXVnDh9J39aV4fbqzAiiU8hRESkm/RItXP/yBxWXVHILUMzyWgnjGxv8HHHompOfK2E/1tfT7PCiCQwhRARkW7WM9XOL0cFwshNx2eS3k4Y2dbg5X8WVnHi9BJe/EJhRBKTQoiISJTkp9r51egcVl5eyA1DMklte9NettZ7uWVBFaNmlPD3DfW0+BRGJHEohIiIRFlBmp1fj8lh1eW9+dlxGbjaCSOb67zcNL+K0TNK+EdxPR6FEUkAKft7gGma9wCXAUcDbmARcI9lWZ+FPMYAHgCuBfKAxcANlmWt7YqiRUQSUWG6nd+MzeWmoVk8s7o20IZptY3I17Veps6r4ulVtdwxPJsrBqWRYmvbzhGJB52ZCTkV+D0wHjgd8ABzTNPsEfKYO4HbgBuB0UAp8L5pmlkRrVZEJAn0Sbfz+LhcPr28N9cck4GznXfqjbVefvbfSsa9Xor1VQNezYxIHNrvTIhlWWeHfm6a5veBamAC8FZwFuQW4FHLsqYHH3MVgSByJfCHSBctIpIM+mXYefKkXG4ZmsnTq+t4qbie1ru9f1nj4dq5lTy5qpY7h2dx6cA07JoZkThxMGtCsoLPqwx+fgTQG5i9+wGWZTUCcwnMnoiIyCHon5nC0+NzWT6lkKsGp9POxTRsqPZwzSeVTHijlNc3NeDza2ZEYt/BhJBngZXAwuDnvYO/lrR6XEnIMREROUQDMlN4dkIey6YU8r2idOzthJH1VR6u/riSiTNLeePrRoURiWn7bceEMk3zaWAiMNGyLO+hfOPi4uJDebpEmM5HbNH5iC2xeD5uLoQp2QZ/2eLgP6V2fIQnks+rPFz1UQVF6T5+MqCFU3t6MRKkSxOL5yMZFRUVHfLX6HQIMU3zGeDbwGmWZW0MObQz+GshsDlkvDDkWBuRKF4io7i4WOcjhuh8xJZYPh9FwGnD4KtqD4+vquHVjY20Xp9a3GDjzvUuhvZwcPfwLM4bkIoRx2kkls+HHLhOtWNM03wW+A5wumVZ61sd3kQgbJwZ8vhUYBKwIEJ1iohIB47MSeEPJ/dg8aUFmIPSaC9irKlo4bsfVnDaW7t4d0sjfrVpJAYY+/uHaJrmNOD7wCXA5yGH6izLqgs+5i7gXuBqYANwH3AycLRlWbW7n1BdXa1/9TFIP1nEFp2P2BKP5+OLqhYeX1nLjE2NdPSme2K+g3tGZDO5nyuuZkbi8Xwkg5ycnIP6R9SZmZDrCVwR8wGwI+Tj9pDHPA48A0wDlgF9gLNCA4iIiHSPo3Md/OXUHiy4pIBLBqa1+5gVZS1c8X45Z76ziw+2NWlmRKJivzMhkaSZkNiknyxii85HbEmE87G2ooXHVtbw5jdNHT5mbIGTe0ZkcUqf2J4ZSYTzkYi6ciZERETi2JAeDv5+ek/mXtSL8wektvuYxaXNXPJeOefNKmPuDnc3VyjJSiFERCRJDOvp5OUzevLxhb0457D2w8jCkmYuereM82ftYt5OhRHpWgohIiJJZni+k39N7slHF/bi7P6udh8zf2czF8wq48JZu1igMCJdRCFERCRJjch38u8z8/nggl5M7td+GPnvzmbOm1XGJe+VsbhEYUQiSyFERCTJjezl5LWz8pl9fj6n920/jHy83c3Z/yljyuwylu1q7uYKJVEphIiICABjClzMODufd8/L55Q+7YeRD7a5mfz2Lq6YXcYKhRE5RAohIiISZlyhizfOyeedc/OZ2NvZ7mPe3+bm9Ld38a055awsUxiRg6MQIiIi7ZrQ28Xb5/birXPyGV/Yfhh5b0sTp761i+/MKWdVucKIHBiFEBER2adJfVy8c24+b5zdk3EF7YeRWVuaOOXNXXzvg3I+q2jp5golXimEiIjIfhmGwSl9U5l1Xj6vn9WTMb3aDyNvb25i4hulXPVROZ9XKozIvimEiIhIpxmGwWn9Unnv/HxeO7MnI/Md7T7uja+bmDCzlKs/qmB9lcKItE8hREREDphhGEzun8qcC3rx78k9Gd6zbRjxA69/3chJr5dyzScVbFAYkVYUQkRE5KAZhsHZh6Xy0YW9+OcZPRjWo/0w8trGRsbNLOXauRV8Wa0wIgEKISIicsgMw+DcAWl8clEv/nF6D45vJ4z4/GB91ciY10v56dwKNtZ4olCpxBKFEBERiRjDMLjg8DTmXtSLv5/Wg+PyUto8xueHf33VyOgZJdwwr5KvaxVGkpVCiIiIRJzNMLhoYBrzLi7gxVN7cGxu2zDi9cPLxQ2Mml7CjfMq+UZhJOkohIiISJexGQaXHJHG/EsK+L9T8hic0zaMePzwUnEDI6eXcMv8SrbUKYwkC4UQERHpcjbD4LJB6Sy8pIA/nZzHUdnth5EXNzRw4vQSbltYxbZ6bxQqle6kECIiIt3GbjO44sh0Fl1awAuT8hiUZW/zmBYf/GV9PSNe28kdC6vYrjCSsBRCRESk26XYDL59VDpLLivk9xNzGdhOGGn2wZ/W1zNi+k7uWlTFzgaFkUSjECIiIlGTYjO4siiDpZcV8tyEXAZktg0jbi/8YV09w1/bydMbHayrbMHv90ehWok0hRAREYk6h83g+4MzWD6lkN9NyKV/Rtsw0uSFf253cNLMUoa+WsKtCyr5z+ZG6lp8UahYIqHtyiAREZEocdgMfjA4g28fmc7LxQ08tbqWre2sCdla7+WvXzTw1y8acNpgQm8XZ/ZP5az+Lo7MTsEwjChULwdKIURERGKO025w9TEZXFmUzksb6nl6dS3bG9qf8Wj2wUfb3Xy03c29S+CILHswkKQyobeLtBQFklilECIiIjHLZTe45thMvleUgbWxgX9/Xs7yGjtN+1ijuqnWyx/X1fPHdfWk2Q0m9XFyZv9UzuyfysAs/bcXS3Q2REQk5qWmBNo0Jxnb6X/EUczb6Wb21ibe39rE17UdJ5JGr5/ZW93M3uoGqhmck7KnbXNSoQunXbMk0aQQIiIicSUtxdgzs+H3+/myxsP7W928v7WJ+TvdNO9jneqGag8bquuYtraOzBSDU/q6OKt/KpP7p9KvncWw0rUUQkREJG4ZhkFRjoOiHAfXD8mkrsXH3B2BQPL+Vne7i1p3q/P4eWdzE+9sbgLguLwUzgqGmzEFThw2zZJ0NYUQERFJGJkOG+cNSOO8AWn4/X7WVXmCgaSJRSXNePaxvcjnlR4+r6zjt2vqyHYanN43lTP7u5jcL5XCdM2SdAWFEBERSUiGYXBcnoPj8hzcPDSL6mYfH2937wklJY0d921qmv3M/LqRmV83AjC8pyPYAnIxMt+JXbMkEaEQIiIiSSHHaePigWlcPDANn9/PmoqWPWtJlu5qxrePWZKV5S2sLG/hiVW19HDZOKNfYF+SM/q56JmqWZKDpRAiIiJJx2YYnNDTyQk9ndx+QhaVbh8fbmti9tYm5mx1U+7ueJakwu3j1Y2NvLqxEQMY1cuxZ1+SYT0d2LRRWqcphIiISNLLc9mYMiidKYPS8fr8rCxv2XMJ8Iqylg6f5weW7mph6a4WHvm0loI0G5P7BQLJqX1d5Lp0d5R9UQgREREJYbcZjOzlZGQvJ/eMyKa00csH2wJtmw+2NVHd3HHfprTRxytfNvDKlw3YDRhT4Nxzxc2QPG0n35pCiIiIyD4UpNn5zlHpfOeodDw+P8t2NfP+1iZmb3WzpqLjWRKvHxaWNLOwpJkHl9fQN922Z3+TU/q6yHJolkQhREREpJNSbAbjCl2MK3Rx/0jYXu9lzrZA2+bj7W5qWzqeJdne4ONvGxr424YGHDYYX+hicv/AZmmDc5JzlkQhRERE5CD1zbDzg8EZ/GBwBs1eP4tLm/dcAryuytPh81p88MkON5/scHP/0hoGZNr3tG0m9XGSnpIcsyQKISIiIhHgtBtM6uNiUh8Xvxqdw+Y6D3O2Bu5xM3eHm4Z97JS2uc7Ln9fX8+f19bjsMKm3a0/rZlB24v5Xnbh/MhERkSgakJnCj45J4UfHZNDk8bOgxL3nEuAvazqeJXF7Yc42N3O2ublrcTVHZtv3XAI8vtBFakritG0UQkRERLpYaorB6f1SOb1fKoyFjTV7t5P/70437o5vccNXNV6++ryeFz6vJz3F4OQ+u2+652JAZnz/Nx7f1YuIiMShQdkpXHdcJtcdl0mDx8d/d+y+4qaJzXUdJ5IGj593tzTx7pbATfeOzU3Z07YZVxh/N91TCBEREYmi9BQbZx+WytmHpeL3+9lQ7dlzF+AFJW5aOt68lXVVHtZV1fG7z+rIchic2nfvWpI+cXDTPYUQERGRGGEYBkfnOjg618HU47OobfHxSchN97Y3dJxIalv8vPVNE299E5glGdrDwVn9A6FkVC8nKTE4S6IQIiIiEqOyHDYuODyNCw5Pw+/3s7bSs6dts6S0Ge8+brq3pqKFNRUtPLW6jlynwRn9UvfcdK9XWmzMkiiEiIiIxAHDMDi+h4Pjezi4dVgWVW4fH28PXnGzrYnSxo5nSaqa/Uzf1Mj0TYGb7o3I33vTvRH50bvpnkKIiIhIHMp12bjkiDQuOSINn9/P6pCb7i3b1UJHkyR+YEVZCyvKWnhsZS35qTbO6Be44ub0fqnkdeNN9xRCRERE4pzNMBie72R4vpM7h2dT3uTlw+BN9+Zsc1Ph7niWpKzJx7+/auTfXzViM2BML2dwcauLoT0cXbqdvEKIiIhIgumZaueKI9O54sh0vD4/K8r2zpKsLO/4pns+PywqbWZRaTMPrYDeaTYmB6+2ObWvixxnZGdJFEJEREQSmN1mMLrAyegCJz8/MZuSht033XPz4fYmapo7Xt26s9HHP4ob+EdxAykGjCt07rnHzbF5jkOuTSFEREQkiRSm2/luUQbfLcqgxednaenejdLWVna8nbzHD/N2NjNvZzO/WFZD1dX9DrkWhRAREZEk5bAZjO/tYnxvFw+MymFbvZc5wUDyyXY3dfu46V4kKISIiIgIAP0y7Fx1dAZXHZ2B2+tnUYmb97cGFrh+Ud3xLMnBUggRERGRNlx2g1P6pnJK31QeHpPD17Ue5gQXt87d0RyR76EQIiIiIvs1MCuFa47N5JpjM2mMUJum+3YkERERkYSQlhKZvUMUQkRERCQqFEJEREQkKhRCREREJCoUQkRERCQqFEJEREQkKhRCREREJCo6tU+IaZonA7cDI4G+wNWWZb0YctwAHgCuBfKAxcANlmWtjXTBIiIikhg6OxOSCXwG3Aw0tnP8TuA24EZgNFAKvG+aZlYkihQREZHE06mZEMuy/gP8B8A0zRdDjwVnQW4BHrUsa3pw7CoCQeRK4A8RrFdEREQSRCTWhBwB9AZm7x6wLKsRmAuMj8DXFxERkQQUiRDSO/hrSavxkpBjEsOKioqiXYKE0PmILTofsUXnI7Ho6hgRERGJikiEkJ3BXwtbjReGHBMREREJ06mFqfuxiUDYOBNYCmCaZiowCbgj9IE5OTmRue2eiIiIxD3D7/fv90GmaWYCRwU/XQA8CrwJVFiWtdk0zbuAe4GrgQ3AfcDJwNGWZdV2ReEiIiIS3zo7EzIK+Cjk8weDH38Dfgg8DqQB09i7WdlZCiAiIiLSkU7NhBws0zRPJNCiWWRZ1oRWx9r7xqssyxreZQUloeC+Lle1c2ixZVnjgufhCsuyXmvnefmWZV3Q9VUmp/28Pk4BfgGcAKQDO4BFwM8sy6rp7loTVcjr4xeWZT0UMn4qgR+8elmWVRYy/iZwPnCOZVnvd2+1ycM0zX4EduE+DygAdhHYq+pBy7K2hjyukMAs/AVAf6AMWA08F9zfSiLANM1eBCYezgP6AFUENjB9NPR1YJrmZcCrwL8sy/puZ752V18dcw3we+B40zSPbef4Twj8gXZ/nNHF9SSrOYT/Pfch8I9Joqvd14dpmscB7xJ4Mz0NOB74KVANuKJQZ6JrAu4IvtF2yDTN3e9RzxA4d9IFTNM8AlhG4N/9VQSWAnwPGAIsNU1zYPBxA4EVwNnAPcAwYDLwDvBCd9ed4KYDY4AfA4MJhL5ZQM9Wj7uGQGfkEtM08zrzhSOxMLVdpmmmEdgxdRKBn+R+TOD+M6GqLMvSFTRdz62/59iyn9fHWUC5ZVm3hjxlI6CfvLvGRwR+ir4fuGkfj/shgXD4HLDeNM2elmWVd315SWca4AMmW5bVEBzbbJrmZKA4ePx8AgEeYJRlWXUhz19nmuY/uq3aBGeaZi6B96kzLcv6IDj8DcELUUIe15/AD03fJxBYvgs8v7+v35UzIZcD31iWtQZ4CfiBaZqOLvx+IvFkX6+PnUAv0zRPi1p1ycUH3A381DTNI9t7QPD2FD8C/mFZ1jcE1r19v/tKTA6mafYAzgGmhQQQAIKf/x441zTNniGPq2v9dSzLquqOepNEXfDjouCVrx25GpgdDOYvEfjBar+6bCYkWMBLwd9/AjQAFwOhaw9eanUvmussy3q5C2tKVueYptn6hTrNsqy7olKNwL5fH68SmGL+0DTNUmAJgZ/WX7Isa1cUak14lmX9xzTN+cCvgW+385BTgB4EpvoB/g7cCvy2eypMGkWAAazr4PjnweNH7udxEiGWZXlM0/wh8CfgWtM0PwXmA69alrUY9oT0qwnczBYC72PTTNMcZVnWsn19/S6ZCTFN8yhgIvBK8A/hB16mbTK6Axge8vFmV9QjzCX873k48ERUK0pi+3t9WJbltSzragItgtuBzQReK+tN0xwSlaKTw13AFaZpjmzn2DWAZVlWc/Dz14AjTdMc223VSSjtOdWNgjen7QtcSGAtyHhgkWma9wYfcgaBK2PfCj6+DphJJ2ZDumom5BrATqCPt3vMADBN8zDLsrYEx3ZalvVlF9UgezXs4++5FshpZzyXwEJIibxOvT4sy9pGYLbkJdM07yOwB88dBNYmSIRZlrXENM3pBBbWhV4pkwtMAZymaf4k5Cl2AudycbcWmti+BPzAccDr7Rw/Lni8OPjrsR08TiLMsqwmAuvS3gd+ZZrmn4Ffmqb5JIHXQS5Q3+o9rdY0zdtat9ZCRXwmxDTNFAIrmu8h/CfvEwis9r860t9TDskXQNhPfqZp2gmcry+iUlECO9jXh2VZlQQu083snkqT1r0EFuGdEzL2XQKXiJ5A+Dm7FviWaZoZ3V1kogquJ3gPuN40zfTQY8HPbwBmWZZVEXzc1OBmmrR6bG531JvkPicwkdEbuITA+1rr9zQ3gfVvHeqKmZDzgXzgT61Xjpum+S8Ci78eaveZ0lVcpmm2vqOxN7i+4Gngr6ZpriWQcNOBGwn0v//YvWUmhc68PkoIvIhfB74CUoEfAEOBx7q33ORiWdaXpmn+Ebg5ZPjHwGuWZX0W+ljTNDcQaGt+C/i/7qsy4U0lsDP3nOAMYDGBNSC/JvDT9dTg424gsDZhmWma9xMI8QaBKzTuAQZ0c90JKbgI+FUC/8ZXE5g9H0Vg/ccHwKVADfCyZVneVs+dQWCW5O8dff2uWBPyY+CjDi5dexUYSOA+M9J9JhP4KTr041MAy7L+SeCn76sJXJv/LoFkO0mX9XaJzrw+cgmEwf8lsCHQXAILI3+ghdvd4leAJ/j7QcAIwhfUAxBcH/Im2jMkoizL+orAf3JrCbQjNxJYP7UOGG1Z1qbg4zYCJxL44ekxAv9BfghcRGCWSiKjjsBGiTcTWES/FniEwDn5FoH3tJmtA0jQq8Ak0zQHd/TFu3THVBEREZGOdPWOqSIiIiLtUggRERGRqFAIERERkahQCBEREZGoUAgRERGRqFAIERERkahQCBEREZGoUAgRERGRqFAIERERkaj4f1jFsD+IgiQSAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# COMMON MISTAKE: Don't use a line plot when the x-axis cannot be ordered sensically!\n", + "\n", + "# For example, ordering by continent below shows a trend where no exists ... \n", + "# it would be just as valid to plot the continents in any order.\n", + "\n", + "# So, a line plot is the wrong type of plot for this data.\n", + "# Always think about what you're plotting and if it makes sense.\n", + "\n", + "drinks.continent.value_counts().plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Important:** A line plot is the wrong type of plot for this data. Any set of countries can be rearranged misleadingly to illustrate a negative trend, as we did here. Due to this, it would be more appropriate to represent this data using a bar plot, which does not imply a trend based on order." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to change the size of a plot" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Technically the figsize is 15 \"inches\" (width) by 8 \"inches\" (height)\n", + "# The figure is specified in inches for printing -- you set a dpi (dots/pixels per inch) elsewhere\n", + "df.plot(figsize=(10,5)); # width, height" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to change the color of a plot" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['col1'].plot(color='crimson', figsize=(10,6),linewidth=1.0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to change the style of individual lines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CheatSheet of Styles\n", + "https://raw.githubusercontent.com/rougier/matplotlib-cheatsheet/master/matplotlib-cheatsheet.png\n", + "\n", + "\n", + "https://matplotlib.org/2.0.2/api/colors_api.html" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# : - dotted line, v - triangle_down\n", + "# r - red, b - blue\n", + "df[['col1', 'col4']].plot(figsize=(12,6), style={'col1': ':r', 'col4': '--y'});" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Challenge: Create a line plot of `RM` and `MEDV` in the housing data. \n", + "\n", + "- For `RM`, use a solid green line. For `MEDV`, use a blue dashed line.\n", + "- Change the figure size to a width of 12 and height of 8.\n", + "- Change the style sheet to something you find [here](https://tonysyu.github.io/raw_content/matplotlib-style-gallery/gallery.html).\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### Type your code here ....\n", + "housing[['RM', 'MEDV']].plot(figsize=(15,8), style={'RM': 'g', 'MEDV':'--k'});" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets use Percent Normalized (MAX)" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDVprice_percentrm_percent
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20.027290.07.0700.4697.18561.14.96712242.017.8392.834.0334.7163.679245115.728437
30.032370.02.1800.4586.99845.86.06223222.018.7394.632.9433.4157.547170112.716437
40.069050.02.1800.4587.14754.26.06223222.018.7396.905.3336.2170.754717115.116373
\n", + "
" + ], + "text/plain": [ + " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", + "0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296.0 \n", + "1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242.0 \n", + "2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242.0 \n", + "3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222.0 \n", + "4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222.0 \n", + "\n", + " PTRATIO B LSTAT MEDV price_percent rm_percent \n", + "0 15.3 396.90 4.98 24.0 113.207547 105.903197 \n", + "1 17.8 396.90 9.14 21.6 101.886792 103.422727 \n", + "2 17.8 392.83 4.03 34.7 163.679245 115.728437 \n", + "3 18.7 394.63 2.94 33.4 157.547170 112.716437 \n", + "4 18.7 396.90 5.33 36.2 170.754717 115.116373 " + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "housing['price_percent']=(housing['MEDV']/housing.MEDV.median())*100\n", + "housing['rm_percent']=(housing['RM']/housing.RM.median())*100\n", + "housing.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### Type your code here ....\n", + "housing[['price_percent', 'rm_percent']].plot(figsize=(15,8), style={'RM': 'g', 'MEDV':'--k'})" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [], + "source": [ + "housing.drop(['price_percent','rm_percent'],axis=1,inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### some Styles you can play with" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [], + "source": [ + "#%matplotlib inline\n", + "#plt.style.use('dark_background')\n", + "#plt.style.use('classic')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Bar Plots: Show a numerical comparison across different categories\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AF 53\n", + "EU 45\n", + "AS 44\n", + "NA 23\n", + "OC 16\n", + "SA 12\n", + "Name: continent, dtype: int64" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count the number of countries in each continent.\n", + "drinks.continent.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "drinks.continent.value_counts().plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OlR+ndL6iU/TU39sOMBFa1MP7IPrfpmjUtW1qs51BWfcBaWgK7GESWkbD+7DtACp/lrZv1tWJqhJ3K4hA0qIewolF5wJn2c6h8qe2+41q2xmUL8RtBxgvLeqD6bBHkZnb3zbXdgblC+8O6lFdWkgH02GPIlNunAXl6VS/7RzKugrgA7ZDjIcW9SBOLDobWGk7h8ovgXBt1zY9lktBQIc/tKgPFAMitkOo/Du9ffNu2xmUL5wXxOEPLeoD6bBHkapr39RrO4PyhXICuPglp6KOx+M3xePx00e5f2k8Hr9p4rEKz4lFZxDAP0CVncWdr+o7JbXfu20HyFWuV9QX4S7HHMkRwIXjTmPX+3E3cFFF6O29u2fYzqB8o+iLeiyzgL48P2ehnGM7gPLOFKdXN9hS+x0hDU1H2Q6RizHfDsbj8bM48CiqD8bj8eH+JWcAHwOeyU+0gjvDdgDlHYGpC3r37NhWOSvwexOrvHg3sNl2iGxlM253DvCtzD8b3AUhHxzhsc8Dl+UhV0FlViMG6iesyt2Sjpe3a1GrjPOA622HyFY2Qx/XAHOAuYAAn8v8fvBtNlCdTCZPTCaTj3uU1UvLbQdQ3qtr39huO4PyjXOkoSlsO0S2xryiTiaTPUAPQDwePwLYlUwmi+0cOh32KAGntb9sO4LyjynAIgIyVJvTlKVkMvmaV0Es06IuAcf0bJ9kO4PylWUUY1HH43HBPfD1EuBI3A8QhzLJZDIwc1adWLQSONV2DuW9WQMd821nUL6yDPix7RDZyLVQrwG+DDwN3ALsy3uiwluCu1pJFbmISddUO33d3eEK3fZUgVvUgZBrUV8I/CaZTAZyY5MR6LBHiRCQxZ2vvd447ZhjbWdRvnCsNDRNN5fXttoOMpZcF7xUAX/yIohFWtQlZFn7pj22MyjfEGDELTH8JNei/jNwmhdBbHBiUUGn5pWUZe2bB2xnUL6yxHaAbORa1F8ATo/H4/8cj8eLYeHA4bjL3lWJOLFzq34eoQartR0gG7mOUW/BfbtwJXBlPB4fANJDHmOSyWRQpkHpasQSs6Bvr/5gVoMVZVH/GncZebEYbSdAVYSq0v2HiUmnjYR0L3YF7geKIXN57dALTl/JdcHLRR7lsEWLusQIVC3s2bltc/V83U1PgTtB4gjc0QLfKvWrCi3qErS0Y3OL7QzKV3w//JHzCsJ4PH4MsBp3V705wPnJZPLeeDw+G3dBzI+TyeSj+Y3pGS3qErSsbVPXL+edaTuG8o/jgd/ZDjGaXI/iWgw8DpwLNAJv7j6VTCZ3425y8vl8BvTYkbYDqMI7teOVUn8nqQ50tO0AY8n1L+zVwHbgGNztTmXI/X8kIAtIMntQT7adQxXewp4dgTuFWnnK959X5FrUZwI3JJPJNoaf/bEVqJlwqsLQYY8SNSPVFZS/o6owiq6oYfQzEecBvePMUmha1CUqjJkzfaCrzXYO5RtFV9RPAu8d7o54PF4GfBx4ZKKhCkSLuoSd0vnKNtsZlG9Ml4YmXy/Sy7WovwecF4/HbwQWZ75WE4/Hz8fdB+SYzGOCQIu6hNW1bfL9jmmqoA61HWA0ORV1Mpm8G/gU7uG292S+/HPgD7gzPj6ZTCYfzmtC7+gm8iXs9PbNKdsZlK/4evgj5zHqZDL5S+Aw4EPAFcA3gDjw9mQymcxvPE/pjI8SVtu1rcp2BuUrvt5kblxHZmUOt12X5yyF5usxKeWt+f2tc2xnUL7i6ymb4yrqeDw+BXg77pmJQ+dSk0wmH5hgrkLQoi5hFSa1IJJ2UqlQODDneypPFU9Rx+PxWcAPcYc99q9KFN6aU73/n8MHf7fvaFGXMIGy47qbX9kw+bAjbGdRvjDFdoDR5Ho1cSPwPmAN8CDBPtxWi7rELW3fvEuLWmUUzxU1cB5wXTKZ/CcvwhSYFnWJq2vf2HNTzTm2Yyh/8HVR5zrroxt41YMcBeXEopXoFq8lL9r5ahCG6FRhFFVR3wLUexGkwHRqnuLwnl3TbWdQvlFtO8Boch36WAecHY/H7wJuwt2EyRn6oGQy+VgesnlJhz0UU50eXy9yUAXl63fYuRb1fYP++V3D3B+UWR9a1IoQTJ/ft293S8WM2bazKOt83Vm5FvXFnqQoPC3qEmPAmAhdppxuUybd6Qp6Tbn0nbvjqTeebl3o61VpyntOOPSGn0/kyvVw2597FaTAfP02R4ERBkw5neky6Tbl9KQrpM+U05eukAFTQSpdLk66grQpF0mXgymTsCkjZCJSZiKUE6LChKgkRDXu+OMkRCYz5POJw19v3Ff7YgcDf+rrMTvTS4BKG/++yrq93Hi+7QwjKtVVWUHZMzsw0hG6TBndply60+X0mnLpTVcwYCpkIF0hTrocx1QI6XKMKZeQW6qUmYhETPjNUq1EmIRbquW4K19neJm7zXQd7Ry17bUZRx55pulO7+3/S++jzjMDh5Pm7V6+rvIdX2/SNWpRx+Px1bhjzt9NJpPpzO/HYpLJ5FV5SeedHtsBbDJCypTRZcqlK11OjymX3nQ5/ekK6TcVpNIVks4Uq5hySJdJyJQRNhEpM2HKCFNpQlQgVCNvXq26BRswBmZ3hJsPrTKzHqisnnZWxXuqV5oLjHGeH3hi4C+9adNmTsXn45cqL4Jb1MC3cYv6+0B/5vdjMYDfizpQV9QmTHfaHVvtSlfQl7la7TflmavVChxTPuRqtYyICUuZiVBhQlQQogqhCpiMSAUwLXMrWb1paQOZBrAzvKFuQer050JEThQRiSwqXxJZVE66Nd08cE/PRmdjqhbDXNuZlWd8XdRizHBHHxY3JxadA+z04rmN4JgyukwZXely6THl9KYrpN+US3/avVpNmQpMulyMKUcGXa1GTIRywpSb/aXqXq1ORkTH1D2wqz/8yk9aZr+5hDxsyltqUqeFBTloZz3jmIHUk/1PDDzYV02PWTz0fhV4t61uvSpuO8RISnWM+s2hDxOmN11Gp3FLtSddLn3pCvrcsVWcdLkMN7YaeXNsNXzQ1Wol7ionX690UtDmhNsH/96R/vm7wy+sn+0cP1OQA4Y7JCxlZUsr6sqWVpBucbb039XzRvp152T0z7lY+PqKOtfd8xzgU5nDA4a7/6PAL5PJpK/H9HZcWNmFMa28Vaz6SX8JakuFu4d+rSe0N9qV3nnfZDPv7JG+LzQ/vLDywskLTb/pGni478HUY31zGeBYT8Mqr3XYDjCaXN9SH7T39DDP5/uxlJr6RoOIIFKq7ygU0JoKDwz39b2RTWen6H10rO+XcplUfk7liuorph1b8YlJG2RO6GGgL+9BVSFstx1gNOMZ+xytiE8nOFuf6uGmJa4tFU6PdN/2yPrjDOnXsn2u8BGRRVWfnXJG1ZendIVPLrufEFvzk1IViK+Leswryng8vgpYNehLDfF4/LvDPHQ67iyCX+Qpm9daQefKlrIOJzziEJ0RZ9qO8LM75zmLuwXJesMeqQ7NrHhv9UrzHmOc5weeHLi31zHtOsUvAIJd1LizI57P/PPhwBuZ22AG6AKeAP4jX+E8FpQrf+WRLidUPtr9/aHOo9vMaw9PTx9+Rq7PnZnid+qbU/zu7tnkbErVYtCzGv0p2EWdTCb/G/hvgHg8/hfgO8lk8s9eBysALeoS15eWMRfotIe3nVFlZj5QYaaeNd7XCU0P1VTEJ9UYxwyknuhvHHior4oec/J4n095otl2gNHkutdHMR2H4es/GOW9ASNZLfjZEX5u2YLU6U0hIhPatUfCUlZ2ekVd2elvTvHbln7diaJT/GxLAztshxjNeE8hrwWOZORTyIMwTr3FdgBlVxpmZfVAMeXbI+un1aSW7BVkZj5eW6f4+cru1a1XFdU86oW4p7wsZeSpeoZgfKC42XYAZU9/WjpBsj7px5G+Q/eEX3pylnPsdCF/K0X3T/ErP6cS5+WBDf339LaZXeklQEW+XkONydfj05D7FfWPgROBywn+KeRa1CWswwntIccj2bpDu0+tSs+6f5KZs9KLTOEjyxZVfbYM05Xe0/+X3kbn2YEjSfM2L15LHaDoivoM4HvJZPLfvQhTYC/jjk3pPholqC114PLxbO0Jv3RWRWrq4xEqTst3pv1kUmhWxXurz85M8Xti4N7etE7x85TvizrXktoNtHkRpNBq6hv7gG22cyg72lLhrnF9oyAtkfVHGdKe/93Zv4tf1WVTl1ZeOqUlfEzkPoRdXr9uCSq6ov4R8Ml4PF4sP9l1+KNEtabC/eP93rSkZuwIP9dhMAVbLh6aETq0Ij7p7KqvTZ1edm7lX6mSZwr12iXA90Wd69DHy5nveTYej/8ceJ3hTyFP5iFbIWwB3mE7hCq8Nmfk5ePZ6A91HN9uXn9wWvptK/KVKRuZKX7Ly06vIL3d2dx/d88bOsVvwl6wHWAsuRb14F3zrh7hMQYISlHrFXWJak+FJvzZRFt464qq9MwHy5lc0LLeL3RI+KjKCycfZfpN18BDfQ+mHtcpfuPk+3cnuRZ1MS14AS3qkjXW8vFstUSeOW1BatlLIcLWClLKZVL5OypXlL+jEuflgef67+lt1yl+WWtZ3XrVbtshxpLrysT7vQpiiRZ1ieozofyc7yimsiXydNUhqVPahOxWOnopfGTZiTrFLyfjvpoWkTnA/wEuAA7B3ehtA3C1MeaeQY/7IHAb8CtjzCfG81rjXZkYBk7B3aQJ4FXgyWQyOaFxPws2o1P0SlJ/Wqbk67lS0vO2veFNj810jj5NkLH2bC+IEab4LUH/rg/17AS+9zdANXAJbpfMBVZy8IrXzwDXAJeJyAxjTM7rT3I+MzFzisu/AfN5a3WiAVqALwXog0QAmtfWPQ3oGXgl5uqtc9tB8voB3OzU8fdXm1meLIbJh/S+9BsD9/Rs1l38DvDJ1a1X3ZrrN4nIdNwFf+caY/40yuMWAJuABbif3a01xvww19fL6adrPB7/AO4Hiq3AZcC7MrfLMqF/GY/H359rCMseth1AFVbK0JvvkgbYHX5hhUP/U/l+3nzJTPFbqVP8DjDeP6/OzO394h7nN5KLgbuNMXuAm3GvvnOW69DHPwNPAmclk8neQV+/Nx6P/xfwEPAvwB3jCWPJQ8AXbIdQhdORCu/GvcLJLyG0PbL+7YemljYLUpP358+TYab4NZfoQb1twIvj+UZjTEpELgJuBP5BRNbjXvTdZox5FNwFS7hF/U+Zb7sduF5Elhhjnsjl9XIdr1oE3DKkpAFIJpN9uD8xFuX4nLbpFXWJaXdCnq2uTcvArJ3hDfsMZtwLagopM8XvrKqvTg1Fllc8SBkv2c5UQI+vbr1q3Ge8GmN+A9QA7wPuBJYDj4jINzIPeSfuDqO/yzy+E1jHOK6qcy3qbmD2KPfPyTwmMGrqG7fiLtxRJWLcy8ez1BdqO6Ej1PyIl6+Rb1Ihk8vfkTmo9+PVpXJQ75gHGI/FGNNrjLnHGHOlMWY58BPg2yJSjvsh4nSgS0RSIpICPgZ8XCT7490g96L+M7AqHo8fNME/Ho+fgTtWPeLAuo89ZDuAKpzWVNjzAmoNv3LWAN2BfLcWXljmHtT7pSmd4cVFfVDvhIt6GE24Q8rzgRhwIXDyoNti3B+AH87lSXMdo/4nYAVwXzwefwrefJt0LO50vRbgihyf0w8eBj5uO4QqjNZU+KBtD7zQEnk6emjq9E0hwkcX4vXyTSaFZlW8r3qlea8xzoaBJwb+UnRT/B4b7zeKyCzcudE34U7x6wCW4Hbkn4F6oB241RjjDPne3+JebWe9b39O/8GTyeRruD8RfgBMAT6UuU0BrgNOzjwmaPSKuoR0OOGCzHU2kq7eEXkmYjDj2lLVL0REIifu38VvcnP46KLYxW/T6tarJnL8VifwCLAKuB/3APDv4c6K+yjuOPS6oSWdcRuwQkSOyfbFcppHHY/HJwGzksnksG+F4vH424DdyWQyUOPUzWvrQrjTC0vtU++S9OPmWY37UpG6Qr3eZGf+IzPTRy0r1OsVgnHMQOrx/icGHu6rpscEcR3Cv61uveortkNkK9e3MNcB/zPK/euAa8cfx46a+sY00Gg7hyqM3nSoqpCv1xluWdYj+4pq+wUJS1nZsoq66q9MXVx5yeTNoQXhB3Df6gclfS0QAAAU7ElEQVTF72wHyEWuRX0usHaU+9cC7x5/HKt0+KNE9Kfzv9hlLLvCz5/pMPB0oV+3EEKHhI+qvOjNKX4PBGCKXysB+/8916I+BGge5f4W3HmFQXSn7QCqMBzIy0niORHCLZH1NQYzkXFRX8tM8TsrM8XvOR9P8bvT76eOD5VrUe8Cake5vxb3p1Xg1NQ3Pgm8YjuH8pZj6AeZbuW1pX/urnDTDoMJVEmMR3hh2YmDpvjd57MpfkFaOQ3kXtR/AD4bj8eXDL0jHo+fBnw285ig+o3tAMpbne7p49b0hvad1BlqCeT86vHITPE7u+rrUxeUf6DqCZkqj+PuWGlLCvijxdcfl1znUX8Ld+/Vxng8/gfcKSngLhv/G2AH8M38xSu424GE7RDKO+2pcCvuEJ41+8JbVlampzeWUVWwmSe2iUgocmL5ksiJ5aT3OdsG7u7d7GxOnWBhF78HV7deFbh3/bnOo27BndR9K+6+q1/L3M4CbgGWJJPJ0cawfa2mvvFR8NVbNJVnbU6403YGgJbI04sM6Zdt57AhNCO8oOKjk86u+trUaWXvqvwrlTKRPaFzFajZHvvlfHBAMpncAVwUj8cF3vxpuCuZTI57cxOf+Q3wJdshlDcKsXw8G0acKS2RZ3bOT53cJUh+TpsJGAlLedmyiuVlyypwtqc2DdzVuz29zYniLqDzSuDGp2EcBwcUu+a1dcvRHfWK1u/3TL1vQ1fV2bZz7DfFqfnrjPSRy23n8AvTZzoHHupdn3q8fx4psl65l6UXVrdeNdpkCN8qljX7+dQIvGE7hPJGe6owy8ez1RFuXt4rbQ/YzuEXUiGTy99ZtaL6a9OOqfh49XMyO/RX8jfFL5DDHqBFfZCa+kYD/NZ2DuWNLidUZjvDUDvDG+rSpJ6zncNvwgvLTqz63JTlg3bxm+h2xOvyEswCLerh3W47gPJGT4GXj2dFTNn2yPrZBhP0jY48sX8Xv6qvTz10AlP8mla3XhXYbSLGdQp5CXgId5XlfNtBVH71m/ydPp5PjvQdsjv84vrZznEzBQnbzuNHw0zx2+Jszvqg3p94HtBDekU9jMwmTT+3nUPlX8oww3aGkfSE9kS7ZGeg9qCwJTPFb+WgKX6jHdTbTw57P/uRFvXIrsddxaSKRNqQwsY+HznYG950VorecW9oX2r2T/GrTkxdXHHJpE2ZXfw6hjxs3erWq3bbyJcvWtQjqKlvfJ0Af/igDtblhPaA+GrWx0EE2R5Zf6whHcQDOKwKHxI5OrOLn0Tqyh8kwsbMXTdaDZYHWtSjW2M7gMqfdicciKXDRpxpO8LP9RpMj+0sQTR4il95vPou3KOxAk2LehQ19Y0PAutt51D50ZYKD31L7Fv9oY5j20Jbn7SdI+gix5Tdvbr1qsCv6tOiHpteVReJ1lS413aGXLSHXz+zTzoetJ0jwLpwD58NPC3qsf03BP4gTwW0psKB+3B4R/jZpWlSTbZzBNStiUQiEMNdY9GiHkNNfWMf8GPbOdTEtTshf3+QOBwxFS2Rp6cazF7bUQLGAD+wHSJftKiz8yNgwHYINTGdTjiQC7xS0rtgT/illw3G5ob7QfPbRCJRNO9EtKizUFPf2IwuKw+8HkcqbWcYr+7Q7iXdsls3b8qOAa6yHSKftKiz12A7gJqYPhPy5fLxbO0Jv7QyRd8TtnMEwO8SicRoKxUDR4s6SzX1jY8R4G0SFTg+Xj6eFUFaIusXGtLbbEfxuSttB8g3LercfA1wbIdQuTOGtPH58vFspCU1Y2d4Q7vB+OKkGh+6M5FIFN38cy3qHNTUNzahmzUFUlc6tIci2ZWuL9Re2x7apvuBDK/orqZBi3o8VgO6tDdgOlKhophPu19b+LUV/XTqTnsH+lMikXjEdggvaFHnqKa+8Q10tWLgtDnBWT6erR2RZ09N47xkO4dPGNyhyaKkRT0+VwO6ACFAWlPhonsXZCRd1RJ5utJg2mxn8YGfFePY9H5a1ONQU9/YCnzPdg6VvSAuH89GSnrevje86SWDCfzGQxPQAXzDdggvaVGP3w8B3TM4INpT4aItsq7QzqU9sreUF8N8L5FItNgO4SUt6nHK7AGy2nYOlZ0OJxTI5ePZ2h1+4UyH/qds57DgZeA62yG8pkU9MbcARbUCqlj1pEOBXT6eFSG8PbL+bQaz3XaUAvtqIpEo+jnlWtQTkDkE93PkfnS9KrC+tEy2ncFraRmYvTO8YY/BlMoGYvclEonf2g5RCFrUE1RT3/gIJfDWK+hSRqbbzlAIfaG2RR2h5kbbOQpgALjMdohC0aLOj2/CmwdpKp8x7pSI2bZzFEpr+JWzBuh+2HYOj30nkUg8ZztEoWhR50FNfWMP8Gl0CMSXetKyD6SoP0wcqiXy9MlpnM22c3jkaUpseqwWdZ7U1Dc+DPy77RzqYB1OeJ/tDIVmJD1pR+SZsMEU24rMAeDiRCJRlPPiR6JFnV/fAIr1KiawgnT6eD4NSPcR+0JbnredI8/+byKReNp2iELTos6jmvrGbuAS3H0HlE+0pcLdtjPY0hluWdYj++63nSNPngW+YzuEDVrUeVZT3/gAcL3tHOotralwqUxXG9au8PNnOAwEfb5/CrgokUiU5J+lFrU3voa7Ykr5QJsTKu13OEKkJbL+EIPZaTvKBHwnkUistx3CFi1qD9TUN3bhzgLR02B8oCMVLooDAybCkf65u8JN2w0miB/C/YUiO6w2V1rUHqmpb7yfIt/RKyi606EK2xn8oDe0b3FnqCVo86t3Ap9IJBIlPfVVi9pDNfWN1wC/tp2j1JXC8vFs7QtvWTlAT1BOQUkDn0okEqW2f8lBtKi992ncT6uVJQNGptnO4CctkadPMKRfsZ0jC1cmEom7bYfwAy1qj2Wm7MXQE2GsKaXl49kw4kxpiTzjGEyX7Syj+ANFelDteGhRF0BNfeMrwMfQDxcLrjctbSA6Rj3EgHQd1Rp61a8LR14BPplIJMY1W0dEDhWRG0Rkm4j0i8gbInKjiCwY8rh5IvIDEdkiIn2Zx90pIhfk5d8ij7SoC6SmvvEe9MPFgutIhfSdzAg6wm+c0SttfjsZph2IJRKJcS37F5EjgCeARcCFwFHAJ4ETgMdF5PDM4w4HngLeDXwdOAl4F/C/wH9OIL8nxJT0UWuF17y27lfAR23nKBWbe8qfuX3XjMW2c/iWkf4FqdM3hogssh0Fd1HLeyYyLi0ifwAWA0cbY7oHfb0a2AQ8bYx5z6DHHWuM6RzyHNONMa3jzeAFvaIuPP1wsYBKefl4VsSUb4+sn2Uwu21HAT47wZKeCZwPXD+4pAEyv/8P4G9EZNagx3UOfR6/lTRoURfcoA8X99jOUgpKffl4NhzpO2R3+MWtBmPzM5TvJhKJmyb4HEcDArwwwv1NmfsXjvE439GitiDz4eIFwEE/zVV+taXCJb1QIls9oT2ndMuuhyy9/K2JROJfCvh6UsDXygstaktq6hsfAz4AFP3BnDZ1OLp8PFt7whvPStH3WIFf9j7c4cB82Iy7c2XtCPfXZu7flPn1+Dy9rue0qC2qqW+8F/eDRZ2255EuJ1RuO0NgCLI98tSxhvRrBXrFDUB9IpHoz8eTGWP2AHcBX8h8ePimzO8vBe40xuzNPO6LIgevWhXx3/maWtSW1dQ3/g/uFYVOv/FAX1om2c4QJEacaTvCz/UaTI/HL9UEvDORSOT7g7svAhHgTyLyDhE5TETOBu7BHfL4YuZxl2Z+/4SIfEREjhWR40Tk8/jww34tah+oqW/8Be5fHJVnunw8d/2hjmPbQluf9PAlXgDekUgk8r7tqjFmC7AEeB64GXe74V9mXvM0Y8wrmce9DJyCW+Dfxy3ne4H3A/+Q71wTpfOofaR5bd2lwA9t5ygmV2+d2w0Hvg1W2ZmXWvxghZmyIs9P+yJwTiKRaMnz8xY1vaL2kZr6xuuBVbZzFIv+tHRqSY/fjvCzS9M4+ZzC9hJa0uOiRe0zNfWNa4Av285RDDqckM5VnwgxFS2R9VMMJh+nuG9ES3rctKizICKniIgjIgdtui4iK0XkzyKyW0S6Mxu83CoiU8f7ejX1jdfhfuihc4AnoC0VbredIehS0rtgT3jjFoOZyN/F/VfSJb+v9HhpUWfnM7jLTxeJyJtzL0WkFvgj7gcR5+BuBPM5oA2Y0I5tmWGQekCXQI9TWyrs5208A6M7tGtJj+x5cJzf/jCwPJFINOczU6nRoh6DiFQBfwvcANwOXDLo7vOAPcaYLxljnjPGvGyMuccY8wVjzK6JvnZNfeMdwEpgx0SfqxS1psJ5mZ+rYHf4xRUO/U/k+G2/Bd6VSCR0B8MJ0qIe24eB14wxz+FO9/k7ESnL3NcCzBGRc7x68Zr6xieAZQRoXwK/aHN0+XjeCKHtkaeONKS3Zfkda4CPJBKJXi9jlQot6rFdglvQAPfjDkV8IPP723DnaN4rIjtE5Hci8mURmZPPADX1ja8Cy3GX26ostadC+vc7j9KSmrkzvKHdYEbb9sAAX00kEqtK/UDafNK/yKMQkaOAM3HLGONOOr+VzPCHMcYxxlwMLAASwFbgq8CLInJCPrPU1De24m5yfks+n7eY6fLx/OsLtde2h7Y9OtLdwMcTicS1hcxUCnTByyhE5GrgCg7ci2P/zluHG2NeH+Z7ZuBORfpfY8xFXuRqXlt3JfBNL567mFz3+pxn+0zoJNs5itH8gehD5Uw6c9CXdgAfTiQStnbgK2p6RT0CEYngHuXzdeDkQbfFuLM8Lh7u+4wx+4DtwEGbveRLTX3jatz9QXSv5VHo8nHv7Ig8c2oaZ2Pmt48Ap2hJe0eLemTvwT29+kZjzIbBN+BXwMUi8lkR+ZGInCciC0XkBBH5PnAisNbLcDX1jT8FVuDuZaCGkYaZtjMUKyPpqpbI0xWG9A+BlTr9zls69DECEbkDqDTGnDfMfUcCW4Cv4e5xeyZQg/tB4ybcI35uHvp9XmheWzcV+BHuFEKVMZCm51+3zauynaOIdQGfX7NqXUH+npc6Leoi0by27u+A6/FwyCVI9g2Et/14++wFtnMUqQ3AR9asWvei7SClQoc+ikRmq9QokOuihKLU5oTabGcoUjcBS7WkC0uvqItM89q6MuA7uNMEA3c2XL4801n52J17py21naOI7AMuW7NqnU4PtUCvqItMTX3jQE194xW4y9tLdhOc1lRYz6LMn18Dx2tJ2xOxHUB5o6a+8U/Na+sWAz8B3mc7T6G1pcJ6DuXEbcX9wPAPtoOUOr2iLmI19Y27auob3w98EHjVcpyCanfCJTvskwcO0ADUakn7g45Rl4jmtXVVuNMJ/wmotBzHcz9untW4LxWps50jgJ4BPrNm1Tr9UNpH9Iq6RNTUN/bU1Dd+CzgB+J3tPF7rSYf0CK7c9OD+IF+iJe0/ekVdoprX1l0A/AA4ynYWL/y/rXNfcZAjbOcIiD8Bn1uzat0W20HU8LSoS1jz2roK3F3/vgEU1RXo1VvntoHu9TGGZ4DVa1atu8N2EDU6LWpF89q6w4BrgY9QBHOvU4a+a1+fN6Gj0IpcE/At4DdrVq3TAggALWr1pua1dSfg7hb4MSBsOc64taZCzf/ZPKfGdg4f2gh8G/j1mlXrdFP/ANGiVgdpXlu3EHcf7guBwG2+v7W3rOmXO2fW2s7hIy8DVwK3rFm1TueXB5AWtRpR89q6/SfX/D0BGsN+rrPy8f/dO+002zl8YCtwFfCzNavWpWyHUeOnRa3G1Ly2bg7wJeBSYKrlOGN6sHXSgw+3T15hO4dFm3AXrPzXmlXr9CT2IqBFrbLWvLZuGvDFzG2+5Tgj+v2eqfdt6Ko623aOAusDfgvcsGbVuvssZ1F5pkWtcta8ti4MnA9chLuPiK9mWPxyx4wHtvaVn2U7R4G8ANwI/GLNqnV7bIdR3tCiVhPSvLZuJvBx3NJeYjeN68bmWX/dk4ost53DQ73A7bhXzw/aDqO8p0Wt8iYzve8i4JNYHBpZs23O+u50KGrr9T20Affq+eY1q9btsx1GFY4Wtcq7IUMjF1DgGSPXvj53c8pIsSyNfw5YB/zPmlXrnrQdRtmhRa08lVmmvgJ4N+5hBid5/ZpXb527B2SW16/jEQd4iLfK+RXLeZQPaFGrgmpeW3cIcC5ucZ8LzMnn86cNqWtenxsGCdJS+FeBu4G7gD+vWbVOz3tUB9CiVtY0r60T3AN5919tL2eCKyE7UqEd1zfPmZeHeF4xwGbgKeBh4K41q9ZttBtJ+Z0WtfKNzMG8x+MOjyzO3E4Csi7eN/rKXrp5x8xjvUmYsxTuBkjrcYt5PfD0mlXrOqymUoGjRa18r3lt3TzeKu/9vx4PlA19bFNX5RN37JlmY5rgXtwr5cGl/NyaVet6LWRRRUaLWgVS5uq7Bjg0c6sBDn28vTr059YpJwKzBt2mjPNl0sAuoBn3RPeRbi1rVq3TU8+VZ7SoVdG77AexcmAGEMEdI87mlgY6dbc55Qda1Eop5XN6uK1SASAiPxMRIyLfHPL1szNfnz3k63eIiCMi5xY2qfKCFrVSwdELfFVERp17LiKHAO8ErgM+U4hgylta1EoFx19wF8d8c4zHXQT8Efh34P0igV2lqTK0qJUKjjTwNeBzIrJwuAeIiACfBm4xxrwGPAp8qnARlRe0qJUKEGPMH3BXNH53hIesBGYC/5v5/S+ASwoQTXlIi1qp4LkC+IiInDrMfZ8BksaY/Udw3Q4sFJHTC5ZO5Z0WtVIBY4x5DPgNcM3gr4vIdOBDwD+ISEpEUrgrJqvQDxUDLWI7gFJqXL6Bu4/I+YO+9gnclZQXDHlsHfCvInK5MaarQPlUHukVtVIBZIzZDNwArBr05UuA240xGwbfgJ/jfhD5UQtRVR5oUSsVXFfi7tAHcCTulrG3D31QZrz6DnT4I7B0CblSSvmcXlErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTPaVErpZTP/X83OiMhBMaTeAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare with bar plot.\n", + "drinks.continent.value_counts().plot(kind='pie');" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
beerspiritwineliters
continent
AF61.47169816.33962316.2641513.007547
AS37.04545560.8409099.0681822.170455
EU193.777778132.555556142.2222228.617778
NA145.434783165.73913024.5217395.995652
OC89.68750058.43750035.6250003.381250
SA175.083333114.75000062.4166676.308333
\n", + "
" + ], + "text/plain": [ + " beer spirit wine liters\n", + "continent \n", + "AF 61.471698 16.339623 16.264151 3.007547\n", + "AS 37.045455 60.840909 9.068182 2.170455\n", + "EU 193.777778 132.555556 142.222222 8.617778\n", + "NA 145.434783 165.739130 24.521739 5.995652\n", + "OC 89.687500 58.437500 35.625000 3.381250\n", + "SA 175.083333 114.750000 62.416667 6.308333" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate the mean alcohol amounts for each continent.\n", + "drinks.groupby('continent').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Side-by-side bar plots\n", + "drinks.groupby('continent').mean().plot(kind='bar');" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sort the continent x-axis by a particular column.\n", + "drinks.groupby('continent').mean().sort_values('beer').plot(kind='bar');" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "drinks.groupby('continent').mean().drop('liters', axis=1).sort_values('beer').plot(kind='bar');" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Stacked bar plot (with the liters comparison removed!)\n", + "drinks.groupby('continent').mean().drop('liters', axis=1).plot(kind='bar', stacked=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a `DataFrame` and Matplotlib commands, we can get fancy." + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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col1col2col3col4
a-1.4553430.473176-1.174481-0.751861
b0.322800-0.6906420.5921021.334866
c1.813939-0.048897-1.390069-0.780476
d0.5402150.0679321.1519560.078888
e-1.118318-0.6699350.636144-1.868485
\n", + "
" + ], + "text/plain": [ + " col1 col2 col3 col4\n", + "a -1.455343 0.473176 -1.174481 -0.751861\n", + "b 0.322800 -0.690642 0.592102 1.334866\n", + "c 1.813939 -0.048897 -1.390069 -0.780476\n", + "d 0.540215 0.067932 1.151956 0.078888\n", + "e -1.118318 -0.669935 0.636144 -1.868485" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = df.plot(kind='bar', figsize=(15,3));\n", + "\n", + "# Set the title.\n", + "ax.set_title('Some Kinda Plot Thingy', fontsize=21, y=1.01);\n", + "\n", + "# Move the legend.\n", + "ax.legend(loc=1);\n", + "\n", + "# x-axis labels\n", + "ax.set_ylabel('Important y-axis info', fontsize=16);\n", + "\n", + "# y-axis labels\n", + "ax.set_xlabel('Meaningless x-axis info', fontsize=16);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Challenge: Create a bar chart using `col1` and `col2`.\n", + "\n", + "- Give the plot a large title of your choosing. \n", + "- Move the legend to the lower-left corner." + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['col1','col2']].plot(kind='bar', figsize=(16,8));\n", + "ax.set_title('Something Important', fontsize=22);\n", + "ax.legend(loc=4);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Do the same thing but with horizontal bars.\n", + "- Move the legend to the upper-right corner." + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = df[['col1','col2']].plot(kind='barh', figsize=(16,8));\n", + "ax.set_title('Something Important', fontsize=22);\n", + "ax.legend(loc=3);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stacked works on horizontal bar charts." + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qRrsPrKpqWpLfS7I6yaIk/5DkU3Vdr+lSNgAAAKCL2i4FklyV5KKMFQP/NcmLkxzfjVAAAABA97VVClRVNSfJUJL31XX9n/bc/d0kdx/sOa1W64C36T/mCwCUrtufh3ze6m/m2/+anPHg4OAht7e7UmB5koEkt090x61Wa9wQTF3m26C7mg4AAPxMNz8P+bzV38y3/032GTvRIAAAABSq3VLg20l2Jjm9i1kAAACAHmrr8IG6rp+squqjSYarqtqZsRMNvjDJq+q6vrGbAQEAAIDumMjVB9Yk+ccklyX550keS/KfuxEKAAAA6L62S4G6rncnuXrPHwAAAGCKc6JBAAAAKJRSAAAAAAqlFAAAAIBCTeREg8Aksu78DT3ZT6vVyuDgYE/2RTPMuD9sf8OB7/+6+fY9f4cBOBJWCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChZjQdAACea855pzUdoW+85rRPJ3c+0nSMA3pk8YVNR5jUrv7hS9p/8F3dy8Ek0cCM152/ofc7BXrOSgEAAAAolFIAAAAACqUUAAAAgEIpBQAAAKBQSgEAAAAo1LilQFVVm6uq+lgvwgAAAAC9Y6UAAAAAFEopAAAAAIWa0e7jqqr6aJJ37vn6Pyb5QF3Xu7sTCwAAAOi2dlcK/Naex56U5HeSvDvJ+7oVCgAAAOi+dlcKPJrk39V1PZrkgaqqjkvye0k+crAntFqtA96m/5hv/zPj/jfZZnx80wEAmHT/NvQz/6/7X5MzHhwcPOT2dkuBe/YUAj9zd5J1VVW9oK7rJw6141arNW4Ipi7z7X9m3P/MGIAD8W9Db/h3uP9N9hk70SAAAAAUqt1S4MSqqqbt8/Vrk/z9wVYJAAAAAJNfu4cPHJPkuqqqbkjyK0l+P8mVXUsFAAAAdF27pcCnkzwvyb1JRpN8IskfdysUAAAA0H3jlgJ1XZ+2z5cXdy8KAABZuipmAAAJDUlEQVQA0EtONAgAAACFUgoAAABAoZQCAAAAUCilAAAAABSq3asPAEDPbN+wuekIfePrrVYGBwebjnEQtzUdYFJb1+bjWpN6xnSCGQPdZKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFGpG0wEYc9n685qOcPjuajrAxHxw0WNNR5hSjkmy4+GmUzRv4Q8+0XSELpqV3PlI0yHokl2b39V0hK7bvmFz0xEAYMqyUgAAAAAKpRQAAACAQikFAAAAoFBKAQAAACiUUgAAAAAKpRQAAACAQikFAAAAoFBKAQAAACjUjHYeVFXVm5L8QZKXJxlN8vUk76vr+ttdzAYAAAB0UbsrBWYnuS7JCUlOSzKS5C+qqjqqS7kAAACALmtrpUBd17fu+3VVVRckeSJjJcGdB3pOq9U64G0AgE7yOcP/gxKYcX8z3/7X5IwHBwcPub3dwwdemmRdkhOTvDhjKwymJ1k03o5brda4IUhyV9MBAGBqKv1zhs9a/c+M+5v59r/JPuO2SoEkX0jyoyS/k+SRJLuS/G0Shw8AAADAFDVuKVBV1QuT/Isk76nr+q/33LeynecCAAAAk1c7P9j/Y5LHk7yrqqqHkyxMck3GVgsAAAAAU9S4Vx+o63p3krcneUWSbyX50ySXJdnZ3WgAAABAN7V79YGvJHn5c+6e0/k4AAAAQK+Mu1IAAAAA6E9KAQAAACiUUgAAAAAK5bKCk8S68zc0HeGwtFqtDA4ONh2DLjLjMduaDtBFZtzfvnHKzeYLAByUlQIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQKKUAAAAAFEopAAAAAIVSCgAAAEChlAIAAABQqGmjo6Mde7GRkZHOvRgAAADQMfPmzZv23PusFAAAAIBCKQUAAACgUB09fAAAAACYOqwUAAAAgEIpBQAAAKBQSgEAAAAo1Ixe77CqqmlJ/jLJm5K8ra7rz/U6A91RVdXNSd6Q5Jgk25N8LckH67r+dqPBOGJVVf1ikrVJ3phkcZLHk3whyaV1Xf+/TWajc6qqeneSc5Icn2Rekl+u6/r7jYbiiFRV9Z4kv5/knyXZkuR9dV1/tdlUdEJVVa9P8v4kr8rYv7sX1HW9vtFQdExVVWuS/Osky5LsTHJPkjV1XX+r0WB0TFVV/zbJ7yRZsueuLUmurOv6i42Fomv2/J2+Ksmf1nV9cdN5nquJlQL/PsnuBvZL9/23JOcneVmSX08yLcmXq6p6fpOh6IhjkixM8h+S/EqSf5Pk9Uk+02QoOm5Wki8l+d8bzkEHVFX19iQfzdiHkOMzVtT+VVVVixoNRqfMSfKtJO9N8k8NZ6HzTktyQ5LXZewXLrsy9pnqF5sMRUf9KMkHkqxM8uokX0ny+aqqXtFoKjquqqrXJnl3kr9pOsvB9PTqA1VVvSbJ/5mxVvuxWCnQ1/Z8U/sfSf5FXdcPNp2Hzqqq6oyMrRaYX9f1E03noXOqqnp1kq/HSoEpraqqe5P8TV3X79rnvlaSz9V1vaa5ZHRaVVXbk1xspUD/qqpqTpKRJP+qruu/aDoP3VFV1daMrQi5qeksdEZVVfOS/D9J/rcklyf5VtErBaqqmpvkz5K8u67rn/RqvzSjqqrZSS5I8sMk3282DV3ygowtaXyq6SDAz6uq6qiMFfBfes6mL2XsN4/A1DI3Y5/b/7HpIHReVVXPq6rq7IytAPpa03noqI9nrIz/66aDHEovzynwfyS5ra7rv+rhPumxPcevfjjJ7CQPJjm9ruudzaai06qqmp9kXZKb67re1XQeYD8vSvK8jK3K29djSf6X3scBjtBHk9yf5O6mg9A5VVX9SsZmOjNj5+N6a13X32w2FZ1SVdW7khybscNuJ7UjKgWqqroyyR+M87BfTfJLSV6ZseNlmELanXFd15v33P50kv+SsZNavT/JZ6uqOrmua79NnoQOY74/W8L4F0keydg5BpjEDmfGAEweVVV9JMkpSU6p6/qnTeehox5M8j9n7OS+v5lkQ1VVpzmh5NRXVdWyjJ3T55S6rp9tOs94jnSlwHVJNo7zmB9m7ORzy5Nsr6pq3223VFV1d13XpxxhDrqn3RknSeq6HsnYMW+tqqruydgyt7OSfKprCTkSE5rvnkLgL/d8+Za6rp/uVjA6ZkIzpm88nuSnSV7ynPtfkuTHvY8DHI6qqv44ydkZK28fajoPnVXX9TNJvrvny/++5/xrQ0kubC4VHXJSxlbtbdnn59/nJXl9VVWrk8yeTKupj6gUqOv68Yx98Dikqqr+IMm1z7n7mxn7TfL/dSQZ6K52Z3wQ0/b8GehcIjppIvPdc16Qv8rYTN9U1/X2bmajM47w7zBTVF3Xz1RV9d8zdhnRz+6z6Y1Jbm0mFTARVVV9NMnbM1YIPNB0Hnpienxu7hefz9iV2fb1ySStjK0geKbniQ6hp1cf2FdVVaNx9YG+UVXVsRlbEfDlJP+Q5J8n+WCSf5nkZXVd+83UFLanEPhSxk4u+K+SPLnP5q17mm6muKqqjk5ydMZWdn06yW8k+fskP6zremuT2Zi4PZck/FSS9yS5K8nqjP32aUVd1z9oMhtHbs/KrWP3fPm1JFcn2ZSx78lW/0xxVVX9aZJzM/Zv7t/us2m7Ur4/VFV1dZIvJnk4YyeSfEfGLlH4G87B1p+qqtqc0q8+QN/bmbFr6v5VxpZB3ZKxHxxPUgj0hVcleW3Gflj8TpJH9/njTOb9Y3WSb2SsEEjGPqx8I8n/2lgiDltd17ckeV+SSzN2grJTkpyhEOgbr87Y389vJPmfkqzdc/uKJkPRMe/J2A+Kt+fn/819f5Oh6KijM3Z434MZm/NrkrxZIUATGlspAAAAADTLSgEAAAAolFIAAAAACqUUAAAAgEIpBQAAAKBQSgEAAAAolFIAAAAACqUUAAAAgEIpBQAAAKBQ/x+gMjIsWWKtjgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(kind='barh', stacked=True, figsize=(16,8));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Histograms: Show the distribution of a numerical variable\n", + "---\n" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 1, 1, 1, 1, 2, 3, 5, 5, 5, 5, 5,\n", + " 6, 6, 6, 6, 8, 8, 8, 9, 9, 9, 9, 12, 13,\n", + " 15, 15, 16, 16, 17, 18, 19, 19, 20, 20, 21, 21, 21,\n", + " 21, 22, 23, 25, 25, 25, 25, 26, 28, 31, 31, 31, 31,\n", + " 32, 32, 34, 36, 36, 36, 37, 42, 42, 43, 44, 45, 47,\n", + " 49, 51, 51, 52, 52, 52, 53, 56, 56, 57, 58, 60, 62,\n", + " 62, 63, 64, 69, 71, 76, 76, 77, 77, 77, 78, 79, 82,\n", + " 82, 85, 88, 89, 90, 92, 93, 93, 98, 99, 102, 105, 106,\n", + " 109, 111, 115, 120, 122, 124, 127, 128, 130, 133, 140, 142, 143,\n", + " 144, 147, 149, 149, 152, 157, 159, 162, 163, 167, 169, 171, 173,\n", + " 185, 188, 192, 193, 193, 194, 194, 196, 197, 199, 203, 206, 213,\n", + " 217, 219, 224, 224, 225, 230, 231, 233, 234, 236, 238, 240, 245,\n", + " 245, 247, 249, 251, 261, 263, 263, 270, 279, 281, 283, 284, 285,\n", + " 295, 297, 306, 313, 333, 343, 343, 346, 347, 361, 376])" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sort the beer column and mentally split it into three groups.\n", + "drinks.beer.sort_values().values" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(193, 6)" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare the above with histogram.\n", + "# About how many of the points above are in the groups 1-125, 125-250, and 250-376?\n", + "drinks.beer.plot(kind='hist', bins=5);" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Try more bins — it takes the range of the data and divides it into 20 evenly spaced bins.\n", + "drinks.beer.plot(kind='hist', bins=20);\n", + "plt.xlabel('Beer Servings');\n", + "plt.ylabel('Frequency');" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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eAhwIHNzpA0KT0y/t5dsY9I4sS2P/Iu+O14FgaLkDERGRoS7Zt/PuAh4xxiwGFhB6+m4koTlLGGMeBrDWbg83s4BrjTF3A/cBRwOXA5fEWqe1dgOwoXMjwiNS66y1nyX8HQ5wjuPwjUnZXLegZsexhz9u5AcH55Lt1VqtIiIydCX1t6C19nHgBuBW4D3gGGCmtfbzcJFR4Y/t5VcDM4Hp4fK3ANdba2f3ok7ppQvGZlGU3vGtUtsWxH7anMQWiYiIJJ+jR9Z7Vltbqy9O2M+W1nLXBw07Xk8s8LDonFIcx9nJVX1DcwxSi/ojtag/Uov6IzXl5+cn5JeX7sdITK6cELncwaoaH/M3tSavQSIiIkmmECUx2TPHw5l7Z0Ycu2+lJpiLiMjQpRAlMeu6n96ctS2sqfclqTUiIiLJpRAlMTuiNI0DOy13EAT+qtEoEREZohSiJGaO43QbjXq4vJGG9kCSWiQiIpI8ClHSK+ePyaK403IHdVruQEREhiiFKOmVDI/D5ROyIo7dt6IBLZUhIiJDjUKU9NqVE3Miljv4qNbH61ruQEREhhiFKOm1PbLdnNVluYNZKzTBXEREhhaFKIlL1wnmc9e1sLpOyx2IiMjQoRAlcTm8NI2DiiOXO3hgVUPPF4iIiAwyClESF8dx+MakyNGof37cpOUORERkyFCIkridNyaLYRmdljtoD/JoeVMSWyQiItJ/FKIkbqHlDiJHo/6yogF/QMsdiIjI4KcQJbvlaxOzSev0XbS63s/z61qS1yAREZF+ohAlu2V4lpsLxkYuvvnn5ZpgLiIig59ClOy2aybnRLxeVNnG0i1tSWqNiIhI/1CIkt22f5GXE0amRxy7d5lGo0REZHBTiJKEuHb/yNGopz9v5vN6Lb4pIiKDl0KUJMQXRqYzqcCz43UgCPet1GiUiIgMXgpRkhCO43SbG/XIx03UtmnxTRERGZwUoiRhzD5ZlGZ2fEvVtwd5+CNtTCwiIoOTQpQkTLrb4aqJkYtv3reykXYtvikiIoOQQpQk1JUTs8l0Ozter2/08/Sa5iS2SEREpG8oRElCFWe4uWRc5OKb93zYQDCo0SgRERlcFKIk4a6ZnI3T6fWH1e28vKE1ae0RERHpCwpRknDj8r2cuXdGxLHff1ifpNaIiIj0DYUo6RPfOTA34vWCijYWb9ZolIiIDB4KUdInpgxL4/guW8H8/gMtvikiIoOHQpT0me8cEDkaNWddCyu2tSepNSIiIomlECV9ZvqINKYO80Ycu1tzo0REZJBQiJI+4zhOt7lRsz/TxsQiIjI4KERJnzp9VAb75ndsTOwPwr3LNDdKREQGPoUo6VMux+HbB3TZmLi8kc3N/iS1SEREJDEUoqTPXTg2iz2z3Ttet/jhDx9qNEpERAY2hSjpc2luh+v2jxyNenCVRqNERGRgU4iSfnHZvtkMz+z4dmv2BzUaJSIiA5pClPSLDE/3J/U0GiUiIgOZQpT0G41GiYjIYKIQJf1Go1EiIjKYKERJv9JolIiIDBYKUdKvMjwON2g0SkREBgGFKOl30Uaj7tFolIiIDDAKUdLvMqOMRv11VQMbGjUaJSIiA4dClCTFZftmMzKr49uv1Q+/fq8uiS0SERHpHYUoSYpMj8P/Ozgv4tg/y5v4uKY9SS0SERHpHYUoSZovjc9iXJ5nx+tAEO54V6NRIiIyMChESdJ4XA63To0cjXp6TQvvVrUlqUUiIiKx8+y6SN8yxlwD3ASMAJYDN1hr39hJ+eOAu4DJwEbgTmvtrN7UaYx5APgCMBJoABYCP7DWrkzgW5MYnDU6g4OLvby3teM23s+W1vHUKcOS2CoREZFdS+pIlDHmIuAe4BfAFEJhZo4xZlQP5ccAz4fLTQF+CfzRGHN+L+tcAlwOTAJOARzgJWOMN5HvT3bN5TjcNi1yNOrVja28trElSS0SERGJTa9HoowxedbaRE1c+S7wD2vtA+HX1xljTgWuBm6OUv6bwEZr7XXh1yuNMYcDNwKzY63TWntfpzrXGGNuBd4HxgIfJeatSayOH5nO9BHpzN/UuuPYLYtrmX9WOm6Xk8SWiYiI9CyekahKY8xjxpjTjTHueP9iY0waMA2Y1+XUPOCoHi47Mkr5ucAhxhhvPHUaY7KBK4C1wJpY2y+J40QZjVq+zce/PmlKUotERER2LZ45UX8BLgIMUGWM+TfwiLV2SS/rGQa4gcouxyuBGT1cMxx4KUp5T7g+J9Y6w/Om7gSyCY0+nWitbaUH5eXlPZ2SBMgDTilJY+6Wjm/JnyzexoH+jWRH+S5Vf6QW9UdqUX+kFvVHahg/fnzC6+x1iLLWftcYcyOhUPJlQqM41xpjPgYeBv5lrV2b2Gb2iX8BLxKafH4j8IQx5mhrbdThj7744kuk347w8fr/VdISXri8ut3h2aZSfjQtP6JceXm5+iOFqD9Si/ojtag/Bre4ns6z1gYI3SKbZ4zJBM4FvgT8FLjdGPMG8AjwhLW2vodqqgA/UNbleBlQ0cM1FT2U94Xrc2Kt01pbC9QC5caY/wHbgPPD7ZYk2CvHw7X75/Lb9zu+Ze5d3sBlE7IZlZP0B0lFREQi7PbTedbaZmvto4Sehns2XOdxwF+BTcaYu40xuVGuawOWAid1OXUSoSfqolnUQ/kl1tr2OOuEUPhygPSdlJF+cMMBOZRlRm4H89MlWoBTRERSz279994YM57QLb0vAWOALYSWF3gIaAO+TuiJur0JjVZ1dRfwiDFmMbAgXHYkMCtc/8MA1tpLw+VnEbp1eDdwH3A0oaUKLulFneMIjTi9FG7vnsAPgFbgud34ckgC5Hhd3Do1j+sW1Ow4Nnt1M1+b1MqRZcq4IiKSOuJZ4mAYcDGh8HQoobD0HHADMMda6+9U/AZjzEbgtmh1WWsfN8YUA7cSmpu0DJhprf08XGRUl/KrjTEzgd8TWrJgI3C9tXZ2L+psBY4HvgcUEJp0Ph840lrb021E6UdfHJfF/Ssb+bC6YwHO7y2qYf5ZpXi05IGIiKQIJxgM9uoCY0wbofD1FqERp8estTU7KX8G8Edr7ZjdaWgy1NbW9u6LIwmzoKKV0+dURRy747B8vjU5RxM1U4z6I7WoP1KL+iM15efnJ+R/5PHczrsTeMhaG9Mzm9ba59BtMumlo4enY/bJxH7avOPYr96t47wxmUlslYiISId4JpZ/DLT3dNIYM9oYc2lP50Vidfsh+eR5O/6zUN8e5NbFtUlskYiISId4QtTf6XlFcYDDw2VEdktZlptbpkauZD57dTOLa5K65aOIiAgQX4ja1X3ETEJrNYnstq9OzOaAosh9oe/8NI0Wn6ariYhIcsU0J8oYMwoY3enQRGPM9ChFCwktKbB695smAh6Xw11HFnDSf7fsOPZ5s4vfvF/XbSVzERGR/hTrxPIrCC1TEAx/3BL+6Gr7iuFfS0jrRIBDS9O4bN8sHvq4Y0eeuz9s4KzRmRxUnJbElomIyFAWa4iyhNZbcsKf/wF4o0uZINAIvGut3ZywFooAPz0kn3nrW9jUFADAH4Rr36zhlTNL8GrtKBERSYKYQpS1diWwEsAYcwUw31qrW3bSbwrSXfzuyAK++HL1jmMfVrfzhw8b+N5B3XYVEhER6XO9XifKWvtQXzREZFdmjsrk/DGZzF7dsXbUr9+r44y9M5hQ4N3JlSIiIom3yxBljPkxoVt1d1hrA+HXuxK01t6+260T6eLXR+Tz8romanyhW3htAbjmjW28cLpu64mISP+KZSTqJ4RC1K8J7ZP3kxiuCQIKUZJwwzLc3LhPG7d+1LEZ8dKqdn7zfj0/nJK3kytFREQSa5chylrr2tlrkf528jA/i5oz+O/alh3Hfvt+PSeOTOfwsvSdXCkiIpI4CkQy4DgO3HN0AWWZHd++gSB8ff426toCSWyZiIgMJb2eWG6MSQeyrbXVnY4NA64CCoAnrbVvJ66JIt0Ny3Dzp2MKueDFrTuOfd7g5wdv1fLnYwuT2DIRERkq4hmJegB4cfsLY0wW8D/gDuAmYIEx5ujENE+kZzP2zODrk7Ijjj36SROzP2vq4QoREZHEiSdETQee6fT6i8BY4DRgBLACuHX3myayaz89JJ9JBZEDqt9eUMPHNe1JapGIiAwV8YSoMmBdp9dnAYustXOttZXA34EpiWicyK5kehzuP66ItE7fyQ2+IJe+Wk1Du+ZHiYhI34knRNUT2mgYY4wHOI5Ot/eAZkDPmku/OaDIy68PL4g4tqrGx3cW1hAMBpPUKhERGeziCVFLgK8ZY6YQ2oQ4B3iu0/l9gIoEtE0kZpdPyOKifTIjjj3xWTMPrmpMUotERGSwiydE3QKUEApTtxF6Gm9Jp/PnAgsS0DaRmDmOw11HFnSbH3Xz4loWVbYmqVUiIjKY9TpEWWvfBSYSCksnWGsv2n7OGFMA/An4XcJaKBKjbK+Lh79QRK63Y/uX9gB8+eVq1tT7ktgyEREZjBzNGelZbW2tvjgpqLy8nPHjx/d4/uk1zVz2anXEsYkFHuaeXkJ+mtaXTbRd9Yf0L/VHalF/pKb8/PyEbLba68U2tzPG5AJ7E5pk3q0x1tr5u9EukbidPTqT7x+cy53v1e84tqrGx1dfq+axGcV4tFGxiIgkQDwrlhcD9wLnA+4oRRxCGxBHOyfSL35wcC7lNT6eWtO849hLG1q5eXEtdx6ej+MoSImIyO6JZyTqAeBM4A/AG8C2hLZIJAFcjsOfjy3k8wYf71R1LLz5wMpGyjLd3HhQbhJbJyIig0E8Iepk4PfW2u8nujEiiZTpcXj0xGJOfHYLG5r8O47//J06CtMdvjoxJ4mtExGRgS6eWbZNwJoEt0OkTwzPcvPYScXkpUXevrtxUS1Pao89ERHZDfGEqH8SWt5AZEA4oMjL4zOKyXR3BKkg8M3525i7riV5DRMRkQEtntt5/wGON8bMBf4GrAX8XQtZaxfvZttEEubIsnQeOqGIL768FV944QpfEL7yylb+cUIRM0dl7rwCERGRLuIJUa91+nxGlPN6Ok9S0sl7ZTBreiFXvb6N7QuAtQXg0leqefD4Is4erSAlIiKxiydEXZHwVoj0kwvGZlHfFuQ7i2p2HPMF4crXqrl/eiHnj81KYutERGQg6XWIstY+1BcNEekvV0zMxu2Cby+o2TEi5Q/CVfO3Ud8e5PIJ2Ultn4iIDAxxr1gOYIwZD5QCy6y1tYlpkkjfu3TfbLwuh2+9uY1AOEkFgnDDwhrWNfi4dWqeFuQUEZGdiitEGWO+CPwK2CN86CTgFWPMMGAhcKu11iamiSJ945JxWXhd8I352/B32iXxdx80sLbBz73HFJLuVpASEZHoer3EgTHmfELLHKwEbqLTvnnW2qrw8UsT1UCRvnTB2Cwe+UJRxPIHAE981sx586qoaun24KmIiAgQ3zpRtwAvWWtPAaLNj3oLOGi3WiXSj2aOyuS504YxLCPyx2FBRRvHP7OFpVvaktQyERFJZfGEqEnAUzs5vxkoia85IskxrSSNl84oYVxe5B3u9Y1+Tnt+C//4qJFgMNjD1SIiMhTFE6IagZ1tOrYPUBVfc0SSZ3Suh3mnD+OosrSI422B0ITzq9/YRl1bIEmtExGRVBNPiHoFuNwYk9b1hDFmJHAVMHd3GyaSDEUZbp4+dRjfmtz9/wmPfdrMMU9vZmFFaxJaJiIiqSbeOVEjgCXANYRWJ59pjPkV8CEQAH6asBaK9DOvy+GOw/L523GFZHsiJ5yvbfBz+pwqbnu7lla/bu+JiAxlvQ5R1tpy4GigAvgJoafzvgt8H3gPOMZauzaBbRRJivPGZvHSGSVMyI+cJxUE7lnWwDFPb+b1jRqVEhEZquJaJ8pauxI42RhTCIwjFMY+s9ZuSWTjRJJtUqGX184q5WdLa/nLisaIc+W1Ps6eW8WFYzP5+aH5lGVpu0gRkaGkVyHKGJMOfBk4mdAE8lygHvgEeMEY86i1Vs+Dy6CS6XH45eEFnLpXBte8UcOGpsi1o574rJm561r4zoG5fGO/bLI88dwlFxGRgSbmf+2NMQcQWkjzfuBCQiEqK/ynAR4ElhtjJvVBO0WS7riRGSw4p5TL9+2+SXFde5CfLq1j2uxKHv64EV9OWWTTAAAgAElEQVRA86VERAa7mEKUMSYHeAYoIzSxfC9rbaG1di9rbSGwF3ArMBJ41hijHVxlUCpId3H30YW8eHoJ+xd5u53f1BTg+gU1HPHUZh7+uFGTz0VEBrFYR6KuAEYBp1trf2Wt3dD5pLV2g7X2l8CZwBjg8oS2UiTFHFqaxmtnlvCLw/LJT+u+v94ndT6uX1DDwU9W8Mdl9dS3a30pEZHBJtYQdTowz1r72s4KWWtfAV4kFKZEBjWPy+GayTm8d8Fwrt8/h/Qo88o3NQX40dt1THqsgu8s3Mb7WzVlUERksIh1YvkBwB9iLPsK8O1YG2CMuYbQRsYjgOXADdbaN3ZS/jjgLmAysBG401o7K9Y6jTFFhNaxOgnYm9Dq6s8Bt1prt8babpHtCtNd/OzQfL4+KZtfvVfPY5804etyF6/BF+TvHzXx94+amDbMy8Xjsjh7dCalmXqiT0RkoIp1JKqI0LpQsagMl98lY8xFwD3AL4ApwEJgjjFmVA/lxwDPh8tNAX4J/NEYc34v6hwJ7EFoXasDCD1tOB34d4zvTySqPXM83HtMIe9eUMY398smy9P9Nh/A0qp2bvpfLRMfr+DcuVU8/HEjlV2e+BMRkdQX60hUOtAeY1kf0G1LmB58F/iHtfaB8OvrjDGnAlcDN0cp/01go7X2uvDrlcaYw4Ebgdmx1GmtXQac16nOT4wxNwHPGWPyrLV1MbZdJKq9cjz86vACbjool/tXNvLQR41UNHefExUIwqsbW3k1vGDnwcVeTtozg5P3zGDKMC8eV/QQJiIiqaE360SNNcYcFkO5fWKpLLz33jTgt11OzQOO6uGyI8PnO5sLXGaM8RJaPb23dQLkAa1A065bLhKb4gw3N0/J48aDcnlhXQt/X9XIKztZ4fy9re28t7Wd37xfT47H4dDSNI4oS+OI0jSmlaSR49X6UyIiqaQ3IeqnxLYnnkNoZ4xdGQa4Cd3+66wSmNHDNcOBl6KU94Trc3pbpzGmALgdeMBa64uh3SK94nU5nLl3Jmfuncmaeh9PrW7myc+aWL6t52+3Bl8wYpTKAcbmudm/yMv+hV72L/Kyb76XvXLcpLk1YiUikgyxhqgr+rQVSRJe/+pZYAOhOVI9Ki8v75c2SWwGcn+ckQlnTIbPmhxernKzoNrNigYXQXoOQ0Hg0zo/n9b5eXpNy47jLoIMTw+yZ2aQPTMCDE8PUpwWpNgbZFha6PNCL/R1zhrI/TEYqT9Si/ojNYwfPz7hdcYUoqy1DyX8bw49FecntIBnZ2X0PIm9oofyvnB9Tqx1hgPU8+GXZ1hrW9iJvvjiS3zKy8sHRX+MB04Jf76l2c9LG1qZt66FNypaqWqJbV2pAA4bWx02tsJioj/p53KgIM1FfppDXpqLPG/oz/w0F3lpDjleF7lehxyvQ7bHRU748xyvK3zMITf8ebR5WoOlPwYL9UdqUX8MbnFtQJwI1to2Y8xSQksNPNHp1El0TBLvahFwbpdjJwFLrLXtALHUaYzJBeYQCl2nWmsbduOtiOy2kkw3l4zL4pJxWQSDQT6t87Goso23Nrfxv8o2Pq3zxXSPPJpAEKpbA1S3Quj/GPFLd0NOOGhle0PhymlLp3Td1h2ha3sAy09zGJHlZo9sN3tmuylKd+E4uvUoIoNH0kJU2F3AI8aYxcACQk/fjQRmARhjHgaw1l4aLj8LuNYYczdwH3A0odXRL+lFnbmEJprnAecA2Z22qanWBsqSbI7jMC7fy7h8L1/ZN/St2dAeYMW2dpZV+1hW3c7KmnZW1/miPvXXl1r90OoPsDVifrwbanY6kAtAptthZLaLffI87JvvZd8CDxPyPUwo8FKQrknzIjLwJDVEWWsfN8YUE9p3bwSwDJhprf08XGRUl/KrjTEzgd8TWrJgI3C9tXZ2L+qcBhwR/vzjLk06AXgtQW9PJGFyvC4OK03nsNL0iOON7QHW1PtZXe9jdb2PyqYAlc1+KpsDbG72U9Hkp6YtNfbva/YHd8zrmrc+8inFcXkeppZ4mTYs9CTigUVeTZgXkZTnBIOp8Q9sKqqtrdUXJwVpjkHvtPqD1LYFqGsLUNsWpK4tQF176FhtW4CG9iCN7UEa2gM0+oLUd/q8Yfvn7aHj/fUDke1xOHp4GsePzOCEkelMLPDoVmCM9PORWtQfqSk/Pz8h/6Ak+3aeiPSxdLdDaaZ7t7eYCQaDNPu3B6tQuGpoD/Lx5+vJLx1Bfacw1tAeZGtrgA2N/h0fjV33wtmJRl+Qeetbd4xYjchycfqoTM4anclRZWlaiFREUoJClIjExHEcsjwOWR4ozew4XlIfYPyYrJ1eGwwGqW0L8nmDj/JaHx/V+Pi4tj38p4/ALvLVpqYAf13VyF9XNTIsw8WZe2fwxXHZHFLi1QiViCSNQpSI9DnHcShIdyhIT+Og4shdoRrbA7y/tZ2lVW28s6WdRZWtO50wX9US2LGZ84R8D18en8VF47K0mbOI9DuFKBFJqmyvi6OGp3PU8NCk+WAwyKoaH69ubOW1jS0sqGjr8VbgR7U+frSkjtvfqeP8sVl8c7/sbiFNRKSvKESJSEpxHIdJhV4mFXq5ZnIOLb4gr25s4ek1zcxZ10JtlKcN2wLw70+a+PcnTRw9PI3r9s/hlD0zdKtPRPqUQpSIpLQMj8NpozI5bVQmbf4gL29o4Z/lTcxd10K0AaoFFW0sqKjmwCIv3z84l5mjMnApTIlIH1CIEpEBI83dEag2N/t5/JMm/vZRI6vru6/E/kF1O19+pZrJhR5umZrHaXtpZEpEEkvLBIvIgFSa6ea6A3JZcl4Zj55YxLHDo8+FWr7NxxdfruaMF6p4t0obEohI4ihEiciA5nY5zByVybOnlTD/rBLOHp0RtdyCijZOeHYLV71ezef1vn5upYgMRgpRIjJoHFicxkMnFLPwnFLOG5NJtJt3T3zWzGFPVfKrd+to6cUCoCIiXSlEicigs1+hl78dX8SbZ5cyY4/0budb/fCr9+o5+ulKXt2w682TRUSiUYgSkUFrcpGXJ08exlMnFzO5sPtzNJ/W+Tl33laufK2aiqbuk9NFRHZGIUpEBr0T9shg/lml3HtMAcMyuv+z93+rmzn8qUoe+6QJbcouIrFSiBKRIcHtcvjy+GyWnFfGlROyu82Xqm0L8s03tnHxy9Vs0qiUiMRAIUpEhpSCdBd3HVXAS2eUcFCxt9v5uetaOEKjUiISA4UoERmSppWk8coZJfz80DwyuuxdvH1U6srXtlHT2vNmyCIytClEiciQ5XY5XLt/Lm+cXcphJd0X63xqTTPHPL2ZhRWtSWidiKQ6hSgRGfLG53uZM3MYt0cZlVrf6OeMF6q44506fAHd3hORDgpRIiKERqWu2z+X+WeVcmBR5FypQBB+8349M5+v0mrnIrKDQpSISCf7Fnh58YwSrp2c0+3c4i1tTH9mM3PWNiehZSKSahSiRES6SHc7/PywfJ46uZiyzMh/JmvbglzycjU/WVKr23siQ5xClIhID07YI4MF55Ryyl7dNzW++8MGznqhSiudiwxhClEiIjsxLMPNYycWcfuhebi7rNC5sDJ0e2/+Jj29JzIUKUSJiOyC44QmnT932jBGZEX+s7m5OcA5c6v4/Qf1WpxTZIhRiBIRidGRZenMP6uU40akRxwPBOGnS+u44rVtNLRrcU6RoUIhSkSkF0oy3fzfycXcdFBut/33/rOmmZOf28LqOi2DIDIUKESJiPSS2+Vwy9Q8njipmIK0yCi1osbH8c9u5qX1LUlqnYj0F4UoEZE4zdgzg9fOKmVyoSfieG1bkAtf3Kp5UiKDnEKUiMhuGJ3rYd7pJZw3JjPieBDNkxIZ7BSiRER2U7bXxYPHFfKzQ/JwdZkopXlSIoOXQpSISAI4jsP1B+QyW/OkRIYMhSgRkQQ6YQ/NkxIZKhSiREQSTPOkRIYGhSgRkT6wy3lS/93CmnrNkxIZyBSiRET6yE7nSW3zcfwzm3llg+ZJiQxUClEiIn1s+zyp/brMk6ppC3LBi1u550PNkxIZiBSiRET6wehcDy+eXsK5oyPnSQWCcNuSOr76+jYaNU9KZEBRiBIR6SfZXhd/O76Qn0aZJ/V/qzVPSmSgUYgSEelHjuPw7QNyeeKkYvK7zJNavs3HCc9u5lXNkxIZEBSiRESS4MQ9MnjtzFL2K4icJ7WtNcj5L27lj5onJZLyFKJERJJkTJ6HeWeUcPbojIjjgSD8aEkdV83fRpNP86REUpVClIhIEuV4Xfzj+CJum5ZHl2lSPPlZMyf/t0r77omkKIUoEZEkcxyH7xwYfZ7Usup2jnt2M89+3pyk1olITxSiRERSxIw9M3j1zFImdZknVdcW5CuvVPPDxTW0+TVPSiRVKESJiKSQsXkeXowyTwrgz8sbOWNOFesbdHtPJBUoRImIpJjt86R+eVg+ni4TpRZvaWP6M1t4cb2WQRBJNoUoEZEU5DgOV0/OYc7MEvbMdkecq24NcOGLW7l9aS2+gG7viSSLQpSISAo7tDSN+WeVcPKe6d3O/e6DBk57foue3hNJEs+ui/QtY8w1wE3ACGA5cIO19o2dlD8OuAuYDGwE7rTWzupNncaYrwOXAFOAfGCMtXZNAt+WiEjCFGW4eWxGMXd/2MDP36mj8+DT21vaOfbpzfzmyAIu3iez50pEJOGSOhJljLkIuAf4BaFAsxCYY4wZ1UP5McDz4XJTgF8CfzTGnN/LOrOAecBPEvyWRET6hMtx+O6BuTxz6jDKMiP/6W7wBbn6jW187fVtaOs9kf6T7JGo7wL/sNY+EH59nTHmVOBq4OYo5b8JbLTWXhd+vdIYczhwIzA71jqttXcDGGMOSfQbEhHpS8cMT2fBOaVc+2YNL6yLnFw+e3UzCzZm8LfCVo4a3v32n4gkVtJGoowxacA0QiNCnc0DjurhsiOjlJ8LHGKM8cZZp4jIgDIsw82/Tyzid0fmkxE555yKVhenz6nilsW1NPs06VykLyVzJGoY4AYquxyvBGb0cM1w4KUo5T3h+pw46oxJeXn57lwuCab+SC3qj+SY7oaHDnK49aN0yhs7/k8cBP60vIFnP6vjx+PbOChP++8lk34+UsP48eMTXmeyb+cNGH3xxZf4lJeXqz9SiPojucYDx+8f5GdL6/jT8oaIc2ubXVz1QQbfmpzDLVPzyOy66JT0Of18DG7JnFheBfiBsi7Hy4CKHq6p6KG8L1xfPHWKiAxo6W6HOw7L5z+nFDM8PXLUKQjcu7yB6c9sZkFFa3IaKDJIJS1EWWvbgKXASV1OnUToibpoFvVQfom1tj3OOkVEBoXjR2bw7yktXL5vVrdz5bU+Tp9Txbfe3EZ1iz8JrRMZfJJ9O+8u4BFjzGJgAaGn70YCswCMMQ8DWGsvDZefBVxrjLkbuA84Gric0JpPMdUZrnc4oflV+4YP7WeMKQDWWmurE/82RUT6R44H7j66kLNHZ3LdghrWN0YGpn+VN/HC2hZuPzSPS8Zl4Ti6xScSr6SuE2WtfRy4AbgVeA84Bphprf08XGRU+GN7+dXATGB6uPwtwPXW2tm9qBNCwepd4F/h1/8Nvz4rwW9RRCQpTtgjg4XnlHJZlFGpra0BrnmzhjNfqGJVTXsSWicyODjBoB6B7Ultba2+OClIEzVTi/ojtUTrj4UVrXxnYQ0f1XZfidPtwNcmZvODKXkUpmsnsETTz0dqys/PT8gQrH5iREQGuaOGp/PG2aX8aGpet3Wl/EG4b2UjU2dX8NeVDdrQWKQXFKJERIaANLfD9w7KZdE5ZZy4R/fVzLe1Brnxf7VMf3ozr25oiVKDiHSlECUiMoSMyfPw5EnFPHxCEXvnuLudX1Hj49x5Wzn7hSrerWpLQgtFBg6FKBGRIcZxHM4anclb55bx42l5ZEdZhPP1Ta2c8OwWLnt1K+W1mnwuEo1ClIjIEJXhcfjugbksOb+Mi/fJjFrm6TUtHPHUZr69YBsbG7W+lEhnClEiIkPciCw3s6YX8coZJUwf0X2+lD8ID33cxMFPVnDjohrWN3R/yk9kKFKIEhERAKaWpPH0KcU8dXIxBxV7u51vC8BfVzUyZXYl31m4jbUKUzLEKUSJiMgOjuNwwh4ZvHpmCX8/vpCxud0nn7cH4O8fNTH1yUquX7CNNfUKUzI0KUSJiEg3Lsfh3DFZvHVeGXcfVcCe2d3DlC8ID3/cxLTZlXxjfjXLqzUBXYYWhSgREemR1+Vw+YRs3jm/jD8cXRB1WQR/EB7/tJmjn96MebGKNyta0W4YMhQoRImIyC6luR0u3TebJeeX8adjChgT5TYfwLz1rZwxp4oZz23hmTXN+LUCugxiClEiIhIzr8vhS+Ozefu8MmYdW8j4fE/Uckur2rn01WoOe6qSf3zUSItPYUoGH4UoERHpNY/L4eJxWbx1bin//EIRh5Z0f5oP4NM6PzcsrOHAJyv43fv11LQG+rmlIn1HIUpEROLmchzO2DuTeaeX8Pxpwzhlr4yo5TY3B7j9nTom2wp+8FaNlkeQQUEhSkREdpvjOBw1PJ3HZxSz8JxSLhmXRZTdZGj0BZm1opEpT1by1deqeU/788kAphAlIiIJtV+hl78cW8h7F5Txrck55ERJU/4gzF7dzPHPbuGsF6p4cX2LnuiTAUchSkRE+sSeOR7uOCyfZWY4t03Loywz+q+c+ZtaufDFrRz9n808Wt5Im19hSgYGhSgREelTBekuvnNgLh9cOJw/Hl3AhB6e6FtR4+OaN2s4+MkK/vBhPbVtmoQuqU0hSkRE+kW62+Er+2az6NxSHp9RzNHD06KW29gU4MdL6tjfVnDr4lpteCwpSyFKRET6lctxOGWvDP57WgmvnFHCuaMzcUWZhF7fHuTe5Q0c/GRoW5ll2lZGUoxClIiIJM3UkjT+fkIR75xfxlWTssmKMgndF95W5pinN3Pe3Cpe26hJ6JIaFKJERCTpRud6+M0RBSy7sIxbpuRSkhH919MrG1s5Z+5Wpj+zBftpE+3aVkaSSCFKRERSRlGGm5sOzuPDC4dz91EFjMuLPgn9w+p2vj5/G1OerORPyxuob9ckdOl/ClEiIpJyMjwOl0/IZvF5pfzrC0UcURp9Evr6Rj+3LK5lf1vB7Utr2dzs7+eWylCmECUiIinL5TicvncmL5xewrzTh3Hm3hlEmYNObVuQ333QwIFPVPC9RTWsqdcTfdL3FKJERGRAOKw0nUe+UMyS88q4ckI2Ge7uZVr88OCqRqbODm0r88FWbSsjfUchSkREBpR98j3cdVQBH144nO8fnEthevexqUB4W5npz2zh/HlVzN/Uqif6JOEUokREZEAqyXTzwyl5LLtwOL86PJ89s6MMTQEvb2jlrBeqmPHcFp5Z00xAYUoSRCFKREQGtGyvi2/ul8O7F5Txl2MLmVQQ/Ym+pVXtXPpqNYc/tZmHP26kVXv0yW5SiBIRkUHB63K4ZFwWC84p5bEZPT/RV17r4/oFoT36/vhhPXXao0/ipBAlIiKDistxOHWv0BN9c2YO45S9MqKW29QU4EdL6jjgCS2PIPFRiBIRkUHryLJ0Hp9RzMJzSrlon0zcUdZH0PIIEi+FKBERGfT2K/Ry3/Qi3r2gjG/0sEeflkeQ3lKIEhGRIWNUjodfH1HAhxeW8f+0PILsJoUoEREZcooz3NwcXh7hl4fFtjzCf1Y3a8NjiaAQJSIiQ1a218XVk2NbHuHy16o56IkK7nyvjsomTUIXhSgREZGI5RH+fWLPyyNsbArwi3fr2f+JCr72ejX/q9StvqFMIUpERCTM5TicNmrXyyO0B+DJz5o59fkqjn1mCw+uaqCmVetNDTUKUSIiIlF0Xh7higlZUZ/oA1hW3c73FtUy4fFNfPW1al7Z0IJfc6eGhOg3f0VERAQILY/w+6MKuW1aPv/+pIkHVzXySV33taRa/aGn+mavbmaPLDeXjMvicI/D+CS0WfqHQpSIiEgMCtJDk9C/sV82r29s5YFVjbywroVog04bmvz89oN6IJPJqys5d0wW543JZGyefu0OJupNERGRXnA5DifskcEJe2SwrsHHvz9p4l/lTXzeEP2JveXbfCzfVsfP36njoGIvZ4/O5NS9MphU4MFxot8ilIFBIUpERCROe+V4+P7Bedx4UC4LKtr4V3kjz3zeQpMv+pyo97e28/7Wdn62tI5ROW5O2SuDU/fK4Jjh6aRH25NGUppClIiIyG5yOQ7Hjkjn2BHp/KY9wH/Cc6Pmb2whQPRwtLbBzwMrG3lgZSNZHofDS9OYPiKd6SPSOajYi8elUJXqFKJEREQSKNfr4iv7ZvOVfbN5a3k5y9wjeGp1Mwsq2ujpmb0mX5BXN7by6sZWAPK8DkeWpXFYaTqHlKQxtcRLrlcP1KcahSgREZE+UpQGXx2fw1cn5lDR5Of5tS3MXdfM65taadnJoud17UHmrm9l7vpQqHKASQUeppWkcWCxl8mFXvYr9FKQrmCVTApRIiIi/WB4lpsrJ2Zz5cRsGtsDzN/Uytx1Lcxb38LGpp0v1BkEVtT4WFHjg/KO43tkuZlc5GG/Qi/j8j2MyQ19DM9y4dKk9T6X9BBljLkGuAkYASwHbrDWvrGT8scBdwGTgY3AndbaWb2p0xiTDvwWuATIBF4GrrHWrk/gWxMREYkq2+vitFGZnDYqk2AwyKd1PuZvamP+plbe2NTK1hhXP9/Q5GdDk5954RGr7TLdDqNz3YzO9bBnjpsRWds/XIzIcjM8y02e19HTgbvJSeaeP8aYi4B/AtcAb4b/vALYz1q7Nkr5McAy4G/An4Fjwn9ebK2dHWudxpi/AGcDlwFbCYWyAmCatXbHAGttba2WnE1B5eXljB+v5etShfojtag/Uks8/REIBlm5zcfizW28vaWNpVva+Ki2++KeuyvL41CU7qIw/FEU/ihMdyhMd5GX5iLH45Dldcj2uMjxOmR5HLI9DjleF1keh7QB+kRhfn5+Qhqe7JGo7wL/sNY+EH59nTHmVOBq4OYo5b8JbLTWXhd+vdIYczhwIzA7ljqNMfnAV4ErrLUvAhhjvgJ8DswA5ib0HYqIiPSCy3GYXORlcpGXKyZmA1DTGuDdqjbe3drOim3trKhu5+NaHz2spBCTJl+QJp+f9Y07mZy1y7ZCmgvS3A5pLoc0V2gz5zS3g9dF+JhDmjt03OWE9ptzuRxcgNsJ1eFynB2fOw64nY6y7vD57ee265yCtn/e43kHfnFYQdzvsydJC1HGmDRgGqHbap3NA47q4bIjw+c7mwtcZozxEvqa7arOaYC3cz3W2nXGmJXhMgpRIiKSUgrSXTsW+Nyu1R+kvNbHim3trKppZ3Wdn8/qfayu91HX1j83UgJBaPFDiz8IPT57mBoGVYgChgFuoLLL8UpCI0LRDAdeilLeE67PiaHO4YAfqIpSZnhPjS0vL+/plCSB+iO1qD9Si/ojtfRlf6QDU4ApeUBe6FgwCLU+WN/iYmOLw+ZWhy1toY+qNofN4T9bAwPzVlwqSfbtvAFDcwxSh+Z8pBb1R2pRf6SWZPbHoTs5FwwGqWsPsq01sOOjujVAdUuAbW2hPxt8QRrbgzT5AjS0B2n0BWnyBWls7zjnT+3Bpz6XzBBVRWhEqKzL8TKgoodrKnoo7wvX58RQZwWh0aphwJYuZXp8KlBERGSwcByH/DSH/DQXo3Pjr8cfCNIaCNLmh/ZAkLZA+E9/kNYAtPuDtHU6HggS/ggFsEAwdBPQHwgSIPTaHz7fURb8wcjA1jm7bX8+LuJYxPm+S3pJC1HW2jZjzFLgJOCJTqdOomOSeFeLgHO7HDsJWGKtbQeIoc6lQHv42KPha/YEJgEL430/IiIiQ43b5ZDlcsgaove1kv227wIeMcYsBhYQevpuJDALwBjzMIC19tJw+VnAtcaYu4H7gKOBywmt9xRTndbaWmPMg8CdxpjNdCxx8AHd51uJiIiIRJXU9eKttY8DNwC3Au8RWvdpprX283CRUeGP7eVXAzOB6eHytwDXb18jKsY6CZ9/CnicUNBqAM7svEaUiIiIyM4kdbHNVKfFNlOTJs6mFvVHalF/pBb1R2pK1GKb2rlQREREJA4KUSIiIiJxUIgSERERiYNClIiIiEgcFKJERERE4qAQJSIiIhIHhSgRERGROGidqJ3QOlEiIiKDj9aJEhEREUkihSgRERGROOh2noiIiEgcNBIlIiIiEgeFKBEREZE4eJLdgFRmjLkGuAkYASwHbrDWvpHcVg0+xpjpwI3ANGAkcIW19h+dzjvAbcDXgULgLeBb1trlncoUAn8Azgofega4zlpb0x/vYTAxxtwMnAdMAFqB/wE3W2uXdSqjPuknxphvAd8ARocPLQd+bq39b/i8+iKJwj8vvwD+ZK29NnxMfdJPjDE/IfS17qzSWjs8fL5P+0IjUT0wxlwE3EPoh2MKsBCYY4wZldSGDU45wDLg20BzlPPfB74HXAccCmwGXjTG5HYq8ygwFTg1/DEVeKQP2zyYHQ/8GTgK+ALgA14yxhR1KqM+6T/rgf9H6Ot3CPAK8B9jzIHh8+qLJDHGHEHol/MHXU6pT/rXR4QGO7Z/HNDpXJ/2hSaW98AY8xbwgbX2qk7HyoEnrbU3J69lg5sxpgG4dvtIVPh/ERuBe621d4SPZRL6QbjRWnufMWYSsAI4xlq7IFzmGOANYKK19qP+fyeDhzEmB6gFzrHWPqs+ST5jTDVwM3A/6oukMMbkA+8AXyM00rHMWnutfj76V3gk6gJr7f5RzvV5X2gkKgpjTBqhW0vzupyaR+h/59J/xgDD6dQX1tpmYD4dfXEk0EBotHC7BUAj6q9EyCX0b8W28Gv1SZIYY9zGmIsJjd4uRH2RTPcT+k/1q12Oq0/631hjzEZjzGpjzGPGmLHh433eFwpR0Q0D3EBll+OVhDpE+s/2r/fO+ldpxJ0AAALkSURBVGI48P/buZ+QqKIojuNf2lS0aBFlC5eGQQguWiTYHwKDiqAgDiRERQghbVoFMUHuJKKQENpKQXVWUcTQImilBQlCBRVhaYskK4jcZAotzh2dBm3xaN4L5/eBYd6bp3J9P0ev9557p919YVg1HX9Gef0LA8AYMJLOlUnOzKwtjdL+BG4AR9z9BcqiEGbWA7QApSUuK5N8PQNOEtNwPcT9GzazDeSQhQrLRWRZZnYV6CSGuueLbk8DewO0A+uBo8CQme0ptEUNysxaiVrZTnf/VXR7Gp27l6vPzewpMA6cIBbF1JVGopb2BZgHmmpebwKm8m9OQ6vc779lMQVsTPPfwMJc+CaUV2Zmdg04Bux19/GqS8okZ+4+6+7v3H001WSOAedQFkXoIGYrXpnZnJnNAbuB3nT8NX2cMimAu88QK1i3kMP7Q52oJbj7LDAKdNVc6uLPeVOpv/fED/JCFma2BtjJYhYjRI1IR9XndQDrUF6ZmNkAix2o1zWXlUnxVgGrURZFuEes/mqvejwH7qTjtyiTwqR7vRX4RA7vD63OW0ba4uAm0EsUmZ0BTgPb3H2iyLatNGn1V0s6HQb6iX06vrn7pJmdBy4Ap4hfUCVgF9Dq7j/S1ygDzcRyY4iizw/ufii3b2SFMLNB4DhwmFi1UjGT/stDmeTHzPqBh8BHosi/m9jy4KC7l5VF8czsCWl1XjpXJjkxsyvAA2CSGD26SNzrNnefqHcWqolahrvfTYVpJWLfiZfAAXWg6mI7UL3CpS89hoiCwcvAWmCQxc3S9lXeAEk3cB14lM7vA2fr2uqVqzc9P655vQ+4lI6VSX42A7fS83diT6L97l65r8ri/6NM8tMM3CamWKeJOqgdVX+r65qFRqJEREREMlBNlIiIiEgG6kSJiIiIZKBOlIiIiEgG6kSJiIiIZKBOlIiIiEgG6kSJiIiIZKBOlIiIiEgG6kSJiIiIZKBOlIiIiEgGvwEHDmky9H2+ywAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare with density plot (smooth version of a histogram).\n", + "drinks.beer.plot(kind='density', xlim=(0, 500));" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Making histograms of DataFrames — histogram of random data\n", + "df.hist(figsize=(16,8));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Single Histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "norm = np.random.standard_normal(50000)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.33276146, -0.83795193, 0.51212395, ..., 0.71104231,\n", + " -1.25707903, -1.11118177])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "norm" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(norm).hist(figsize=(16,4), bins=50);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Another bins example: Sometimes the binning makes the data look different or misleading." + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(norm).hist(figsize=(16,4), bins=300);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge: \n", + "### Create a histogram with pandas for using `MEDV` in the housing data.\n", + "- Set the bins to 20.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Do a hist for housing MEDV column\n", + "housing.MEDV.hist(bins=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
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\n", + "
" + ], + "text/plain": [ + " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", + "0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296.0 \n", + "1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242.0 \n", + "\n", + " PTRATIO B LSTAT MEDV \n", + "0 15.3 396.9 4.98 24.0 \n", + "1 17.8 396.9 9.14 21.6 " + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "housing.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [], + "source": [ + "### Type your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Grouped histograms: Show one histogram for each group." + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Reminder: Overall histogram of beer servings\n", + "drinks.beer.plot(kind='hist');" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Histogram of beer servings grouped by continent -- how might these graphs be misleading?\n", + "drinks.hist(column='beer', by='continent');" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Share the x- and y-axes.\n", + "drinks.hist(column='beer', by='continent', sharex=True, sharey=True, layout=(2, 3));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Box Plots: Show quartiles (and outliers) for one or more numerical variables\n", + "---\n", + "\n", + "We can use boxplots to quickly summarize distributions.\n", + "\n", + "**Five-number summary:**\n", + "\n", + "- min = minimum value\n", + "- 25% = first quartile (Q1) = median of the lower half of the data\n", + "- 50% = second quartile (Q2) = median of the data\n", + "- 75% = third quartile (Q3) = median of the upper half of the data\n", + "- max = maximum value\n", + "\n", + "(It's more useful than mean and standard deviation for describing skewed distributions.)\n", + "\n", + "**Interquartile Range (IQR)** = Q3 - Q1\n", + "\n", + "**Outliers:**\n", + "\n", + "- below Q1 - 1.5 * IQR\n", + "- above Q3 + 1.5 * IQR" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.boxplot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's see how box plots are generated so we can best interpret them." + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n", + " 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 5, 5,\n", + " 6, 6, 6, 7, 9, 11, 11, 12, 13, 15, 15, 16, 16,\n", + " 18, 18, 18, 18, 19, 21, 21, 22, 22, 25, 25, 27, 29,\n", + " 31, 31, 34, 35, 35, 35, 35, 38, 39, 41, 41, 42, 42,\n", + " 44, 46, 50, 51, 55, 56, 57, 60, 61, 63, 63, 65, 67,\n", + " 68, 69, 69, 69, 71, 71, 72, 74, 75, 76, 76, 79, 81,\n", + " 84, 87, 87, 88, 97, 97, 98, 98, 100, 100, 100, 100, 101,\n", + " 104, 104, 112, 114, 114, 114, 117, 117, 118, 118, 122, 122, 124,\n", + " 126, 128, 131, 132, 133, 133, 135, 137, 138, 145, 147, 151, 152,\n", + " 154, 156, 157, 158, 160, 170, 173, 173, 176, 178, 179, 186, 189,\n", + " 192, 194, 200, 202, 205, 215, 215, 216, 221, 226, 237, 244, 246,\n", + " 252, 254, 258, 286, 293, 302, 315, 326, 326, 373, 438])" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sort the spirit column.\n", + "drinks.spirit.sort_values().values" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 193.000000\n", + "mean 80.994819\n", + "std 88.284312\n", + "min 0.000000\n", + "25% 4.000000\n", + "50% 56.000000\n", + "75% 128.000000\n", + "max 438.000000\n", + "Name: spirit, dtype: float64" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Show \"five-number summary\" for spirit.\n", + "drinks.spirit.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare with box plot.\n", + "drinks.spirit.plot(kind='box');" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2X3/99VxxxRWpS2tZhhBJksieotvZ2UmlUtm3bPvAwIBP0W0gh2MkScKn6KZgT4gkSfgU3RQMIZIk1fgU3eZyOEaSpBqXbW8ue0IkSSILICtWrGDWrFkA7Nq1ixUrVgAu294o9oRIkgSsWbOGPXv2ADAyMgLAnj17WLNmTcqyWpo9IZIkAUNDQ8yfP58NGzaMWTF1aGgodWkty54QSZJqrrzyyjEPsLvyyitTl9TSDCGSJNVs3LhxzDohGzduTF1SS3M4RpIkYOHChTz33HNccMEF+9pmzpzJwoULE1bV2uwJkSSJbNn24eFh5s6dy4wZM5g7dy7Dw8Mu295AhhBJkoCHHnqIiy66iM7OTgA6Ozu56KKLeOihhxJX1rocjpEkCdi9ezef+cxnmDVr1r4VU1966SW++MUvpi6tZdkTIkkS0NHRwebNm8e0bd68mY6OjkQVtT57QiRJAi677DL6+voAWLx4MRs2bKCvr48lS5akLayFGUIkSQLWr18PwNq1a1m1ahUdHR0sWbJkX7smXtvo0rQHE0JYCVwI5IDdwDeBlTHG79bt0wZcBywDTgIeBq6KMT5Wt89JwGeB0Xuf7gSKMcYd9efbuXPn6xekwzZ3y1Z2LDk1dRnSa3htarLyKboTb86cOW37t41nTsi5wOeAs4HfAPYCXwsh/HzdPh8FeoAicBbwLPDVEMKJdfvcAZwJnF/7OBO47bDfhSRJagmHHI6JMb63/vsQwqXATuBdwN/WekE+AnwixvhXtX0+SBZELgFuDiG8hSx4nBNjrNT2+RDwjRBCLsZYncD3JEmSpoAjuTvmxNrrXqh9fzqwALh3dIcY48+AAbLeE4A88CJQf7P1g8Cuun0kSdI0ciQTUz8D/DNQqX2/oPb5mf32ewY4tW6f7THGffM9YowjIYRn617/GoODg0dQnl5rlj9LTVJem5pc7rnnHjZv3sxTTz3FaaedxtKlS3nve9976BfqgA41r+awQkgI4UbgHLJhlZePoq5xcVLQBHlgqz9LTU5em5pEyuUymzZtolQqMW/ePLZv306xWGTBggV0d3enLq8ljXs4JoTwKeBi4DdijE/WbdpW+zx/v5fMr9u2DZhXmz8yerw24JS6fSRJSqa/v59SqUShUKC9vZ1CoUCpVKK/vz91aS1rXCEkhPAZXg0gT+y3+QdkQeK8uv2PA97Nq3NAKsAJZHNDRuWB2YydJyJJUhLVapWhoSHy+TxdXV3k83mGhoaoVr13olHGs07IRuBS4D8Cj9dtejHG+GJtn17gY8AS4PvAKqAA5GKMP63t8xXgDWRriQB8Hngqxvjb9edznZCJ51oMmqy8NjWZvPWtb+XFF19k7ty5/OQnP+ENb3gDO3bs4IQTTuDxxx8/9AH0uo50nZArye6I+Xvg6bqPP6rb55PAp4CNwLeBTuA9owGk5hLgEeCe2scjZOFGkqTkXnrpJXbt2sWyZcu4//77WbZsGbt27eKll15KXVrLOmRPSLPZEzLx/GtTk5XXpiaTuXPncu211/KVr3yFarVKLpfjfe97HzfeeCM7duw49AH0uo60J0SSpGnh8ccf58knn+SVV17hySefdBimwXyAXWKn3T7EjuHGd/7M3bK1scef2cZTH1jY0HNIUiPNnj2bu+++m6VLl3LppZdy2223sXnzZmbPnp26tJZlCElsx/BIw7ujm/EgpkaHHElqtN27dzN79my+9rWvsWXLFn7hF36B2bNns3v37tSltSyHYyRJAvbu3cv69euZPXs2bW1tzJ49m/Xr17N3797UpbUsQ4gkSUBHRwfPP/88lUqFhx9+mEqlwvPPP09HR0fq0lqWwzGSJAGXXXYZfX19ACxevJgNGzbQ19fHkiVL0hbWwgwhkiQB69evB2Dt2rWsWrWKjo4OlixZsq9dE8/hGEmSlIQ9IZIkAcuXL2fLli309fWxePFi7r///n3DM/aGNIY9IZIkAbfeeisXXnght99+O4sXL+b222/nwgsv5NZbb01dWsuyJ0SSJLJ1QiqVChs3bmTevHls376dq666ynVCGsieEEmSgLa2Ns477zwKhQLt7e0UCgXOO+882tpe88gTTRB7QiRJAkZGRrjlllu46667eO655zj55JN57rnnmGwPem0l9oRIkgQsXLiQjo4OXnjhBV555RVeeOEFOjo6WLjQ52I1iiFEkqSaY489ls7OTmbMmEFnZyfHHnts6pJamiFEkiTg6aef3hc6Rodgjj32WJ5++umUZbU0Q4gkScDMmTO55pprePTRR/nWt77Fo48+yjXXXMPMmTNTl9ayDCGSJAHDw8Ns2rSJgYEB9u7dy8DAAJs2bWJ4eDh1aS3Lu2MkSQLe/OY38/73v5/e3l6q1Sq5XI7u7m6+/OUvpy6tZRlCJEkCenp6WLt2LaVSad9iZcVikdWrV6curWUZQiRJArq7uwHG9ISsXr16X7smniFEkqSa7u5uuru7GRwcZNGiRanLaXlOTJUkSUkYQiRJUhKGEEmSlIQhRJKkmnK5TD6fp6uri3w+T7lcTl1SS3NiqiRJZAHkQLfoAt4h0yD2hEiSBPT391MqlSgUCrS3t1MoFCiVSvT396curWUZQiRJAqrVKvl8fkxbPp+nWq0mqqj1GUIkSQJyuRyVSmVMW6VSIZfLJaqo9RlCJEkiW7a9WCyOeYBdsVikp6cndWkty4mpkiThsu0pGEIkSapx2fbmcjhGkiQlYU+IJKml3XDDDaxbt66h5+jt7WXlypUNPUcrMoRIklraypUrDzsgzN2ylR1LTm1QRRrlcIwkSUrCECJJkpIwhEiSpCQMIZIkKQlDiCRJSsIQIkmSkvAWXUkHdNrtQ+wYHmn4eeZu2drY489s46kPLGzoOSQdGUOIpAPaMTzS8HUSmrE0dqNDjqQj53CMJElKwhAiSZKSMIRIkqQkDCGSJCkJQ4gkSUrCECJJkpIwhEiSpCQMIZIkKQlDiCRJSsIQIkmSkjCESJKkJAwhkiQpiXE9wC6EUAD+CHgnsBBYEmO8pW57G3AdsAw4CXgYuCrG+FjdPicBnwUuqDXdCRRjjDuO/m1IkqSpZrw9IScA3wU+DPzsANs/CvQAReAs4FngqyGEE+v2uQM4Ezi/9nEmcNuRlS1Jkqa6cfWExBi/DHwZIIRwS/22Wi/IR4BPxBj/qtb2QbIgcglwcwjhLWTB45wYY6W2z4eAb4QQcjHG6sS8HUmSNFVMxJyQ04EFwL2jDTHGnwEDwNm1pjzwIvBQ3eseBHbV7SNJkqaRiQghC2qfn9mv/Zm6bQuA7THGkdGNta+frdtHkiRNI+MajkllcHAwdQlNMKsp77Px52jO+1AzeW1qOvO6mQiLFi163e0TEUK21T7PB35U1z6/bts2YF4IoW20N6Q2l+SUun1e41DFt4QHtjb8fQ4ODjb+Z9mE96Em89rUdOZ10xQTMRzzA7Igcd5oQwjhOODdvDoHpEJ2h02+7nV5YDZj54lIkqRpYrzrhJwAvKn27QzgjSGEdwDPxxh/FEL4NPCxEMITwPeBVWQTUe8AiDF+L4RwN9mdMstqx7kZuMs7YyRJmp7G2xPyq8A/1T6OB/5b7evra9s/CXwK2Ah8G+gE3hNj/GndMS4BHgHuqX08Alx6lPVLkqQparzrhNwHtL3O9hGgr/ZxsH1eAH7/sKqTJEkty2fHSJKkJAwhkiQpCUOIJElKwhAiSZKSMIRIkqQkDCGSJCkJQ4gkSUrCECJJkpIwhEiSpCQMIZIkKYlxLduuxrl2zpfY9fU7G3qOhcCuHzf0FFw75wLgysaeRJLUUgwhid2483dYc2Fj//EeHBxk0aJFDT3HjVu2sqahZ5AktRqHYyRJUhKGEEmSlIQhRJIkJWEIkSRJSRhCJElSEoYQSZKUhCFEkiQlYQiRJElJGEIkSVIShhBJkpSEy7ZLkqaU024fYsfwSMPPM3fL1sYef2YbT31gYUPPMdkZQiRJU8qO4RF2LDm1oedoxjO3Gh1ypgJDiKQD8gnPkhrNECLpgHzCs6RGc2KqJElKwhAiSZKSMIRIkqQkDCGSJCkJQ4gkSUrCECJJkpIwhEiSpCQMIZIkKQlDiCRJSsIQIkmSkjCESJKkJAwhkiQpCUOIJElKwhAiSZKSMIRIkqQkDCGSJCkJQ4gkSUrCECJJkpIwhEiSpCQMIZIkKQlDiCRJSsIQIkmSkmhPXYBg7patDT7DLHigseeYO7OtoceXJLUeQ0hiO5ac2vBzzN2ytSnnkSTpcDgcI0mSkjCESJKkJAwhkiQpCUOIJElKwhAiSZKSMIRIkqQkvEVXkjSlXDvnS+z6+p0NPcdCYNePG3oKrp1zAXBlY08yyTU9hIQQrgSWA53AY8BHYozfaHYdkqSp6cadv8OaCxv7j/fg4CCLFi1q6Dlu3LKVNQ09w+TX1OGYEMLvAZ8BPg78W+Ah4CshhDc2sw5JkpRes3tCrgVuiTFuqn1fDCGcD1wBrGxyLZIOwUcKSGqkpoWQEMJM4J3An+636V7g7GbVIWl8fKSApEZrZk/IycAxwDP7tT8D/OaBXjA4ONjomqacz3/+82zatOnQO+5n7jXj3/fyyy9n2bJlh30OTW9em2qeWYfXS3f3Rrjnc40rB+C9V8L5Vx3WS36ufaTl/5071LyatpGRkaYUEkJYCGwFFscYB+ra1wAfiDHmAHbu3NmcgqaRZkywko6E16YmK6/NiTdnzpzXjI02c2Lqc8DLwPz92ucD25pYhyRJmgSaFkJijMPAd4Dz9tt0HtldMpIkaRpp9t0xNwK3hRC+BTwI/CHZmjB/1uQ6JElSYk1dJyTG+AXgI8Aq4J+Bc4D3xxh/2Mw6JElSek1fMTXG+DmgwdOUJUnSZOcD7CRJUhKGEEmSlIQhRJIkJWEIkSRJSRhCJElSEoYQSZKUhCFEkiQl0bQH2I2XD7CTJKn1pH6AnSRJ0j6GEEmSlMSkG46RJEnTgz0hkiQpiaY/wE5HJoRwH/DdGOPVqWuRJkII4Q+ADTHGEw6x3y3AyTHG32pGXZp+QginAT8AzooxfjtxOdOKIURSKl8AvjyO/T4M7JtVbyBXA/wY6ASeS13IdGMI0UGFEGbGGIdT16HWFGP8GfCzg20PIbQDL8cYdzavKk1HMcaXgW2p65iOnJg6RdT++nsC2A1cVmv+c6A3xvhKCGEmsBb4APDzwGPAqhjjPXXHeCuwHiiQ/fL/e+CaGOO22vZbgJOBbwBFYGaM8ZSGvzlNWSGEAvBJ4N8ALwNVYCnwq8AG4GKgH3gjUAH+c4zxydpr/4C64ZgQQh/QDfwpsBo4DZhTO87JMcbfql2jH9yvjNNjjE816C1qigohnA98ETgpxrg3hPAmYBC4Ocb4h7V9/hj4deC/UDccE0I4F/gH4DeBjwNvAx4HlsUY/7HuHGcDNwBnAS8Ad5L9Tv5/zXmXU58TU6eWD5D9N8sDHwKWAR+pbdsCLAYuIfsH4S+Avw0hvB0ghNAJDADfBX6N7H+uE4AvhRDqr4PFwK8A5wP/vsHvR1NYrafiS8ADwNuBLuDTZGEEoAO4DlhCds0eA/x1COE1CxbVOZ3sGr6odsx/3W/7h8nCzBay7vNOsq50aX8PAMeRBWKAc8mGW86t2+dc4L7XOcYNwArgTOD/ArePXr8hhLcB95IFj7cDFwLvADZPTPnTg8MxU8vTwH+NMY4AT4QQfgm4NoTwJbK/OE+LMf6otu+GEMJvkoWVK4ErgEdijL2jBwshXAY8T/Y/6bdqzf8KLI0x7m7KO9JU9nPAXOBvY4z/p9b2BEAIoYvs98uHY4wP1touBZ4kC7dfO8gxZwKXxhifGW0IIezbGGPcGUIYBl4a7cGTDiTG+GII4TvAvwO+SRY4NgAran+U7STrwVjxOodZHWP8B4AQwvVkweZU4CfAcuALMcb+0Z1DCFcA/xRCOCXG+OzEv6vWYwiZWr5ZCyCjKmRDMOeQTdx7vP4XNtlfol+vff1OoBBCePEAx/1FXg0h3zWAaDxijM/XhkfuCSH8PdnwXrkuCL/Cq9cVMcYfhhCGgLdy8BDyk/oAIh2l+8jCxw1kvbyfJQsl5wLbgb1k1+jCg7z+0bqvh2qfTyELIe8E3hRC+L0HgHXaAAACVUlEQVS6fUZ7+X4RMISMg8MxrWOELNW/o+7jLWTj85D9t/67/ba/A1gE3FV3nF1NqlctIMa4hGwYZgC4AKiGEN5bt8vhTjrz+tNEug94VwjhLWQ9d9+ptY0GkcohJt/vqft69FqeUff5zxn7+/TtZL9T/3lCqp8G7AmZWrpCCG11vSG/TpbOK2QJfMFo1+EB/CMQgB/GGPccZB/psMUYHwEeAdaFEL5CNnH0XrJf0r8GPAQQQngj2V+c3zvKUw6TzS+RDuUBsh7hjwIPxBhfrk3y3wQ8A9x9FMf+R+CXY4z/ctRVTmOGkKllIfDpEMLnyGZrLwf+OMb4/RDC7cAtIYQesv85fp4s6T8ZY/xrYCNwOfCFEMI6sq7IM8iCSU+M8adNfzea0kIIp5PNOboT2Ep2Pf0KcFNtl71k1+uHye7G+hTZXVsHG4oZr6eAX6stMPUi8HyM8ZWjPKZaUN28kN8HVtaavwm8gWwS9OvNBzmUdcA3Qwh/BtwM/BR4M/DbMcYPHcVxpxWHY6aW28n+AnyYLMn/d7Jf7JDdgbCF7HbJJ8iGWArADwFijEPAu8jG6e8m+8dgI9ktv84B0ZF4Cfglstsgv092R9btZL+cIbuu/gS4leyanQFcuN+8piPxp2S9IY+Thek3HuXx1NruI/uD+z6AGOO/kl2Pu6mbs3S4YoyPkv2OPQ24n6w38AayHhaNk+uESJpw412SXdL0Zk+IJElKwhAiSZKScDhGkiQlYU+IJElKwhAiSZKSMIRIkqQkDCGSJCkJQ4gkSUrCECJJkpL4/2GLQZb5EPooAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Include multiple variables.\n", + "drinks.drop('liters', axis=1).plot(kind='box');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to use a box plot to preview the distributions in the housing data" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "housing.boxplot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Grouped box plots: Show one box plot for each group." + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Reminder: box plot of beer servings\n", + "drinks.beer.plot(kind='box');" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Box plot of beer servings grouped by continent\n", + "drinks.boxplot(column='beer', by='continent');" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Box plot of all numeric columns grouped by continent\n", + "drinks.boxplot(by='continent');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Scatter plots: Show the relationship between two numerical variables\n", + "---\n" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 0],\n", + " [ 0, 74],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 0, 0],\n", + " [ 1, 7],\n", + " [ 1, 1],\n", + " [ 1, 4],\n", + " [ 1, 1],\n", + " [ 2, 0],\n", + " [ 3, 1],\n", + " [ 5, 0],\n", + " [ 5, 0],\n", + " [ 5, 16],\n", + " [ 5, 1],\n", + " [ 5, 0],\n", + " [ 6, 1],\n", + " [ 6, 0],\n", + " [ 6, 1],\n", + " [ 6, 9],\n", + " [ 8, 0],\n", + " [ 8, 1],\n", + " [ 8, 1],\n", + " [ 9, 2],\n", + " [ 9, 0],\n", + " [ 9, 7],\n", + " [ 9, 0],\n", + " [ 12, 10],\n", + " [ 13, 0],\n", + " [ 15, 3],\n", + " [ 15, 1],\n", + " [ 16, 5],\n", + " [ 16, 0],\n", + " [ 17, 1],\n", + " [ 18, 0],\n", + " [ 19, 32],\n", + " [ 19, 2],\n", + " [ 20, 0],\n", + " [ 20, 31],\n", + " [ 21, 11],\n", + " [ 21, 11],\n", + " [ 21, 5],\n", + " [ 21, 1],\n", + " [ 22, 1],\n", + " [ 23, 0],\n", + " [ 25, 8],\n", + " [ 25, 14],\n", + " [ 25, 2],\n", + " [ 25, 7],\n", + " [ 26, 4],\n", + " [ 28, 21],\n", + " [ 31, 128],\n", + " [ 31, 6],\n", + " [ 31, 10],\n", + " [ 31, 1],\n", + " [ 32, 4],\n", + " [ 32, 1],\n", + " [ 34, 13],\n", + " [ 36, 19],\n", + " [ 36, 5],\n", + " [ 36, 1],\n", + " [ 37, 7],\n", + " [ 42, 2],\n", + " [ 42, 7],\n", + " [ 43, 0],\n", + " [ 44, 1],\n", + " [ 45, 0],\n", + " [ 47, 5],\n", + " [ 49, 8],\n", + " [ 51, 20],\n", + " [ 51, 7],\n", + " [ 52, 2],\n", + " [ 52, 149],\n", + " [ 52, 26],\n", + " [ 53, 2],\n", + " [ 56, 140],\n", + " [ 56, 1],\n", + " [ 57, 1],\n", + " [ 58, 2],\n", + " [ 60, 11],\n", + " [ 62, 18],\n", + " [ 62, 123],\n", + " [ 63, 9],\n", + " [ 64, 4],\n", + " [ 69, 2],\n", + " [ 71, 1],\n", + " [ 76, 8],\n", + " [ 76, 9],\n", + " [ 77, 8],\n", + " [ 77, 16],\n", + " [ 77, 1],\n", + " [ 78, 1],\n", + " [ 79, 8],\n", + " [ 82, 9],\n", + " [ 82, 0],\n", + " [ 85, 237],\n", + " [ 88, 0],\n", + " [ 89, 54],\n", + " [ 90, 2],\n", + " [ 92, 233],\n", + " [ 93, 5],\n", + " [ 93, 1],\n", + " [ 98, 18],\n", + " [ 99, 1],\n", + " [102, 45],\n", + " [105, 24],\n", + " [106, 86],\n", + " [109, 18],\n", + " [111, 1],\n", + " [115, 220],\n", + " [120, 11],\n", + " [122, 51],\n", + " [124, 12],\n", + " [127, 370],\n", + " [128, 7],\n", + " [130, 172],\n", + " [133, 218],\n", + " [140, 9],\n", + " [142, 42],\n", + " [143, 36],\n", + " [144, 16],\n", + " [147, 4],\n", + " [149, 120],\n", + " [149, 11],\n", + " [152, 186],\n", + " [157, 51],\n", + " [159, 3],\n", + " [162, 3],\n", + " [163, 21],\n", + " [167, 8],\n", + " [169, 129],\n", + " [171, 71],\n", + " [173, 35],\n", + " [185, 280],\n", + " [188, 7],\n", + " [192, 113],\n", + " [193, 9],\n", + " [193, 221],\n", + " [194, 339],\n", + " [194, 32],\n", + " [196, 116],\n", + " [197, 7],\n", + " [199, 28],\n", + " [203, 175],\n", + " [206, 45],\n", + " [213, 74],\n", + " [217, 45],\n", + " [219, 195],\n", + " [224, 59],\n", + " [224, 278],\n", + " [225, 81],\n", + " [230, 254],\n", + " [231, 94],\n", + " [233, 78],\n", + " [234, 185],\n", + " [236, 271],\n", + " [238, 5],\n", + " [240, 100],\n", + " [245, 312],\n", + " [245, 16],\n", + " [247, 73],\n", + " [249, 84],\n", + " [251, 190],\n", + " [261, 212],\n", + " [263, 97],\n", + " [263, 8],\n", + " [270, 276],\n", + " [279, 191],\n", + " [281, 62],\n", + " [283, 127],\n", + " [284, 112],\n", + " [285, 18],\n", + " [295, 212],\n", + " [297, 167],\n", + " [306, 23],\n", + " [313, 165],\n", + " [333, 3],\n", + " [343, 56],\n", + " [343, 56],\n", + " [346, 175],\n", + " [347, 59],\n", + " [361, 134],\n", + " [376, 1]])" + ] + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select the beer and wine columns and sort by beer.\n", + "drinks[['beer', 'wine']].sort_values('beer').values" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare with scatter plot.\n", + "drinks.plot(kind='scatter', x='beer', y='wine');" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sBb4F/I6/6CHgRmvtaL11ExERkeirq8+NMeZTwFeBe4GrAKe0zlq7B3gQ+IMad3c58G3gEuA9QB540hizrGybzwGfAW4E3gkMA08YY8obUe8FzgOu9B/nAd+rp14iIiISH/Vmbv4D8L+stdcZY46vsv4XwE217Mha+/7y58aYa4ADwG8AP/CzNjcBt1lrH/C3uRYvwLkauNsYcyZeQHOptXaDv80ngWeMMadba7fUWT8RERGJuHpHS50C/HiW9fuBZbOsn02vX579/vOTgdXA46UNrLXjwNN42R6Ai4FDwLNl+/kZcLhsGxEREUmQejM3+/E6Ec/kLGBng2W5E9gEbPCfr/b/reysvBtYW7bNiLXWLa201rrGmOGy1x9jcHCwwSLOrlX7DbOk1Tlp9YXk1Tlp9YXk1Tlp9YX41bm/v3/W9fUGNz8ErjPGfLtyhTHmHcAfA9+pc58YY24HLsVrXirU+/p6zfWmNGJwcLAl+w2zpNU5afWF5NU5afWF5NU5afWFZNa53mapL+KNkHoRuM3//39vjLkP+Dle1ub/qWeHxphvAh8B3mOtfa1s1S7/31UVL1lVtm4XsMLvn1Pan4OXXdqFiEjAJnIFNu4Y5aev7mPjjlEmci2/fhNJvLqCG2vtLuAC4GG8YdwOXufe3wL+DrjYWru31v0ZY+5kKrB5pWL1NrwA5Yqy7buBy5jqY7MBWITX96bkYmAh0/vhiIgEQjdyFGm/uue5sdaO4M07c50xZgVegDRira3r7mTGmLuAa4APAfuNMaU+MoestYf8vjN3ALcYY14BtuJljg7hDf/GWvuyMeZRvJFT1/mvvxt4WCOlRCQMsvkinf79jXQjR5H2mNftF/xAp1HX+/9Wjr76CnCr//9fB3qAu5iaxO991tryS5+rgb8CHvOfPwT8yTzKJSLSNF1+xiblOLqR4wwmcgU2DY2RzRfpyqQY6OuluyMddLEkwhqZoXgpXlPSKXgBh1OxiWut/cRc+7HWVr6u2jYuXqBz6yzb7Af+aK59iYgEYaCvl807x5jITZ24w2IiV+CFPTmGUvsCDSqqNd3p/lAyH3UFN8aY9wP/gNen5SBTc9KUc6ssExFJpDDfyHHT0BjFEAQVarqTZqs3c/Nf8Tr5ftha+0ILyiMiIm2SzRdxHC+JHmRQoaY7abZ6g5tTgc8qsBFJhmy+yMYdo+oLEVNdmRSu6yXbgwwqwtx0J9FUb3AziHebBBFJgK2jBVb1BN9sIa3pdDvQ18vOIYfJfLBBRZib7iSaGpnE79PGmJNbURgRCZfJotdcAeoLEbRWzJfT3ZHm7OUd/ObblnHRSUuUlZPYqDdz835gL/CyMeYp4A2gcrpN11p7QzMKJyLB6kx5zRXqCxE8dboVqV29wc2nyv7/yhm2cQEFNyIxcNqSNIdTjvpChIA63YrUrq7gxlqrb5NIgnRlUpytvhChoE63IrWb1wzFIiLSHup0K1I7ZWJEREQkVmbN3BhjtgFF4Axrbc5/PtcMxK619m3NKqCIiIhIPeZqlvopXjBTrHguIhJbupGjRI0+s9PNGtxYaz8223MRkTjSjRwlavSZna7eG2duw8vePA08Y60dbEmpREQCpDllJGr0mZ2u3tFS/wL8JvBRwDXGDPvLngaettZubnL5RKSC0s+tpzllJGr0mZ2urtpba6+x1p4MrAOuAb4PnAHcAfz/xpj9xpgfNL+YIlLSimn4ZbqBvl4yKe+eS2nH0ZwyEnr6zE7X0Dw31to3gXuBe40xS4H/G/g8cDrwgeYVT0QqKf3ceppTRqJGn9np6g5ujDGrgHeXPc4C8sDzwP8LPNPMAorIdEo/i4jMrt4OxVuAU4HDwEbgH4A/BTZaayeaXzwRqaRp+EVqU61/miRDvZmbfrw5b/4ZeAqvI/EvrLWa+0akTZR+FqlNtf5pyxrclzryR0u9wc0ZeE1Rl+FlbG4HDhpjnsUfMQX83Fqbb2opRURE6tTM/mmaRyZa6r0r+FZgK/A/AIwxJzAV7HwC+EtgHFjU3GKKiIjUp2r/tMnG9qWO/NHScE9EY0wvcI7/OBdveLgDdDanaCIiIo1r5vDorkyKouv1wFBH/vCrt0Px7zI1SuodQBovU7MR+BreSKkNTS6jiIhI3ZrZP00d+aOl3j43DwD7gZ8B9+EFM/9bfWxERCTO1JE/WuoNbt5hrX2xJSURERERaYJ6b7+gwEZERERCTT2iREREJFYU3IiIiEisNHTjzGYxxrwb+AvgfKAP+Li19p6y9fcA11a87Dlr7UVl23QB3wA+AvQAPwau92/uKSIiIgkTdOZmEfAi3mzH4zNs8ySwpuxRedfxO4DfwwtuLgOOAx42xmhebBERkQQKNHNjrf0R8CM4mqWpJmut3VVthTFmMd7MyB+31j7hL7sG2AG8F3is2WUWERGRcAs0uKnRpcaYYWAU+CnwBWvtsL/ufKADeLy0sbX2DWPMy8AlKLhpqWy+yMYdo7qRnIiIhErYg5tHgX8EtgHrga8CTxljzrfWZoHVQAHYU/G63f66qgYHB1tS2FbtN6y2jhYo7n8Lx3FwXZedQw5nL+8IulgtlbRjDMmrc9LqC8mrc9LqC/Grc39//6zrQx3cWGvvK3v6gjHmebwmpw/iBT0NmetNacTg4GBL9htmzw//ipNOWHv0+WS+SP/blgVYotZK4jFOWp2TVl9IXp2TVl9IZp2D7lBcF2vtEPAmUDpKu/Dub7W8YtNV/jppoc4UupGciIiETqTORsaY5cBaYKe/6HkgB1xRts0JwJnAs20vYMKctiTdtDvuioiINEvQ89wsAk71n6aAdcaYAWCf/7gV72adO/H63HwNGAb+CcBae8AY8zfA1/1Ox3uB24Ff4g0hlxbqyqQ4WzeSExGRkAk6c3MB8Av/0QN8xf///4zXUfgc4EFgK/BdYAtwsbV2rGwfN+EFO/fj3a38EPDb1tpCm+ogIiIiIRL0PDf/DDizbPL+GvaRBW70HxKwiVyBTUNjGh4uIiKBCTpzIzGzaWiMguvSmUlRcF027xyb+0UiIiJNpOBGmiqbL5JyvGRcynGYyBUDLpGIiCSNghtpqq5MSsPDRUQkUDrzSFMN9PVqeLiIiAQq1DMUS/R0d6S5UMPDRUQiKS6DQpS5ERERESA+g0IU3IiIiAgQn0EhCm5EREQEiM+gEPW5EZG2ikubvkgcDfT1snnnGBO5qe9nFCm4iSmdQCSsqrXpqxO6SDjEZVCIgpuY0glEwiqbL9Lpp7qj3KYvraULNJmPaDamyZzi0ilM4icubfrSWnEZtSPB0K9KTOkEImGliR6lFrpAk/lQs1RMhbFTmNLMAvFp05fW6vIzNinH0QWa1E3BTUyF8QSifkAiUqswXqBJdCi4kbZRR9JoKM+w7d2T48T1BWXYpO3CeIEm0aE8n7SN+gFFQ3mGraiOnCISQTq7SNuoI2k0lHfkdJRhE5EIUrOUtI3SzNFQ3pHTVYZNRCJIwY2ITFPekTMVwwybRu2JxJ+CGxGZpjzDNljcG7sTv0bticSfghsRibXKTM1YNs+iLu+nT6P2ROJJwY2IxFplpmbo4ASnLl+oyeEiQE2I0ih9q0Uk1iqn8e/r7dKovYjQ/aWkUcrciEisVU7j39vdoT42EaGJP6VRytyISKxpfqXo0sSf0ihlbkQk1jS/UnTp/lLSKAU3IiISSgpMpVHK8YmIiEisBJq5Mca8G/gL4HygD/i4tfaesvUO8GXgOmAp8Bxwg7X2pbJtlgLfAn7HX/QQcKO1drQddRAREZFwCTpzswh4EfhTYLzK+s8BnwFuBN4JDANPGGPKG17vBc4DrvQf5wHfa2GZRUREJMQCzdxYa38E/AjAGHNP+To/a3MTcJu19gF/2bV4Ac7VwN3GmDPxAppLrbUb/G0+CTxjjDndWrulXXUREQmSJrwTmRJ05mY2JwOrgcdLC6y148DTwCX+oouBQ8CzZa/7GXC4bBsRkdjThHciU8I8Wmq1/+/uiuW7gbVl24xYa93SSmuta4wZLnv9MQYHB5tZzpbvN8ySVuek1ReSV+eo1nf7cI6OssvVXBGWZUdqem1U69yopNUX4lfn/v7+WdeHObhpmbnelEYMDg62ZL9hlrQ6J62+kLw6R7m+eztHp83EnHYc+k+aexh1o3WOajNYlI9xo5JY5zA3S+3y/11VsXxV2bpdwAq/fw5wtK/OyrJtRERir90zMasZTMIszJmbbXgByhXAvwIYY7qBy4DP+ttswBtxdTFT/W4uBhYyvR+OiEjk1JMdafeEd7rvk4RZ0PPcLAJO9Z+mgHXGmAFgn7X2dWPMHcAtxphXgK3AF/E6EN8LYK192RjzKN7Iqev8/dwNPKyRUiISddWyI2GZsbfyhqS675OESdCfxguAX/iPHuAr/v//Z3/914FvAncB/xtYA7zPWlue/7wa2Aw85j82A9e0o/AiIq2UzRdJOV6re9iyI7ohqYRZ0PPc/DPgzLLeBW71HzNtsx/4oyYXTUQkcGHOjui+TxJm4fmmiIjINMqOiDQmzB2KRUQSTdkRkcYocyMiIiKxosyNyDxEdSIzEZE4U+ZGZB40kZlE0USuwMYdo/z01X28sCfHRK4QdJFEmkrBjcg8hHmorshMyoPyooJyiSEFNyLz0OWfHIDQDdUVmUl5UO4oKJcYUp8bkXkY6Otl884xJnJTfW5E6hFEv63y+XNcBeUSQwpuROZBQ3VlvoK4xUJ5UJ7S/DkSQwpuREQCVMsNKJud3SkPygeLezXCT2JHuUgRkQDV0m9Lo/JE6qPgRkQkQLXcYkGj8kTqo2apeSqli7cP59jbOapJ3EQSaD7NRrX02wrzDTRFwkjfkHkqpYs7UihdLJJQrW42asYNNMsn7tu4Y1QT90msKXMzT7V0BhSReGv170AzRuUFMSpLJCgKbuaplC4GTeImUo843ZcrCs1GuhCTJAnfNzBiSuniXJGG08UiSRSnEUDNaDZqNc2mLUmizM08ldLFy7Ij9J+kFK9IreKUSYjCZI6aTVuSRMGNiAQiCk05cRKFAEykWfRrIiKBiEJTjohEkzI3IhIIZRJEpFWUuREREZFYUeZGRERCIU7TA0iwlLkREZFQiNP0ABIsZW5ERAKgLMWx4jQ9QBD0mZqizI2ISACUpTiWJhqcH32mpuiTIyISgGy+SMpxAGUpSjQ9wPzoMzVFzVIiIgHQJIbH0vQA9Rk9Msn3XxpmPOfS0+FwwnGdZNId+kyhzI0kzESuwMYdo/z01X1s3DHKRK4QdJEkoZSlkPn6/kvDFIou3R0pCkWXHQdy+kz5Qp25McbcCny5YvFua+1qf73jr78OWAo8B9xgrX2pneWU6KjWJq0rRQmCshQyX+M5L7ABSKVSTOSK+kz5opC52QKsKXucU7buc8BngBuBdwLDwBPGmOSGqzIrtUmLSFz0dDgUi95vWLFYpKfDCbhE4RHqzI0vb63dVbnQz9rcBNxmrX3AX3YtXoBzNXB3W0spkRD2fg5RGcoZlXKKxNmHzlrJg78a4cikF9h86KyVQRcpNKIQ3JxijBkCsnjNTrdYa18DTgZWA4+XNrTWjhtjngYuQcGNVDHQ18vmnWNM5KZOymESlWazqJRTJM6WLOjk2gvWBl2MUAp7cPMc8DHgFWAl8EXgWWPMWXiBDcDuitfsBmY92oODg80tZYv3G2ZRrPOy0v9MwhvbR+p6bavru304R0dZMilXhGXZ+srYbNXqHMZyNksUP9PzlbQ6J62+EL869/f3z7o+1MGNtfaR8ufGmI3Aa8C1wMZG9zvXm9KIwcHBluw3zJJW53bUd2/n6LRms7Tj0H9ScBmRmeoctnI2S9I+05C8OietvpDMOoerw8EcrLWHgJeAfqDUD2dVxWarytaJNFWrh5JHZXhwVMrZLI0cd007IBKcUGduKhljuoEzgJ8A2/CCmCuAfy1bfxnw2aDKKPHW6r4mURkeHJVyNksjx139kkSCE+rgxhjzDeAHwOt4fW6+BCwEvmutdY0xdwC3GGNeAbbi9ck5BNwbUJEl5nRjv2Rq5LjrsyISnFAHN8AJwN8Dy4ERvH42F1lrd/jrvw70AHcxNYnf+6y1yb1bmLRU2IeSz0V1xp/yAAAYiElEQVRDuBvTyHGP+mdFJMpCHdxYa/9wjvUucKv/EGm5sA8ln4uaShrTyHGP+mdFJMpCHdyIhE3U+5qoqaQxjRz3qH9WRKJMeVKRBOnKpCi6LoCaSkQktvTLJpIgSRvCLSLJpOBGJEG6O9Kcu6aXrkyKbL7IpqGxUMy/ojlhRKSZFNyIJEy1TsVBC2OZRCS6FNyIJEw2XyTlOEB4OhWHsUwiEl0aLRVimpNEWiGM86+0o0z6PokkR/C/ajKjUn+IbfvG+eXQGPdv2qW+CDJvYexU3I4yqelLJDmUuQmxbL7I9v0TFF2XjkyKiUJRk67JvIVx/pV2lElz/Igkh4KbEOvKpJjMF+nIpHBdl660fpBFGjVT01d5c9XePTlOXF9Qc5VIxKlZKsQG+rwhu5OFIikHTlraHYr+ESJRNFPTV3lzVVHNVSKxoMxNiHV3pLlqYHVo70+TzRfZuGNUHTQlEmZq+ipvrnLUXCUSCwpuQi6M/SNKto4WWNWT3JswavRNPJQ3V7khGT0mIvOjb7E0bLJIoucm0eibeChvrkqFZPSYtJ9myY4XZW6kYZ0p7+aLYZovpZ00+iYeyrOjg8W9yr4lVLWLlSRlouNGwU1MtLuJZCJXIFd0eXn3IVwcTl/ew/kJ+yEI42R4YaDmOokiXazEi36NY6LWJpJmpV43DY2RduCs1b28fdVCujrSiTuBhXEyvDBQc51EUZc/Wg7QxUoMKHMTE7VedTQr9ZrNF3Hq7G8TtSv6yvIuyk+vY7XO3lGrYyvoCliaqV3fqYG+3tCOTJX6KTSNiVqvOpp1g8Iuf2LBuf5euahd0VeWd3B07ixX1OrYCroClmZq13eqdLHym29bxkUnLUncRUncKHPTIu2+gq/1qqNZ/UQG+nrZOeQ1ydR6ldOsK/p2vbeV5c3WUNyoZC1a+R7qCjhcop5NjMp3SsJFwU2LtLrnfbUfrFr236wTT3dHmrOXd9D/tmU1v6ZZgVW7RjVUlrezhuJGpZNxK9/DMM/NlERRHwUUle+UhIs+JS3SrOafmdSTqi3vRLxpaIxz1/QGknptVgfcVr+3JZXlPW3J3O9VVDoZt+s9lOBF/VhH5Tsl4aLMTYu0+mqjnlRtWK7cmnVF364rucryDg6O1P2aRrW6KUFXw8kR9WOtTKA0QsFNi9TS/FPrCWz0yCTff2mY8ZxLT4fDh85aOecPVvm+t+45wmnLe+jMpAO5cmv2iTpufTqqvT+tDkhnew91l+x4idv3RaQWCm5apLsjzblreo+eJDYNjR1zUq/1BPb9l4YpFF26O1IUikUe/NUIV507+w01y/edceC1fROcsXJhIFdu5WWZyBW4f9Mu1i/raSjQmS1QimrHyWqfg2Z3oqynj1a1u2RH/co5qp+NZlDmQ5JIwU0LzRW81HoCG895gQ1AKpXiyKS3nT/atqryfZ9y/AIG9xyua2RTNXPN+1JLWbbvnyBbKDackfj56wd4ZfgQO8cmcV345VsHOWPVQlwctu07wvqlPXR1pCPVcbLa56DZTQn1ZIJacZfsoIOLsDTNxknQx1RkNgpummAiV+CFPTmGUvumfckrT1oHJ/Js3DF69MfAwa3p3kw9HQ6FYpFUKkWxWKSnw+Hnrx/g1b1HyBchk4JcweWyU5YefU35yTGTdjh7dS8XnbTkaHnLy1Eq71w/VuUniPFcng2vZ9nbuW/OH7bysngBVuOdG7eOHGbXoUkcxzvxvjB8iI6ONGesXEi24LJ9dILTVyyMVMfJaoFMtaaE+ZxM6skEteIu2e0KLmZ6j6rVv9q2UjsFjBJm0epZFlKbhsa8ocIVI5cqJzN768DEtB8Dx0lNGwVwxooFVW+NcOVpx7NrLMuvR46w82CWK087nq0jh3GBTNrBBbaMHJpWpjNWLODXew6z6a0xBkcOc8aKBYD343/fpp1s3jnGtn1HGM/lj5Z3phFYpWDoF2+NMThyhMl8kR37J8gWqWm0Vvloh86Mw0lLu4++J/WeOF3HoVDk6OzIxSLkC96JuivtMOmftOvZd9B3A642GqTahGLzmcysnon1WnGX7HaN2JnpPapW/zhMuBjkZzfqo7Ak3pS5aYKxiRw7xgrs3zlGR9ph7XFdwLEd+dYe1zXtxyDvFrlw3VS2ZeOO0apXQttHs1x+6vFHr+x3HMjiOg75QpHhwzlyhSKplMNEbqrj5ysjR1i3pNsLQgouD/5qmKvOXcOmoTEm81P9KV7fn2X9Mu9EN9PVfekkUMogbds/Trbg0pmq7YetvM3/wnWL2bxzjIPjOd46mGXt4m427hitOQtx+vIedh3Mki8UyaRSLF+YIeOX46Sl3by+P1t381vQV6C19omYTz+cuTqVzpTxaNZdsts1Ymem96ha/Z97/UDkJ4cL8rMb9VFYEm+xCW6MMdcDnwXWAC8BN1lrn2nH337rYJZi0SWT9jr8Dh2cBI49aZWCl2o/BhO5Ai/uHMPFoSPtcPKyHvIF70qz2g/26ct7eHrbfnIFSKccVi/qnPbDlvWzK0UXOtMpsrnitI6qRdf1+lMUikfLMdOPVek1Jy/rYfu+CY5Mek1jxy3wsyd1/LCV3pONO0bp78qQcpy6fpTftW4JjgNbRsbBdTl5WQ+dGS/L0NOR4aqBpXWfjKMyA+p8TiZzBVDzOUmWB0ZeHtH7b3mQ1K4ROzO9R9XqX3XbyZYUq2WC/OxqFFZzqQ9Tc8UiuDHGXAXcCVwP/Iv/7yPGmLdba19v9d9fu7ibfftS5AsuHekUfYu7qm53xooFPPjSCEdyxaNDuks2DY2RTnknhaLr8tq+I7xtmddMNThymHTK4ZTje8ikU3RlUpyxYgH//No+iq5DB14wVP7D1pVJedmVtHcPKG+kkvelWb+0m+2jE0zmvP4vpR+lM1Ys4MFfDXNk0p1WvtJJoDOT5tQVC442nTz08wNseusgw4cnWbmgg8l8kXetW1xT/52ZfpTnel13R5rLTjmey05p/HhV/o1a+z4FrZUnk/mcJMsDo1d2HwIHzli5aFqQ1K4RO/W8R9W2fWP73HMZhUmQ2RONwmquoDPIcROL4Ab4c+Aea+13/Oc3GmOuBD4N3NzKP/x3G37N/3zhgP9sN0sycFbfcewZm+CRV/awP1sg5TicfFyGwwUYnfCev21ZF88v7uQ3Tj6eTUNj/PyNAwwfmGD/RIEi4ACDw4dIpRxwHfaP53jujVFWLuhkxaJOHtu6h2LR5cQl3WRSDm8enOTMlQuPluuMFQt4fOsIu8ZydKTg9OULcOg5GsCMlwUwlU1Zv947zvZ9E9z+9DbWLV1AX28XI0cm6evtore742jAkUk5dKdTnLjY60Pz6r4jOI6L6zo89eo+cF3WLu6mf/mCY76oM/0ol77g4PLy7kO8uGuMs1f3HjPk++evj7JlzziO63LaioW8a93io6+fKTAqBTUv7DpExvFGkRVcl3zBZdv+w8cEdeWvqXfOl1ZchbXyZDKfk2R5YJQvepkbCCYLVs97FIeTs7In8RGVDHI9gsxGRT64McZ0AucD36hY9ThwSav//lRg4xnNw54jWR58+QhjEwV/VA/8am+OFLBkQQbXdXl1X5buHQdZ2NVJwXU5OJFnbLJAJu2dGA5mc6QnM6RSDmPZPL1dGVJFl9Fsno6ONEW/U+3woUlWLerCoTjth+2VkSOsXtSF4zjkC7B3PI/juLwycoRTly88ehLbsucIF67rBKaasnYdzJJKOew9VCCTzpJJp+hfvpBMypl2MpgsQt7xmuMA8gWXV0bGyTjg4jWX7TqUpSOT4uSlPdPep5l+lEtf8G37xnEB1z222WrT0Biv7h0n7QCOw6v7jtDVkcJ1mfXK52jg5Lq4jsO2/eOcvmIhr+0b58zVi6q+J43O+RK1q7D5nCSnjczz+z+B7gjeDnEI0MQTxz5MQf4ORj64AZYDaWB3xfLdwHurvWBwcLClBcpO5BifLHoZGBdc1/Wam4B8Pg9AwYXRA2Nsf3OSjhSksjmOTBQoul7Q4hSLTBQLuK7LeB5S+RyO4+C6cKA4SdpxyBeLrFyYYWlxgpTj8Mb2146WYftwjv0H8yzwTzb5Ary10+tQ0FH2nckVYVnWS8Xv3ZNj1/48ByaKpICJrMtBd5LdhXF6cwembQvQmYIDo/soXak7fr1cx2F8vACuV+eO3BGOy2UYLO6d9j4dveXmJEebA/buyVF0XXYfKJB2vCuYXe7BaX97+3CO4YP5oyfSvAs9kwdnrVvpdR0pOHgw7wVCOCyaHGXfgQK73LGqryu9Brzjsu2NoWn7nEn566qVJYyqHQ+Y+/uyKF9kcLRAtggL3CKO47DjzYN0puC0JemablsRJq3+fQijpNU5jPUt/x614rsTRJ1b+TvY398/6/o4BDd1m+tNqctPnj9m0YIFXfQUc+Qn8kfnY3Fw6UhBJpOhUCzS6Ti8/cTlrF/t9U0Yyxwh33kEBwdwGDmcZWlPBwcmcuTG8yzsybCku4P94zkWL+xi5aIORg5PcvyiLk5Y03tMum9v5yjjuw9RmufPAdavWgQw7eog7Tj0+/PfnLi+wNjmnYzvnSCTcliw0BvquWppD6v9vjalbQGy+S2sWLXqaOfe01Z4zWKv7j3CkmKR4bFJ8q7L6qU9fGBgTU3pyBPXF9i8c4z96ammo0zamfa3G6lb6XUF12X5yiKv7ZugUChywppeVq4qkE6nqr6u9JqU4/DWW2+x/oS10/Y5k/LXVStLVAwODtb0fTm7DWVph1rrGydJq3OY69uq71FQdQ7ydzAOwc0eoACsqli+CtjV6j/+8XMWT2uaWtMNZ69cRO+JKR7Zsof9EwVc4O3Hd9DV2cHOwzlSpDl/7XH8/jlef5fNO8dYu7iLfLFAruACKVYuTFMkxepFnV67FoDr0tfbhYs3U/G7T17Ku9ZVv7P3QF8vuUJhWuBRamqYqfmhuyPNVeeu4V/fOMiWkUNMFlw60w5rF3dVvRtvVyZ1TOfeiVwBx3HYMnKI1cd1c/rynhnLWE0pzX7umqlmksq/3UjdSq/bvHOMfMHhzJULp01eONPryptr6pnzRX0hRCTpgvwddFx3ljn8I8IY8xyw2Vp7XdmyrcAD1tqbAQ4cONDSiob5aqBVklbnpNUXklfnpNUXklfnpNUX4l/nxYsXO5XL4pC5Abgd+J4x5ufAz4BPAX3AXwdaKhEREWm76HfHBqy19wM3AV8ENgGXAh+w1u4ItGAiIiLSdnHJ3GCt/Tbw7aDLISIiIsGKReZGREREpETBjYiIiMSKghsRERGJFQU3IiIiEisKbkRERCRWFNyIiIhIrCi4ERERkViJxe0XatHq2y+IiIhI+1W7/YIyNyIiIhIrCm5EREQkVhLTLCUiIiLJoMyNiIiIxIqCGxEREYmV2NwVPCjGmOuBzwJrgJeAm6y1zwRbquYwxtwKfLli8W5r7Wp/veOvvw5YCjwH3GCtfamd5ZwPY8y7gb8Azgf6gI9ba+8pWz9nHY0xS4FvAb/jL3oIuNFaO9qOOtSjhvreA1xb8bLnrLUXlW3TBXwD+AjQA/wYuN5a+2ZLC98AY8zNwIeB04EssBG42Vr7Ytk2cTvGtdT5HmJynI0xNwCfBNb7i14Cvmqt/aG/PlbHF2qq8z3E5Pg2SpmbeTDGXAXcCfwX4N8AzwKPGGPWBVqw5tqCF7iVHueUrfsc8BngRuCdwDDwhDGmt92FnIdFwIvAnwLjVdbXUsd7gfOAK/3HecD3Wljm+ZirvgBPMv2Yf6Bi/R3A7+H9KF4GHAc8bIxJt6LA83Q58G3gEuA9QB540hizrGybuB3jy5m7zhCf4/wm8Hm8Y3IB8BTwfWPMO/z1cTu+MHedIT7HtyHK3MzPnwP3WGu/4z+/0RhzJfBp4ObgitVUeWvtrsqF/tXQTcBt1toH/GXX4v1wXA3c3dZSNsha+yPgR3D0aueoWupojDkT78fwUmvtBn+bTwLPGGNOt9ZuaVddajFbfctkqx1z/zWLgU/gZXye8JddA+wA3gs81uwyz4e19v3lz/2yHgB+A/hBTI/xrHUuWxWL42ytfbBi0ReMMZ8GLjbGvEDMji/MXmfgl/6yWBzfRilz0yBjTCdeav/xilWP410xxcUpxpghY8w2Y8x9xphT/OUnA6spq7+1dhx4mvjUv5Y6XgwcwsvalfwMOEx034dLjTHDxpitxpjvGGNWlq07H+hg+nvyBvAy0ahvL97v3n7/eRKOcWWdS2J3nI0xaWPMH+JlKJ8lAce3Sp1LYnd866HgpnHLgTSwu2L5brwvUxw8B3wM76rmj/Hq9awx5nim6hjn+tdSx9XAiLX26JwK/v8PE8334VHgo8C/xUvlvwt4ym+fB69OBWBPxeuictzvBDYBG/znSTjGlXWGmB1nY8w5xphDeH2M/hr4XWvtC8T4+M5SZ4jZ8W2EmqVkRtbaR8qfG2M2Aq/hdVTbGEihpKWstfeVPX3BGPM8Xqr6g8A/BlOq5jDG3A5citf8UAi6PO0wU51jeJy3AAPAYuD3ge8aYy4PtEStV7XO1toXY3h866bMTeP24EW+qyqWrwKqtnNGnbX2EF6v/H6m6hjn+tdSx13ACr/vBnC0r85KYvA+WGuH8Dov9vuLduFlLJdXbBrq426M+SZex8n3WGtfK1sV22M8S52PEfXjbK2dtNb+2lr7vLX2ZrxM1Z8R4+M7S52rbRvp49sIBTcNstZOAs8DV1SsuoLp7Z6xYYzpBs4AdgLb8L4EV1Ssv4z41L+WOm7Aa+u+uOx1FwMLicH7YIxZDqzFO+bgfeZzTH9PTgDOJKT1NcbcydRJ/pWK1bE8xnPUudr2kT/OFVJAFzE9vjMo1fkYMTy+c9LtF+bBHwr+PeB6vA5on8LrgX6WtXZHkGVrBmPMN/BGV7yOdxXzJeDdwDnW2h3GmM8DtwAfB7YCX/TXn26tHQum1PUxxiwCTvWfPgvchjfHxT5r7eu11NEY8whwAt48GgD/Hdhurf3ttlWkRrPV13/cCjyA9yO4HvgacCJwZll9/z/gt/H6Y+0FbsebP+T8sDX3GGPuAq4BPgT8qmzVIT8TSQyP8ax19j8DtxKT42yMuQ34IfAGXufpq/GGSX/QWvtI3I4vzF5n4BlidHwbpT4382Ctvd/vXPtFvHkEXgQ+EIfAxncC8Pd4qcsRvH42F5XV7+t4kz/dxdTkWO+LSmDjuwD4Sdnzr/iP7+J96Wup49XAXzE1fPIh4E9aWurGzVbfT+PNY/RRYAneD+NPAFNR35vw5k65n6nJvz4a0h/E6/1/f1yx/Ct4JwCI3zGeq84F4nWcVwN/6/97AG8o9G9Za0vHKm7HF2apszGmh3gd34YocyMiIiKxoj43IiIiEisKbkRERCRWFNyIiIhIrCi4ERERkVhRcCMiIiKxouBGREREYkXz3IhIaBhjbgW+DKyx1sZiGngRaT9lbkRERCRWFNyIiIhIrCi4ERGpwhizMOgyiEhj1OdGRMJomTHmdrwbARYBC3ymdLNLAGPM+4AvAOf7i/4F+I/W2k3lOzLGnAZ8Ffi3eHd6fhn4S2vtP5Rt8zHgf/rbfAi4Cu9msU4rKiciraXMjYiE0X14Nzm8BfgHvLs129JKY8zVwKPABHAz3g0hTwGeMcacUbbdmXg3SjwH7waKn8G7A/L/Msb8UZW/+1fAecBfAv+p2ZUSkfZQ5kZEwugt4APWWhfAGLMT+JIx5r3ABuC/AfdYa/996QXGmL8BtuAFJVf7i+8EhoALrLXj/rK7jDGPA7cZY/6u9Dd8h4DLrbX5FtZNRFpMwY2IhNF/qwg6vgV8Cfh3wCK8rM69xpjlFa97Bvi/AIwxy4D3Al8BFlb0oXkUuAI4DS8gKvmOAhuR6FOzlIiE0WD5E2vtHmA/sB4vIAF4AhipeHwYr68MwKl4fWZurbLdf/W3KW1b8mrzqiAiQVHmRkSipnRR9jG85qu5tvsm8KMZtnmx4vl41a1EJFIU3IhIGPUDW0tP/OanpcB2prIrI9baJ2fZx2v+v/k5thORmFGzlIiE0Z8YY8qHYf8H/98fAo8Bo8AtxpjOyhcaY1YAWGuHgZ8Af2yMWTvTdiISP8rciEgYrQV+ZIx5GDgX+GPgcWvtEwDGmE8Bfwf8whjz98BuYB1wJfASXpMVwKeBnwG/NMZ8By/rsxK4EHg7Xr8cEYkZZW5EJIw+gteB+L8ABvgfwB+UVlpr78cbFfU63tw138Ib/v0ycHfZdluAC4CHgI8CdwHX413YfakN9RCRADiu6869lYiIiEhEKHMjIiIisaLgRkRERGJFwY2IiIjEioIbERERiRUFNyIiIhIrCm5EREQkVhTciIiISKwouBEREZFYUXAjIiIisaLgRkRERGLl/wC5w3kQeeM2tQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Add transparency (great for plotting several graphs on top of each other, or for illustrating density!).\n", + "drinks.plot(kind='scatter', x='beer', y='wine', alpha=0.3);" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Vary point color by spirit servings.\n", + "drinks.plot(kind='scatter', x='beer', y='wine', c='spirit', colormap='Blues');" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Scatter matrix of three numerical columns\n", + "pd.plotting.scatter_matrix(drinks[['beer', 'spirit', 'wine']], figsize=(10, 8));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting `DataFrames`" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(x='col3', y='col4', kind='scatter', color='dodgerblue',figsize=(15,7), s=500);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to view the association between the variables `ZN` and `INDUS` using a scatter plot" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qG4UQDibe/O3TSb5oCOEE4EJgMfE55x+IoujKiuevBM6Z8GkPR1F0bMK6JWmvjI5RDuS5SZ7NUSJ+/tGz+x0xlyRJ0l5LFMqjKCoAa0IIxwCHAb+KoujhSZq2A2cA9yX8/3cDPyXeqf2Vdmu/Czi74uPRhF9bkvbaBfd0TrqZRqUScOG9nVy6crgWJUmSJKkBJR0pByCKoseAx6Z4fhPx+vOkX+9W4FbYNSo+mZEoil6YQZmStM8efK4NJh0lr5TjgWfbAEO5JEmS9k6iUB5CeM0UT5eAHVEUbZ+dkvawLITwIvA74AfA30VR9GKV/l+SBMRrx2eznSRJkjSZpCPlLzD5sWi7hBB+B6wH/msURb/ax7rG3Qb8G/A0cCjw34B7QgiLoygamewT+vr6Zul/XV1ZqVNq1r6a401MP1Iery5v1u9RvfHnoKywryoL7KfKiiz01d7e3imfTxrK/56pQ3kX8EbgXcDJIYQ3z0Ywj6LouooPfxJC+BGwGVhNHNb3MN1fuB709fVlok6pmfvqcb8qcN+z001hL3H8QYWm/R7Vk2buq8oW+6qywH6qrGiUvpp0o7eLkrQLIbwReAj4HPCBvS/rFet4LoTwayD733lJde2SFcMsWdc25d3IHHDxcteTS5Ikae+1zOYXi6LoSeDrwKrZ/LrjQgj7AwcCz1fj60vSuPZWuOLkwfLhZxOjeYkcJa44edDj0CRJkrRPZrT7ekJ9wO8laRhC6AYOL3/YAhwcQjga2FJ+XATcSBzCDwX+EXgR+PasVixJkzhm4RiPnt3PBfd08uBzbRRL0JKDZQfu5OLlwwZySZIk7bNqhPIDgW0J2x4D3Fvx8efLj6uAvwaOAt4PvIo4mN8LhCru9C5JL9PeCl9bNYzHnkmSJKkaZjWUhxBeRbyWfEOS9lEUfZ+pd1F62yyUJUmSJElSXUp6TnmYpkkncARwJvAa4L/vY12SJEmSJDW8pCPl1xHvdDTdob2PA+dGUfTYPlXVgLYNwXvWz+W5wVZgMWyAA+eOceNpg3S1pV2dJEmSJCkNSUP58mme3wFsjqLohX2spyHd3Jfnsxu6yh/tvq/x7GArx17dwxeOG2JNbyGd4iRJkiRJqUl6TvkPql1IoxraSTmQTzbJIL722Q1drDq03xFzSZIkSWoye7XRW/kos0OBecB24OkoigZnsa6G8Y6b5iZq986b5nLbu/0WSpIkSVIzmVEoDyGcDPwdcCzxueLjxkIIDwL/PYqiO2exvsx7frCV6Zfi58przSVJkiRJzaRl+iaxEMIngPXAnwDfBy4F/qH8533AUuB7IYSPz36ZkiRJkiQ1nqRHoh0JfAl4CHhvFEX/3yRtDgauBS4OIdwZRdGTs1qpJEmSJEkNJulI+V8CA8CpkwVygCiKngH+HBgE/mJ2ypMkSZIkqXElDeXLgG9FUbR1qkZRFG0BvgWcuK+FNYrDuwvER7xPpcQb5nkkmiRJkiQ1m6Sh/PXA/07Y9n+X2wu45rShRO2+uSZZO0mSJElS40gaynuAbQnb9pfbC5iTh7XLh4hHyyeOmMfX1i4fYs5eHU4nSZIkScqypKG8lennYI8rzeDrNoUVhxR45H39HDl/fCp7/Fi0IL6+4hCnrkuSJElSM5rJ+OwHQgjLErTr3dtiGtmcPFxfnqLe19dHb6/fJkmSJElqdjMJ5SeVH0kkHVWXJEmSJKlpJQrlURQ5HX0fbRmAd3y3m60jLcBi2ADzO4qsP32A7va0q5MkSZIkpcGwXQNXb8zzlht6yoE8t+uxdaSFpdf0cPVGd3mTJEmSpGZkKK+ygVH40qNd7A7jleJrX3q0i4HR2tcmSZIkSUpXoiHaEMI9M/y6pSiKkq4/b2irb+xO1O7UG7v5/hkDVa5GkiRJklRPks6bfg3JNm/rBg5J2LYp7J6yPpUcW0actCBJkiRJzSbpRm9/ONXzIYRu4DzggvKl9ftYlyRJkiRJDW+fdhgLIcwFPk4cxl8NfA+4KIqix2ahNkmSJEmSGtpehfIQQhdxGP8bYH92h/FHZ7G2hjC/o5hgCnuJBR3FWpUkSZIkSaoTMwrlk4Tx24jD+CNVqK0hrD99gKXX9Ezb7pbT3eRNkiRJkppN0t3Xu4CPEYfx38Mwnlh3O3xyyVD5WDR4+Yh5vB/eJ5cM0d1e89IkSZIkSSlLOlL+NPHI+OPAXwAPAYQQXvNKnxBF0Yv7XF2DOGtRgTW9/ay+sbs8lT22oKPILacPGMglSZIkqUklDeW/V/7zj4FvJ/yc1pmX07i62+EH5XPI+/r66O3tTbkiSZIkSVLakobyz1e1iiawbQjes34uzw22AothAxw4d4wbTxukqy3t6iRJkiRJaUh6TrmhfB/c3Jfnsxv2XFP+7GArx17dwxeOG2JNbyGd4iRJkiRJqUm60dubZ/qF3QQuNrSTciCf7Ei0+NpnN3Sx6tB+R8wlSZIkqckknb7+EONbhU8vV27rmnLg9JvmJmr3rpvncuu7BqtcjSRJkiSpniQN5R+oahUN7NnBViYfJa+U49cD3sOQJEmSpGaTdE35VdUuRJIkSZKkZtMyfRNJkiRJklQNhvIqO3DuGNMvxy9xUPdYLcqRJEmSJNURQ3mV3Xhass3bbljjJm+SJEmS1GwM5VXW1QZfOG6IeLR84oh5fO0Lxw15HJokSZIkNSFDeQ2s6S3w0Fn9FVPZ48dB3WM8dFY/a3oLKVcoSZIkSUpD0iPRtI+62uB7746nqPf19dHb25tyRZIkSZKktDlSLkmSJElSSgzlkiRJkiSlxOnrNTKwAz54exdPbs0Di2EDHDm/wFWrh5jjT0GSJEmSmpIj5TVwz+Y8S6/rKQfy3K7HE1vzvPmbPdyz2VQuSZIkSc3IUF5lOwpw/r1d7A7jleJr59/bxQ43YJckSZKkpmMor7Jz1nclaveBW5O1kyRJkiQ1DkN5lT2xa8r6VHJs3OIUdkmSJElqNoZySZIkSZJSYiiXJEmSJCklhnJJkiRJklJiKJckSZIkKSWGckmSJEmSUmIor7oiUJqmTancTpIkSZLUTFI9hyuEcAJwIbAYOAD4QBRFV1Y8nwM+B3wYmA88DHw0iqKNta9279z17gFWfqsnUTtJkiRJUnNJe6S8G/gp8J+A4Ume/1vgb4CPA0uAF4E7QwjzalbhPson/A4nbSdJkiRJahypjpRHUXQrcCtACOHKyufKo+TnA1+MoujG8rVziIP5mcA/17TYvfSW67sTt/vxuY6WS5IkSVIzqefx2dcDC4E7xi9EUTQM3AcsTauomWsBctO0yVHfPwpJkiRJUjWkOlI+jYXlP38z4fpvgANf6ZP6+vqqVtDeWZy4Zf3VLtkvlR32VWWFfVVZYD9VVmShr/b29k75fD2H8r0y3V+45jYkb1p3tavp9fX12S+VCfZVZYV9VVlgP1VWNEpfrec50y+U/3zthOuvrXhOkiRJkqTMqudQ/jRx+F41fiGEMAc4HngwraIkSZIkSZotaZ9T3g0cXv6wBTg4hHA0sCWKomdCCGuBT4cQngR+AXwGGACuSaVgSZIkSZJmUdpryo8B7q34+PPlx1XAucCXgU7gMmA+8DDw1iiKtte2TEmSJEmSZl/a55R/nynOC4uiqARcVH5kUkdLkZHidMeilehoKdaqJEmSJElSnajnNeUN4c4wMKvtJEmSJEmNw1BeZa+aAx9cNASUyo9K8bUPLhriVXNqX5skSZIkKV2G8ho4f0mB+97bX56iXtr16Ggpct97+zl/SSHlCiVJkiRJaUh7o7em8ao58Oj74ynqjXLIvSRJkiRp3zhSLkmSJElSSgzlkiRJkiSlxOnrNfL0VlhzczfxfZDFsAGgyF3vHuA1c9OtTZIkSZKUDkfKa+C/3Jtnzc09xN/uXMWjhZXf6uG/3Ou9EUmSJElqRobyKntxEG7d3MXuIF4pvnbr5i5eHKx9bZIkSZKkdBnKq2zlt7pntZ0kSZIkqXEYyqtufMr6VHL4o5AkSZKk5mMSlCRJkiQpJYZySZIkSZJSYiivuiJQmqZNqdxOkiRJktRMDOVVtvbEgVltJ0mSJElqHB6QXWXn/yDZrurn/6CbH7/eYF4P/mM7vP3mbgYLu+9Zzc0Xuf3dA/R0pFiYJEmSpIbjSHnVuft6lnz98Twn3dhTDuS5XY/BQgvLru3h6497H0uSJEnS7DEJSmX9I/C1x7vYHcYrxde+9ngX/SO1r02SJElSYzKUS2Vv+1aypQYnJ2wnSZIkSdMxlFedu69nxe4p61PJMVDwn40kSZKk2WG6qLJrTkm2eVvSdpIkSZKkxmEor7I/fA0snDNEPBo+ccQ8vrZwzhB/+Jra1yZJkiRJSpehvAZOOWzfnldtzM0nW2rQnXepgSRJkqTZYSivst/tgH/dOPWO3v+6sYvf7ah9bXq529+dbAnBbQnbSZIkSdJ0DOVVtipKtlN30naqnp4O+NjRUy81+NjRQ/R01L42SZIkSY3JUF5lI8VkO3rH7ZS2Dx9d4IEz+iumsseP7nyRB87o58NHF1KuUJIkSVIjyaddgFRvejrgh+9r3inq24bgPevn8txgK7AYNsCBc8e48bRButrSrk6SJElqLA7PStrl5r48x0c95UCe2/V4drCVY6/u4eY+7+NJkiRJs8lQXmUtJNvRO24npWdoJ3x2w9SbEn52QxdDO2tfmyRJktSoDOVVVpw04EyUK7eT0nP6TXMTtXvXzcnaSZIkSZqeoVwSAM/umrI+lRy/HmitRTmSJElSUzCUV13SEXBHyiVJkiSp2RjKJUmSJElKiVspSwLiY8+mn8Je4qDusVqVJE3J4/skSVIjcKS8yvIJd1/Pu/u6UnbjaYOJ2t2wJlk7qZo8vk+SJDUKQ3mV3fWegVltJ1VLVxt84bgh4ptIE28kxde+cNyQI5BKncf3SZKkRmIor7IFnXDGEVMHnTOOGGJBZ+1rkyZa01vgobP6OXDuGLv7bDxl/aGz+lnTW0i5Qsnj+yRJUmNxfl8NPPb8vj0v1VJXG3zv3fEU9b6+Pnp7e1OuSHo5j++TJEmNxFBeZZu2QF//+DTLieJrff1dbNrSz+ELalqaXsHwKFz4/U5++HwbxRK05GDpATu5ZMUw7b7HV8ZN179HC7D2Rx2sf6qdQhHyLXDqYaN84pgR8s6tkiRJmnW+xaqyd36ne1bbqboee6GVY6/p4f7n2iiUchTJUSjluO/ZNpas6+GxF0zlyq7p+vfNfXmOv24e33yig60jLWzf2cLWkRbW/ayD466Zx6atvmRIkiTNNkfKq66FJNMsvT+SvtEx+NBtcym9wqyGEvHzj57d74j5NJ75Hbz9pm6KFf26hSJ3hQH270qxsCaWpH/v3jxtz+eHCznOWj+XDWduT33E3OP7JElSIzEJSmUX3NOZ4PA6uPBed+Wbyufuz3PqTT3lQL77qKoiLayIevjc/d4LTEOS/j2d4UKOS3/UMSv17AuP75MkSY3EUC6VPfhcG0lmNTzwrGeCvZKXhuDbv5z6qKpv/7KLl4ZqX1uzS9q/p3v+5l+2z1JFe8/j+yRJUiNxyEoqKyYcRkzarpEN7IAP3t7Fk1t3/wo5cn6BJxOuOV4ZdfP4uQPVKk+TmK1+W6iTGeFregusOrSf02+aW57KHjuoe4wb1gwayCVJUmYYyqWyllyy4NIy3WBig7tnc57z7x1fGL77m/HEroA+/Whr0Uk6NZe0f08nX0f7KXh8nyRJagS+M5bKlh6wkz2nwk5UYtmBO2tRTl3aUaAcyF95errqU9L+Pd3zaw4bnaWKJEmSBIbyqjtyfoEkb3QXLSjUohxN4ZIVw4lW3F68fLgW5dSlc9a7dXpWJenf0+nMlzhv8cis1CNJkqSYobzKrlqdbEerb5zizldpa2+FK04eLB8OtefmUTlKXHHyYFMfhxZPUd/XaFeiheJslKMZSNK/v3DcEJ354qTPd+aLXL16MPXj0JRNw6Pw0Ts6edNVPRx9ZQ9vuqqHj93ZyWid7FEgSVKafHtVZXPysHb51LsEr10+xBxX99eFYxaO8ejZ/Zxw4E7yuRItlMjnSrzloJ08enY/xyz0HeRsuCu4yVsapuvfa3oLbDhzO+//gxHmzykyr60YBDNuAAAgAElEQVTI/DlFzl00woYzt3P4fG+maOYee6GVY6/p4f7n2iiUchTJUSjluO/ZNpas6+GxF5r4TqckSbjRW02sOKTAd07r5+03dfPy+yBF7njXAAu706pMk2lvha+tGgaad5r6vhu/AZXb49o7Dhtif2fBp2a6/p1vgQvfPMKFb3aauvbd6Bh86La5lCadYRPP2/jQbXN59Oz+pp6FJElqbobyGlj7aJ5/3bjnbtXQwltv6OGDi4Y4f4lrylX/jpxfSDCFvcTr5xTYvKPlZbust1DkrjAwq4F8ywC847vdbB3Z/f+Z31Fk/ekDdKd/nLbU9C64pzPR9oIX3tvJpSu9ESpJak6G8ir73Q7KgXzyUQKInz/3qH5eNaempUkzdtXqId78zZ5p213/ruovybh6Y54vPbrnza6tIy0svaaHTy4Z4qxF3uyS0vTgc20kOSbxgWfbcHaSJKlZ1XUoDyFcBHxuwuXfRFG0MIVy9sqqKNnc9FVRN4++33W2qm/jeyRMdk75+PT0WuyRMDBKOZC/8s2uLz3axZre/roYMXdEX82qON0w+QzbSaqe4VG48Pud/PD5NoqlN9HyYI6lB+zkkhXDLi+RqiwLG739HHhdxeOodMuZmZFiC0lGCeJ2Uv1bcUiBR97XX3HcX/xYtCC+vuKQ6o9Or74x2c2uUxO2q6arN+Z5yw095UCe2/UYH9G/emNd3xuV9klLwsMakraTVB17bsjY4oaMUg1l4d1gIYqiF9IuQtJuc/Jw/Zr0jvHbHXCnkmPLSLo3u7I2oi/NtqUH7OS+Z6ebwl5i2YE7a1WSpAnckFFKXxaGZ38/hPBcCOHpEMJ1IYTfT7sgSUoiSyP6UjVcsmI4we0zuHi568mltMxkQ0ZJ1VHvI+UPA+cCTwKvAT4DPBhCWBRF0W8n+4S+vr7aVZdAG/8nO5l+t+p2CnVXuwT1928qtjhxyzTr3zqymKQj+vX5fc4Wv4f16e8XzeW/bnxj+U3/y/ehyAF/v+hJNj81mEptabGvqp5sePZNJHmtuv/Xefuu6lIW+mVvb++Uz9d1KI+i6HuVH4cQHgKeAs4BLpnsc6b7C9fa3f/HECdcN/1u1Xe9d4hXzamv2qW+vr66+zcFwIbkTVOtPyt1NoC67auiF/izN/VzwT2dPPhcG8VSvIZ82YE7uXj5MO2tB6RdYk3ZV1VvShuSbepQImffVd1plN+pdR3KJ4qiaCCEsJH4NT4TXjUHPrho6BXOKY/HDT64aMjj0CRJDau9Fb62ahiPPZPqT0su2QkIbsgoVU8W1pTvEkKYA7wReD7tWmbij16zb89Lyqb5HUVIsFJvQUexFuVIkrSHpQfsJMlrlRsyStVT1yPlIYSLge8CzxCvKf8sMBe4Ks26ZmJHgfKZzq+8+/L593bxyPv6q362s9Qojpxf4Imt0+/VsGhB9Y9nm8r60wdYes30y1duOX2gBtVIkrSnS1YMs2Rd25Sx3A0Zpeqq95Hyg4Bric8q/zdgBDg2iqLNqVY1A2d/t2v6RsA5tyRrJwmuWp3sOLZvnJLesW0A3e3wySVD7D7PvVJ87ZNLhjwOTZKUmvZWuOLkwfLhZ3u+VuUoccXJgx6HJlVRXY/NRlH03rRr2Fc/3zbdaB5Ajid+V9c/CqmuzMnD2uVD5VkoMNleDWuXD9XF7JOzFhVY09vP6hu7y+erxxZ0FLnl9AEDuSQpdccsHOPRsys3ZCzRkstVbMiYdoUvN1aEuzbn+c6mdnaM5ZjTWmJN7ygrDym49l2ZVAdvWSVp5lYcUuCR9/Vzzvqu8lT22KIFBb5xSn0E8nHd7fCDM5yiLkmqX5UbMtbzjta/Hc5x3t1d/HxLK6PF3Qn84efzXLVgjEtPGuLVnQl2rpPqSB29bZVUDzZtgXd+p5t4dcvi8rFeRe541wALu9OtbaI5ebh+TbpT1CVJmoqjurOnWILz7u7iJy/tGWFGizl+8lKe8+7uYt3qQb+3yhRDeZW1AEn2Va73xf1qDp+4K8/dv55sSngLb72hh5MOGuIrK9PdPE2SpKxwVHd23bU5z8+3TD2X/udbWrlnc56Vh/p+RdlhFqyyHMmORGpJFN2l6nlhgHIgz7HnPgjxtbt/3cULzsKWJGlalaO6lYEcXj6qm+SMcMVu7mvf43s50Wgxx7c3uWGLssWR8iobo4UkG70VvD+ilL31hmRz0996Qzc/PtdkLildowVY+6MO1j/VTqEI+RY49bBRPnHMCHlfUlUHHNWdfTvGks1J31Gon7nrLl9QEoZySWXJbiA5wUZS2jZtbeGs9XMZLrx8Zs+6n3Vwwy/auXr1IIfPdwaa0jWTUV1DeTJzWpNNK5iTr4/pBy5fUFK+u5YkSZlRKFIO5JPdSMwxXIgDe8FMrpRlbVR3tABffriDE6+dx9kPHc2J187jnx7pqKt/S2t6R2lvmTrEtreUeMfhozWq6JW5fEEzYSivsrn5ZGvKu/N19BtPkqQ69ZXHOsoj5K9suJDj0h911KgiaXJZGtXdtLWF46+bxzef6GDrSAtDY3m2jrSw7mcdHHfNPDZtrY/IsPKQAkcsGJuyzRELxlhxSPozD2ayfEGqj39hDez2dydbe3tbwnZS9SS7gZTsPAFJlbYMwInXdvNHV/bsepx4bTcD6Q/mZM4tv2wnyVKbm3/pRk9KV1ZGdbM0+6QlB5eeNMRR+xf2+N62t5Q4av8Cl540VBdrtd2UTjNhKK+yng445ZAh4jAz8RdzfO2UQ4bo8Ya+UnbHu5LdGEraTlLs6o153nJDD1tHxt/wxo+tIy0svaaHqzc6SjITSYNBYerBNKnqsjKqm7XZJ6/uLLFu9SD/eMIQxx+0kyULCxx/0E6+eMIQ61YP1s0a7awtX1C6DOVVtqMAt26e+pipWzd3sSP9WTZqcgu74agFU99AOmrBEAuTbdIuCRgYhS89OvVrwJce7XLEfAaS7qyen3rWqFR1WRnVzeLsk5YcrDq0wGUrh7ji5EEuWznEykPrazfzLC1fUPoM5VV2zvquRO0+cGuydlK17CjAT7ZMHR5+ssUbSNJMrL4x2V2sUxO2U3zsWZKlNmsO806H0peFUd2dCWef7HT2yYxkZfmC6oNz5qrsia15ktx93LjFH4XSNZMbSNe+fajK1UiNYfeU9ank2DLiPfKkPnHMCDf8on3K6bad+RLnLR6pYVXSKxsf1V1Vp8ee7Uw4zbowzfpovdzKQwpctWCMn7z0yu/x62H5guqD7wIkAd5AkpQN+Ra4evUgnZOeblKiM1/k6tWDiae5S0o2Wl8qpT+qnyVZWb6g+uC7a0mSlCmHzy+y4cztrH2sg+8+1U5hLF5DvuawUc5bPGIgV10ZLcDaH3Ww/ql2CsX4xtKph43yiWPqo6/mc5BkXkne8Dhjr+4sceWfDbL2Rx3cUvG76s9/f5Tz6+Tnr/pgKJckqUrmdxQTTGEvsaCjDs4ayph8C1z45hEufHP9T1OvDGUjhaPpeKylrkKZqmfT1pbycWMv369l3c86uOEX7Vy9epDD56f77789D4MJ1ou3t1W/lkbz2+Ec593dxc+3tO4+Hm0nXPdkB//rxTyXnjRUF/sKKH2+FFRZ0m+wPwil7cj5BZJsnrRogWufpKTWn57sCMFbErZT9mza2sLx183jm090sHWkhaGxPFtHWlj3sw6Ou2Yem7b6DqBRZeX8bzdPrI5iCc67u4ufvJTf47zy0WKOn7yU57y7uyiayYVZsOqSrhNxPYnSdtXqZJu3feMUN3mTkupuh08umfqowU8uGaK7fk4a0izKSihTdWTl/O9PHDNC5zTHcrl54szdtTnPz7dMfTbjz7e0cs9mJy7LUF51rbnJNqKZqFRuJ6VnTh7WLp86PKxdPsQcXzukGTlrUYEHz+xnfsf460H8WNBR5MEz+zlrkbNPGlVWQpmqIyvnf7t5YnXc3Ne+xwj5RKPFHN/eVD93ZUcL8OWHOzjx2nkcd/U8Trx2Hv/0SIc3DmvAt9dVNlJMdhxO3E5K14pDCjzyvn7OWd9V3o09tmhBgW+cYiCX9lZ3O/zgDKeoN5uZhLILljgK2WiSBplCHZz/PXHzxJGdRTraWtw8cR/sSHjU3I5pbtzVShb2P2hkvsWW9DJz8nD9mniKel9fH729vSlXJEnZlKVQptmXNMjmp57hXDOVmyf6+r/v5rQmWyw+Z5qlA7Xw8qU2E+UYLuQ4a/1cNpy53Rs0VeK3VZIkqQqyFso0u9xArbmt6R3d43zyidpbSrzj8PR//i61SZ+hvMoOnDtGkl/IB3V7m1z7bqwItz+d56N3dvGh2+by0Tu7uONXeXf2lKQUGMqamxuoNbeVhxQ4YsHU7++PWDDGikPS31ckK/sfNDKnr1fZFScPcvKNPdO2+x9vG6xBNWpkk56FCTz8fJ6rFox5FqakhjJWjHc3/s6mdnaM5ZjTWmJN7ygrDynUzYkmnzhmhBt+0T7lCJShbO8Mj8KF3+/kh8+3USzFp9gsPWAnl6wYpr1OZh6Mb6A22TrdeAO1khuoNbCWHFx60tCk783aW0ocUX5vVg+/r1xqkz5DeZWt+XZ34naPvt9NgLR3Ks/CnKjyLMx1qwfr4pe/qmNgB3zw9i6erNik78j5Ba5a7SZ9aixZuQlpKKuOx15o5UO3zS3PQYi/p8US3PdsG0vWtXHFyYMcs7A+0sPEDdQKY/FyBTdQaw6v7iyxbvUgd2/Oc9OmdnYUcszJx1PWV9TRDUSX2qTPt2lV5u7rqoWZnIW58tD0p0lp9t2zOc/593aVP9r9O+eJrXne/M0e1i4fqospctK+ytpNSHe1nl2jY5QD+WQ/3Bwl4ucfPbu/rkbMxzdQU/NpycGqQwusquP3X6ceNsq6n3UwdWZxqU01GcqlBjCTszAN5Y1nR4FyIJ/8TSrEzz/yvn5HzJV5WbwJ6a7Ws+eCezoTrNKHC+/t5NKVw7UoqWFULgnZuv0NzP9V56wsCRktwNofdbD+qXYKxfjfw6mHjfKJY5rjppRLbZSEb8+kBpC1szA1u85Z3zV9I+ADt3Zx7duHqlyNVF3ehGxuDz7XRpIZiA882wYYypP67XCOj93Zxc+2tJZnIbRBPzzwbJ4/WDDG11bt3ZKQZj/72qU2SspvrdQAsnQWpmbfE1vzJHmTunGL92GVfd6EbG5JTxPx1JHkiiX48O1dbNyS32NZQKn82vHh27tm/D19+dnXE/895hguxIE96SZjWVO51GbijcTKpTb10lfHl9q8/w9GmD+nyLy2IvPnFDl30Qgbztze0DdP6oHv0KQGsKZ3lIef3/OXfqV6OQtTkvaFNyGbW0suWeCul2nBWXDb03n6fjf1kpC+37Vyx9N5Tv795LNPZnL29QVLGm9adNaX2qi2HCmvsqR7jNTJXiTKqCydhSlJ+2JN7yjtLVOnsnq7CTlagC8/3MGJ187j7IeO5sRr5/FPj3Q07AhhNS09YCdJzn5fduDOWpTTEC790RySzLZa++9zZvR1m/3s65kstZEM5VX2hvkFkrx4vHGBYUl7b/wszKP2L+zxZrW9pcRR+xfq5ixMzb4jE/6eWeTvGTWArN2E3LS1heOvm8c3n+hg60gLQ2N5to60sO5nHRx3zTw2bfWt2ExcsmI4QcyDi5e7njyp3wwl64MvDs6srzb72dcutdFM+EpQZQs6Icldwld11qAYNbTxszD/8YQhjj9oJ0sWFjj+oJ188YQh1q0erIuNRFQdV61OtnnbN06Zvt1YEW5/Os9H7+ziQ7fN5aN3dnHHr/J1s+ZNytJNyGZfU1sN7a1wxcmD5cPPJv5iKpGjxBUnD9bNcWhZUK11+s1+9rVLbTQTrimvsummrexql/BumjSVLJyFqdk3Jw9rlw9Nek75+JvWtcuHpj0OLSu7xErjNyHv3pznpk3t7CjkmJOPp6yvqKNjhpp9TW21HLNwjEfP7ueCezp58Lk2iqX49W/ZgTu5ePmwgXyGckw/12pXwxlo9rOvs7jfT7MfX5cmQ3mVeZdM1ZSFsy9VGysOKfDI+/o5Z31XeTf22KIFBb5xyvSBvHKX2Ikqd4ldt3rQvqW6kIWbkDNZU2son5n2VvjaqmE89mzf7d9Z5MXh6e9k7D9nZlM6mv3s65WHFLhqwdikr6vj6m2pTTMfX5c2Q3mVZfEumbLBUU1NNCcP16/Zu3PIs7hLrFTvmn1NrbJhJOGa5pnO6mz2s6/Hl9pM9l6tvaXEEeX3avVwo/vlS20myjFcyHHW+rlsOHN7Xfy8Kgeltm5/A/N/1Zn5Qak6+LY2tqxtSKNsyNrZl6p/Wdwl1h2tVe+afU2tsmH7zmQppn905mmn2c++fnVniSveNsifvG4n+VyJFkrkcyWOPWAn3/iz+tnvZyZLbdL22+Ec7791Ln93fxf3P9vGT/t7uP/ZNj59Xxdnr5/Lb4ezmcoN5VWWpQ1plB0zGdWUksjaLrHuaK0sOPWwUZKcjNCoa2qVDUlj4d7Gx/Gzr3/w3u1sOGs7P3jvdi5Y0hxrlDdtbeHE6+dx/7NtFEo5iuQolHLc9+s2jr+2fl6rsnJ8XSMPStVHT2hwE3fF/sOefnfF1j7J4qim6luW9r9wR2tlxSeOGaFzmn8zjbymVtnQmvBea9J2imXptSorS20aeVDKUF4j4xvSXLZyiM8f9QsuWznEykOzu+5B6craqKbq35re0T1m80xUL/tfZGmanZrb+JraznyRyY7v6swXG3pNrbJh6QE7STKjY9mBO2tRTsPI0mtVVpbaNPKglC8DUgZlaVRT2ZCl/S+yMs1Ogj3X1Ha1FppqTa3q3yUrhhP8RoWLl7vT/Uxk6bUqK0ttGnlQylAuZVCWRjWVDVna/yIr0+ykcZVratcd+3hTralV/WtvhStOHiRHiclmdOQoccXJg57/PkNZeq3KylKbRh6Uyt6Ee0mZO/tS2TC+/8Xdm/PctKmdHYUcc/LxzZ0VdXTMSFam2UlSVhyzcIxHz+7ngns6efC5NoqlEi25HMsO3MnFy4frLpBXHom1YyzHnNZS3R2JlaXXqqwcX9fIR00byqUMytLZl8qW8f0vVtXxWeSnHjbKup91MPW0wPSn2UlSlrS3wtdWDQPD9PX10dvbm3ZJk/rtcG7S9z8PP5/nqvL7n3rYRDlrr1XjS23WPtbBd59qpzAW3zBYc9go5y2uj5k9jTwolSuV0u+0+2rbtm2Z+kvU8y86ZUuxRFVHNe2rqkeFIhx3zbzyjraT68zHby7q4U2EVMnfq8qCeu2nxRKcvX7ulKHsqP0LrFs9mPrAhK9V1fFKN2UqB6Xq4abMVPbbb789eqcj5VKGZWFUU5ptWZlmJ0maXTM5Emtlyu+NfK2qjolL7bb2DzO/p7PultrNlKFckpQ5E6fZjews0tHWUlfT7CQpSyrXaW/d/gbm/6qz7tZpz+RIrLRDOWRjSngWVQ5K1eusjpkylEuSMml8R+sL3zzSMC/KkpSGPacEt0F//a3TzuKRWJWvVdIr8f6MJEmS1KSKJTjv7i5+8tKeu1qPFnP85KU8593dRTH9TN7QR2KpuRnKJUmSpCY1k3XaaVvTO0p7y9SBO6tHYqm5GcolSZKkJjWTddppW3lIgSMWjE3ZJqtHYqm5GcolSZKkJpWlddotObj0pCGO2r+wx4h5e0uJo/YvcOlJQ3WzMZ2UVPrzUCRJkiSlImvrtCceibWjkGNOvpT5I7HU3DIRykMIHwH+M/A6YCNwfhRF96dblSRJkpRta3pHefj5PTd5q1Rv67Qrj8SSGkHdT18PIbwH+CrwD8AfAw8C3wshHJxqYZIkSVLGuU5bSl/dh3LgAuDKKIr+JYqiJ6Io+jjwPPDXKdclSZIkZZrrtKX01fX09RBCO7AYuHjCU3cAS2tfkSRJktRYJq7T3to/zPyeTtdpSzVS16Ec2B9oBX4z4fpvgJWTfUJfX1+1a5oVWalTsq8qK+yrygr7qurVocD5h1Rc2Am/3JRSMVJCWfid2tvbO+Xz9R7KZ2y6v3A96Ovry0Sdkn1VWWFfVVbYV5UF9lNlRaP01XpfU/4SMAa8dsL11wIv1L4cSZIkSZJmT12H8iiKRoEfAasmPLWKeBd2SZIkSZIyKwvT1y8B1oUQHgE2AH8FHAD8v6lWJUmSJEnSPqrrkXKAKIquB84HPgM8DiwDTomiaHOqhUmSJEmStI+yMFJOFEWXA5enXYckSZIkSbOp7kfKJUmSJElqVIZySZIkSZJSYiiXJEmSJCkluVKplHYN+2zbtm3Z/0tIkiRJkhrafvvtl5t4zZFySZIkSZJSYiiXJEmSJCklDTF9XZIkSZKkLHKkXJIkSZKklBjKJUmSJElKST7tAppNCOEjwH8GXgdsBM6Pouj+dKtSswohfAp4J3AEMAI8BHwqiqKfVrTJAZ8DPgzMBx4GPhpF0cbaVyzFyn33H4DLoij6WPmafVV1IYTwOuCLwCnAPOAp4K+jKPpB+Xn7qlIXQmgFLgLeR/y+9HngauCiKIoK5Tb2VdVcCOEE4EJgMXAA8IEoiq6seH7afhlCmA9cCry9fOk7wMejKPpdLf4OM+VIeQ2FEN4DfJX4jeQfAw8C3wshHJxqYWpmbwEuB5YCK4ACcFcIYUFFm78F/gb4OLAEeBG4M4Qwr7alSrEQwrHEL8Q/nvCUfVWpCyG8CtgA5IDVwJHEffLFimb2VdWDTwIfBc4D3gj8p/LHn6poY19VGrqBnxL3yeFJnk/SL68B3gScXH68CVhXxZr3iSPltXUBcGUURf9S/vjjIYSTgb/m5b8ApZqIouhtlR+HEM4GtgHHAd8t34k8H/hiFEU3ltucQ/zL70zgn2tbsZpdCGE/4pGcDxLfJR+/bl9Vvfhb4Pkoit5fce3p8f+wr6qOLAW+G0XRd8sf/yqE8B3gT8C+qvREUXQrcCtACOHKyueS9MsQwpHEQXxZFEU/LLf5S+D+EMIRURT9vFZ/l6QcKa+REEI78RSMOyY8dQfxL0WpHswj/r2wtfzx64GFVPTbKIqGgfuw3yodXwduiKLo3gnX7auqF6cBD4cQrg8hvBhCeDyE8LHyG0mwr6p+PAAsDyG8ESCE8AfEs+ZuLT9vX1U9StIv/xQYIJ6VPG4DMEid9l1Dee3sD7QCv5lw/TfEHUuqB18FHgd+WP54vG/ab5W6EMJfAIcDn5nkafuq6sXvAx8hXkf+NuLfq18knhYM9lXVjy8RT+f9WQhhJ/FeR1dFUXR5+Xn7qupRkn65EPiPKIp2nf1d/u8XqdO+6/R1SQCEEC4BlhFP9RlLux6pUgjhCOL9OJZFUbQz7XqkKbQAj0VRNL4s7X+FEHqJQ/nX0itL2sN7gPcTT/ndCBwNfDWE8HQURVekWpnUZAzltfMSMAa8dsL11wIv1L4cabcQwleA9wLLoyh6quKp8b75WuCZiuv2W9XanxLPONoYQhi/1gqcEEL4K2BR+Zp9VWl7HvjZhGtPEG9YBP5eVf34J+DiKIquK3/8kxDCIcT7HF2BfVX1KUm/fAH4vRBCbny0vLyE6DXUad91+nqNRFE0CvwIWDXhqVW8fL2DVFMhhK8CZwAroih6csLTTxP/8lpV0X4OcDz2W9XWTcBRxCM544/HgOvK//0L7KuqDxuIj5ms9AZgc/m//b2qetFFPGBUaYzd+cC+qnqUpF/+kHgH9z+t+Lw/BeZSp303VyqVpm+lWVE+Em0d8VqzDcBfAR8CFkVRtHmqz5WqIYRwGXA28cZElSM7A1EUDZTbfBL4NPAB4uDzGeAE4IgoirbXtmJptxDC94GfVpxTbl9V6kIIS4jf9F0EXE98BOr/AD4dRdFl5Tb2VaWuvKv1SuAviaev/zHxZpr/M4qivym3sa+q5kII3cR7yED8+/SLxOeMb4mi6Jkk/TKE8D3gIOIjVCHu27+KoujPa/YXmQGnr9dQFEXXhxBeTdxxXkd8/t4pBnKl6CPlP++ecP3zxG8oAb4MdAKXAfOBh4G3+mKsOmRfVeqiKHo0hHAa8R4InyWeXvlZ4PKKZvZV1YOPA18g7puvIV568S/A31e0sa8qDccAlaesfL78uAo4l2T98kzg/wZuL3/8HeBjVa16HzhSLkmSJElSSlxTLkmSJElSSgzlkiRJkiSlxFAuSZIkSVJKDOWSJEmSJKXEUC5JkiRJUkoM5ZIkSZIkpcRQLkmSJElSSvJpFyBJkmojhHAo8HSCpp+PouiiEMKvgEOAf46i6K8mfK1j/v/27pbFqigKA/ArCBNMRosfoGgx+FMsC4OCowiiVRC0GcYkUwYExY9gmQVmg8moBpvJpBYxKwyCY7g3yHBR0Zk54H6edDl778NbXxb73CSvkyx39+NtjgoAw1DKAWAcn5Oc+8X6rSRHkrzc8ny5qla6+/2OJQOAQSnlADCI7v6S5Mmitaq6nFkhX+3uZz8tvU1yNMmNJJcXnQUA/p475QAwuKo6mWQ1yask17csf0jyKMmFqjq429kA4H+nlAPAwKpqX5JOspHkTHd/W7BtJclmkpu7mQ0ARqCUA8DY1pKcSHKxuxd+BG5+l/xhZnfLD+1mOAD43ynlADCoqjqb5HySte5++pvtpuUAsAOUcgAYUFUdS3I3yZsk1363v7s/JHmQ5LxpOQBsH6UcAAZTVUtJ1jObfFd3b/zhUdNyANhmSjkAjOdOklNJLnX3uz891N0fM5+WJzm8I8kAYDBKOQAMpKpOJ7ma5F53r//FK1aSfM/sf8sBgH+0d+oAAMDuqKoDmU26vyZ5Pf/Q2yKfuvv5ooXu/lhVD5Jc2aGYADAUpRwAxnE8yf757/u/2PciycJSPnc7ycUkS9uUCwCGtWdzc3PqDAAAADAkd8oBAABgIko5AAAATEQpBwAAgIko5QAAADARpRwAAAAmopQDAADARJRyAAAAmIhSDgAAABNRygEAAGAiSjkAAABM5MeMKKkAAAAFSURBVAd0xOXLo+iQHwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "housing.plot(x='ZN', y='INDUS', kind='scatter', color='dodgerblue', figsize=(15,7), s=100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to use a list comprehension to change the size of the scatter plot dots based on `DIS`" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This list comprehension sets the point sizes ('s') to be the squares of the values in housing['DIS']\n", + "housing.plot(x='ZN', y='INDUS', kind='scatter', \n", + " color='dodgerblue', figsize=(15,7), s=[x**2 for x in housing['DIS']]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Seaborn `pairplot`\n", + "\n", + "---\n", + "\n", + "- **Objective:** Know when to use Seaborn or advanced Matplotlib.\n", + "\n", + "With the `DataFrame` object `housing`, we will render a pairplot using the Seaborn library." + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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MEDV-0.3620770.360445-0.4837250.175260-0.4273210.695360-0.3769550.2643250.113519-0.468536-0.5077870.333461-0.7376631.000000
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" + ], + "text/plain": [ + " CRIM ZN INDUS CHAS NOX RM AGE \\\n", + "CRIM 1.000000 -0.300774 0.590822 0.013922 0.634679 -0.190197 0.482013 \n", + "ZN -0.300774 1.000000 -0.533828 -0.042697 -0.516604 0.311991 -0.569537 \n", + "INDUS 0.590822 -0.533828 1.000000 0.062938 0.763651 -0.391676 0.644779 \n", + "CHAS 0.013922 -0.042697 0.062938 1.000000 0.091203 0.091251 0.086518 \n", + "NOX 0.634679 -0.516604 0.763651 0.091203 1.000000 -0.302188 0.731470 \n", + "RM -0.190197 0.311991 -0.391676 0.091251 -0.302188 1.000000 -0.240265 \n", + "AGE 0.482013 -0.569537 0.644779 0.086518 0.731470 -0.240265 1.000000 \n", + "DIS -0.495148 0.566660 -0.678498 -0.090950 -0.748872 0.225052 -0.713313 \n", + "RAD -0.088451 -0.119290 -0.087615 0.079105 0.009217 0.088753 0.019658 \n", + "TAX 0.793392 -0.314563 0.720760 -0.035587 0.668023 -0.292048 0.506456 \n", + "PTRATIO 0.362615 -0.391679 0.383248 -0.121515 0.188933 -0.355501 0.261515 \n", + "B -0.377013 0.175520 -0.356977 0.048788 -0.380051 0.128069 -0.273534 \n", + "LSTAT 0.481907 -0.412995 0.603800 -0.053929 0.590879 -0.613808 0.602339 \n", + "MEDV -0.362077 0.360445 -0.483725 0.175260 -0.427321 0.695360 -0.376955 \n", + "\n", + " DIS RAD TAX PTRATIO B LSTAT MEDV \n", + "CRIM -0.495148 -0.088451 0.793392 0.362615 -0.377013 0.481907 -0.362077 \n", + "ZN 0.566660 -0.119290 -0.314563 -0.391679 0.175520 -0.412995 0.360445 \n", + "INDUS -0.678498 -0.087615 0.720760 0.383248 -0.356977 0.603800 -0.483725 \n", + "CHAS -0.090950 0.079105 -0.035587 -0.121515 0.048788 -0.053929 0.175260 \n", + "NOX -0.748872 0.009217 0.668023 0.188933 -0.380051 0.590879 -0.427321 \n", + "RM 0.225052 0.088753 -0.292048 -0.355501 0.128069 -0.613808 0.695360 \n", + "AGE -0.713313 0.019658 0.506456 0.261515 -0.273534 0.602339 -0.376955 \n", + "DIS 1.000000 0.003030 -0.541369 -0.269140 0.293621 -0.479158 0.264325 \n", + "RAD 0.003030 1.000000 -0.049221 -0.116969 0.040705 -0.069828 0.113519 \n", + "TAX -0.541369 -0.049221 1.000000 0.460853 -0.441808 0.543993 -0.468536 \n", + "PTRATIO -0.269140 -0.116969 0.460853 1.000000 -0.177383 0.374044 -0.507787 \n", + "B 0.293621 0.040705 -0.441808 -0.177383 1.000000 -0.366087 0.333461 \n", + "LSTAT -0.479158 -0.069828 0.543993 0.374044 -0.366087 1.000000 -0.737663 \n", + "MEDV 0.264325 0.113519 -0.468536 -0.507787 0.333461 -0.737663 1.000000 " + ] + }, + "execution_count": 242, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "housing.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a heatmap on the correlations between variables in the housing data:\n", + "sns.heatmap(housing.corr());" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge\n", + "### How much is the corrolation in the plots below? (guess and then check with the Corr matrix above)\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "housing.plot(x='NOX', y='INDUS', kind='scatter', \n", + " color='dodgerblue', figsize=(15,7), s=100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Now, create a scatter plot of two heatmap entries that appear to have negative correlation." + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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uIiivYZU4TgwAAAAAcBHhWI2rtHFiAAAAAICL6L6+CFTaOLH5SLvStkMR7ToRlutlX9OGtrQ2r06R6QcAAABQtQjKUfH6h21t2tWopGtJujjmrfdIRI++HtaOzgQZfwAAAABViRwjKprraTwgtzU5IM+ylHSzAbtLTA4AAACgCpEprwAZT9o94OiJ/rBGM5bqQ7662tPqaHVlT49D83is4fPvUfPJ6NuPlRqTtn4vqhdO101p++ZY0k1XjunetUmFQ4Vdz0LddzAyniGfW9K11HMooi1rqrc8HwAAAMDiRFBeZkNJS917GnTsbEhp72LweWDQ0faWjHrWjWhpNGjW+FyPVSedyz7W/U2e3vj5RGHE1CDX9aV9p+q0prdO990+ogcORwqynkL49vHwjPXOZGnn8TBBOQAAAICqQ/l6GXm+1L2nQYfPOFMCYElKe5YOn3HUvadBnkEMHPRYb/w8pGxwO1eAa8mXpc8/V5j1FIppWbqbKe46AAAAAKAYyJSX0e4BR8fOhnJec+xsSHsHHHUsdxf8WIVgup5CMe2s7hT/pc9AR3gAAAAAC0XoUEY7+8IzMtLTpT1Lj/WHC/JYZgqznkLZ0JaWFJSa99XVli7Fct7WP2zrlkea9ODRiIZTts6P2RpO2eo9EtHNDzWpf5gfLQAAAADBiBzKaDRjFkSPBjQ6y+exCsFkPYWyeXVKUSd3UB51fHXfULrz5HSEBwAAAFAoBOVlVB8yO5xdHxCU5vNYhWCynkJxbGlHZ0JRx9PMjLmvqONpR2eipOXi+XSEBwAAAIBcCMrLqKs9rbCdO8AN274+viK4NNvkscwUZj2FtKLZ0/6N53XX+1JqrvfUVOepud7Tp65Paf/G81rRXNqUdD4d4QEAAAAgFxq9lVFHq6vtLRkdPjP3x7CyJaO1rcFN1UweqxBM11Noji1tvTGlrTeWf+wZHeEBAAAAFAqZ8jKyLaln3YhWLXNnZLnDtq9Vy1z1rBuRbXCEO+ix3n1pRtks+FyZcF+WfG27vTDrqWWV3BEeAAAAQHUhU15mS6O+ejsT2jPg6PH+sEZdS/VOtkR8baubVwA8/bGGzyXVfEn07cdyPWnL3qieP1U3JTR3LOkjV43pG7cnFQ5JH73GLch6atWGtrR6j0SUu4S99B3hAQAAAFQfgvIKYFvS+uWu1hdg9vfkx+rr61N7e/vbfxcOSd9cn5SULNl68lXs2d8ZLzvT/Yn+sEYzlupDvrra0+rIY8Nh8+qUHn09nLPZW6k7wgMAAACoTgTlqBj9w/b4qDFLk7PQvUcievT1sHZ0JhbU1G0oaal7T4OOnQ1Nmel+YNDR9paMetaNaGk0uFneREf42daa7Qjvl7wjPAAAAIDqRNiAilDs2d+eL3XvadDhM86UgFyS0p6lw2ccde9pkGfYwL7SOsIDAAAAqE5kylER8pn9vWVN/mXhuwccHTubu/PasbMh7R1w1GFYtl9JHeEBAAAAVCeCclSEfGZ/zyco39kXnpEhny7tWXqsP2wclBfifDoAAACAxY2gHBWh2LO/RzNmUfJoQLZ+QqHOpwMAAABY3DhTjopQ7Nnf9SGzALneCb6u0OfTAQAAACxeBOWoCBva0pKCotj5z/7uak8rbOd+/LCdncceJJ/z6QAAAACQC0E5KsLm1SlFA7LUC5n93dHqamVL7tr3lS0ZrW0NPk+ez/l0AAAAAMiFVB4qQjFmf18YlT7zdINeG754m9eHPHm+NSWoDtu+Vo6fAzdp0Fbo8+kAAAAAFi+CclSMidnf2w5G9OSJsNxM9gx5V1ta3Tek8grI9w44+vxzDeN/uhgcj44ny9+31FVjXfYM+cdXpLU2j47phTyfDgAAAGBxIyhHRSnE7O9RV+MB+WxRdvZrR4ZCevmT51Q/j5+Aj61I64VTjvwcI9ysBZx/BwAAALB4cKYcNef3dzUEXyTp098xu246itIBAAAAFApBOWrO0WFHwaGzpR+fnV+hyBP94ZxZcknyZWnncRq9AQAAAMiNoBzIE43eAAAAABQKQTmQJxq9AQAAACgUgnLUnJWXupKCAmJf17cEzySfTVd7WmE79+OH7WxXdwAAAADIhe7rNSDtStsORbTrRFiul+1gvqEtrQ2Xlntlpdc/bOsnF0JG1/79r4/M6zk6Wl1tb8no8Jm5f3xWtmS0tnV+QT8AAACAxYOgvMr1D9vatKtRSdfS5OZmvUciitsf1MPvGNGKZq98Cywh11P2vcjkKgDJZri33T4yr3FokmRbUs+6EXXvadCxsyGlvYvve9j2tbIlo551I8Zzz1H5Mp60e8DRE/1hjWYs1Yd8dbWn1ZHHfHsAAABgNgTlVeztINSdLQi1lPJC2rSrUfs3npezCA4q3HcwMr45kdum96YWnMVeGvXV25nQngFHj/eHNepaqneyJetrCdRqylDSmnUD5sCgo+3jGzBLo/QPAAAAwPwQlFcxkyA06VrqORTRljWpEq2qfL59PCyTUWjfORnWv/vwwt8P25LWL3e1fjll6rXK86XuPQ2zHlVIe5YOn3HUvadBvZ0JNmIAAAAwLwTlVcw0CN15PGwclCfT0tbvRfXSYJ08Pxt43nTlmO5dm1TY7Kh22biGVfpuprjrQO3YPeDo2NncN/6xsyHtHXDUweYMAAAA5mERFDXXrkIHoQffDOnDD12i50/XyfUtebLk+pb2narTmt5LdPDNyo7KTUv0ncp+GaggO/vCU0rWZ5P2LD3WHy7RigAAAFBryJRXsUIGoemMdPdTjfJnzbxb8pX9+1fuPFexGfPVV4zp2YGg6gFfXW2MKsulFpuazTWhYPPqVM6fo9GM2QseNehlAAAAAMyGoLyKbWhLq/dIRIUIQrfsjRpM9pa2PhdVT0cyj1WWRjojg4Bcijq+um+o/fP181WLTc1yTSh49PWwdnQm5pxQUB8ye631TnW9JwAAAKgclK9Xsc2rU4oGBAOmQeiLp+tkcj79hVN15gssoS17owZX+bq+xV0UnejnY3JTs+kl25ObmnlVFH9OnVAw/f62lHSzAftcR0G62tMK27lfcNjOdt0HAAAA5oPwpIo5trSjM6Go40kz8ty+InZGOzoTRkGoaaBVqQGZ6abC//zXytxUqAT5NDWrFvlMKJhNR6urlS25mzKsbMkseMQeAAAAFi+C8iq3otnT/o3nddf7Umqu99RU56m53tOnrk/pH3/5h3OW5U5nela4Us8UV/umQiWoxaZm+UwomI1tST3rRvT+pa5C0za+QvL1/qWuetaNVOzPBQAAACpf9aS8FrlczbccW9p6Y0pbb5xapt7XZ/74N105pn2ngrLNvj5y1di81m9qvg25bMss4CZ4mlstNjUr2IQCS7IsTSlIsaYeUQcAAADmhUx5FRhKWrrrO4364vMNev5UnV5509Hzp+r0hX0NunNXo4aSC48M7l2bNMgnSt+4vXhN3vqHbd3ySJMePBrRcMrW+TFbwylbvUciuvmhJvUPz3273nTlmGaW8E9X/E2FalaLTc0WOqFg4pz9q2ccuf7UnxDXt/RqFZ6zBwAAQGUhKK9wpWq+FQ5JD9yRGB9+NvN8uiVfD9yRKNo4tIU25Lp3rdlmQTE3FapdLTY129CWlslmzVwTCmrxnD0AAAAqC0F5hStlULD6ioxeufOcbr1qTI7ly5Yvx/L10avH9Mqd57T6ios1vmlX+tqBiG57uEk372jSbQ836esvR4zLhadbaEMux5befWnuJ3/3pR6d13OoxaZmJhMKbEmvnnF0z7MNeuakM2WDqxbP2QMAAKCykN6pcPkEBR3LFx4shUPSN9cnJc2dUV7I3Oe55NOQa8uamSPedg84OnU+d8R96rytvQNOQd6nWjTR1Gy2OeVh29fK8Tnl1XQuf2JCwWz360QG3ZOlgz/L/lM4fR57LZ6zBwAAQGUhKK9w8w0K0q709yeu1v6DTXk1TAsytcx8OktJ19KmXY3av/F8Xs+z0IZcpd68qFVLo756OxPaM+Do8f6wRl1L9U62ZH1tq1tVAfmEiQkF2w5G9OSJsMZcKZmxlPFnvpjJR0J6OxM1ec4eAAAAlYWgvMLNJyi4mMm+RIXKZE/Ip8x8toz2XBbakIuMZuHYlrR+uav1NbR5MXlCwTMnHX1hX4MyOX60Jo6EfGxFWi+ccuTnqOKwcpxJBwAAAIJwwrbC5dt8a6EN04IsdO7zXBbakIuMJkzlU1XBFg4AAACKjaC8wuXbfGuhDdOCFGzu8zSf+5BZVv2zH5z9ulrsHI7iyKeq4on+cM4suZSdTZDvJhQAAAAwgaC8wk0031q1zJ0RdIZtX6uWuVOabxUrkz1hoWXmc/mbH5htEnzrh7NfV4udw1Ec+VRVcCwCAAAAxUZQXgUmmm995dYR3XL1mNZc4eqWq8f01VtH1NuZ0NLoxSCjWJnsCQstM5/LQjcT8t28MJHxpKffyI7KuvupxllHZqH65FNVwbEIAAAAFBuN3qqEafOtYmWyJ2xendKjr4dzlshHHV/dN5g3eZMKs5lQyM7hQ0lr1tFg00dmBUmmpa3fi+qlwTp5fvZzvOnKMd27NqnwPD8DLExHq6vtLRkdPjP3P38TVRWepO8POhrLcQa9jmMRAAAAWAAy5TWmWJnsCRNzn6OON8vz+Io6nnZ0JvIeu1aozYSJzYv7O0b0wB0J3d8xoo7l+QXkni9172nQ4TPOjIZgk0dmBWXMD74Z0ocfukTPn66T61vyZMn1Le07Vac1vZfo4JtE5eWQT1XF2mtcOQFZdcf29dFrOBYBAACA+SEorzGbV6cUDSilnU8me7KJuc93vS+l5npPTXWemus9fer6lPZvPD+vcWvF3kzIx+4BR8fO5g6YJ0ZmzSWdke5+qnG8SdjMLvi+LN39VKPS8zxGgIUxPRKy9yeO3IBz5W7G0vd+QtERAAAA5qesv0nGYrF3SvqqpF+X1CTphKT/Ix6P/1M511XNJjLZ2bFo0wNCX1HHn1cme7bnmZj7XAjFKoufj3xGZnXMcZxgy96owRaDtPW5qHo6kvNbKIxlvOxmyxP9YY1mLNWHfHW1p9XRmvtIyM6+sMb83PfCmJ/7XgAAAAByKVtQHovFLpO0X9ILkjol/aukayX9S7nWVCsmMtlfenZELwz/gtxMtuy7qy2t7htSCw7Ii6FQmwlpV9p2KKJdJ8JyvezjbmhLa/Nq89ddiI7bL56uk0njuhdO1UkiKC+mhfQHoPs6AAAAiq2cmfJ/K2kwHo/fNelrb5RrMbXGsaVPXXtKf9XeUO6lGJvYTNh2MKInT4Tz3kzoH7ZnDep7j0T06Oth7ehMGJXWF6LjtmmHdjq5F9fk/gDTTe4P0NuZmLXvAN3XAQAAUGzlDMp/U9JTsVjsv0q6XdJpSX8r6f54PM5vuIvUfMviXU/jAflskbulpGtp065G7d94PjC472pP68DgzCZvk4UDOm7bllnAnU8DOuQvn/4As5Wf/8aKtF445Yz3BpidVaJeBwAAAKhN5QzKr5X0WUn3KXuu/AOS/mb877451zf19fUVf2U1IONLB4aa9VdHfKUztsIhT7e/Y0gfXjpck4HgP5y4Wkn3kpzXJF1Lf/ZsQr9/7emc17X6UmvDe9V3Ycnc1zQk9K70a5rrdvylS9t06K3LlLuE3dc10Qs69npfTX4m81Hon++Hjqww6g/w4D+PqXWsf8bfDf5rs3xdm/P7fUlvDg6qz31rIUtFFeL/j1BNuF9RTbhfsdiUMyi3JR2Mx+N/Mv7nH8ZisXZJ9yhHUN7e3l6KtVW1iTO0rw1ZGvMvZglf/fmlembIfMZ2NXnhYJNMznA/P3yF/n17Y+Dj/fXlvjbt8uY83/7X63ytaJ77Xvz/rpXW9Ab1k7f009El+ovXP1CTn0m++vr6Cv7zHToe/FlLUiiyZNbn3nayQSb31YGRVt3Vfnn+C0TVKsb9ChQL9yuqCfcrqkmhNpDK2fJrUNKRaV87KumaMqylZkw+Qzs5IJfym7FdbVzDKWyuwQgyz5e+tD86Xgo/c5xZ0rX1pf3RnO9hOCQ9cEdClnzlCs1r+TOpBAs9E06jNwAAABRbOYPy/ZJWTvvaeyQNlGEtNaMQM7arkWlndSf3WyOpcO/Vx50yAAAgAElEQVTh6isyeuXOc7qu2VVQzrwWP5NK0NWeVtjO/d7n6g9AozcAAAAUWzmD8vskfTgWi30xFoutiMVivyOpW9L9ZVxT1ctnxnYt2dCWVlDgK8OGXIV8D8MhaVmDFFQCXYufSSXoaHW1siV3ecTKlozWts4+Y3yhQT0AAAAQpGxBeTwef0XZDuwxSa9K+itJfyrpW+VaUy1YrOW2m1enFA3IVkYdX903BHd1L/R7uFg/k0pgW1LPuhGtWubOCK7Dtq9Vy1z1rBuZs9HeQoN6AAAAIEhZ62Xj8fguSbvKuYZas1jLbR1b2tGZmHVO+URzth2dCaMy90K/h9X2mWS8bAn/E/1hjWYs1Yd8dbWn1dHqVmWX+KVRX72dCe0ZcPR4f1ijrqV6J5vdXhvwmiaC+u49DTp2NjSlgiJs+1rZkskZ1AMAAABBOMRaYwoxY7tarWj2tH/jeW07GNGTJ8JyM9kz5F1taXXfkDI+d97VntZLg47cHO+hk8d7WE2fyUTn/ukB6IFBR9tbqrdzv21J65e7Wj/LLPIgCwnqAQAAgCAE5TWmo9XV9paMDp+Z+6Ot5XJbx5a23pjS1huDy9Tn8pGr3MBu7q4n3XSV2XtYLZ/J5M79003uEt/bmVh0gehCgnoAAAAgl3I2ekMRTD5DW2dNPQtrcoa2WDKe9PQbju55tkF3P9Woe55t0DMnnYocA/bp7zQYXXf3d82uW+i55lJZrJ37AQAAgHLit+saNFFu++D3/1UHEq1lL7ettpLoo8OOgrqlS5Z+fNb8x6caSqDz6TrfQcYYAAAAKAiC8hplW9KvLHtLd/3K5WVdByXRF1V6CXQtd4mvteZ1AAAAqB0E5SiqfEqiyb6WV7V1iTdVbZUaAAAAWFw4U46iyqckulI0GAanDVUWnAbpak/POPM+XaV0iTc1uVJj+n04uVIjqLdBNfVEAAAAQHUhU46iqsaS6A/8QkYvng7er/rgOzKB11STaukSn49CVGqQaQcAAEAxkSlHUZW6JDrtSl87ENFtDzfp5h1Nuu3hJn395UjgiLPJ/s170nICMsaO7eu33lM9GWMT1dIlPh8LrdQoVKYdAAAAmAuZchRVV3taBwZnBjST5VsSnUxLW78X1UuDdfL8bDB505Vj+uwHU/r0U41KupYmd0/vPRLRo6+HtaMzoRXNwdF5R6ur6wIyxtdVWcbYVDV0ic/HQis16IkAAACAYiMoR1EVuiT64Jsh3f1Uo7KJyWwg5fnSvlN12neqTrOPMrOUdC1t2tWo/RvPywmoD5nIGM9Wshy2fa0cL1mutgDVVKV3ic/HQis1GBMHAACAYqN8HUVVyJLodEbjAfnUTHjWbF+bKula6jkUMVr3RMb4K7eO6Jarx7TmCle3XD2mr946ot7OBGeIq8RCm9dVY08EAAAAVBcy5Si6oJLokZQU29mg14Yv3o62fEUsqT4sbWhLa/PqlLbsjWphobClncfD2rImZXR1LWWMS2WuowX3rk0qnLsKvCgWWqkRCQjo377OMCMPAAAATEdQjpKYK8DdO+Do8881jP/pYrbRk6WkLyVTF8+EpzPB2fAgbm01TK8ouY4WrOmt0wN3JLT6itJ+AAs9ivDuyzJ64bSj3PedrxWXcWMBAABgfgjKUTajrsYD8qBA2xpv3rbwbKRThmztYjD1aMF0lnxl//6VO8+VPGO+kOZ1b/w8JJP78/hb3FgAAACYH4JylM3v72oIvqigfHW11dYYs0phcrTAl7T1uah6OpKlWNIU8z2KkDI9U254HQAAADAdjd5QNkeHg8qCp1tYtjzq+Oq+wew8OfLz4um5Ot9PZumFU3WlWE7BLLR7OwAAABCETDmMXRiVPvP01IZs1zW72t45ovqKuZMmgiNryteijq8dnYnAcWiYH88wJjW9rlJ0tad1YNDJORYtV/d2AAAAIEjFhFKobHM1ZDs67OjGBy/RtttHjGeNL0Rjna+RMU1pJpaVPc38X341oX0/dfTkibDcTPYMeVdbWt03pKYE5BlP2j3g6In+sEYzlupDvrra0+oIOGOM2dmWWcBdbe/tQru3AwAAAEEIyhEod0O27Nc+/1yDXv7kubwy5tc1u3mWsPv6nfek9ccfyo5He/H0xbFbH7lqTN+4PTt268YrM9p649xl6kNJa9Zu3AcGHW0f78bNHPL83HTlmPadCiph9/WRq8ZKtaQp5rsJs9Du7QAAAEAQgnIEMm3I9unvNOjh3xgxftztnSO68cFLjK+fOBPu2NI31ycl5d8wzPOl7j0Ns2Y+056lw2ccde9pUG9nouCBVtqVth2KaNeJsFxPcuyLM9irvaz+3rVJremty3ni35L0jdtL3+RtoZswC+neDgAAAASp8lAApWCWzbb047P57fHUO9K220eULUbPFc75ijpeQc6E7x5w9NpQ7vFVrw2FtHegsPtV/cO2bnmkSQ8ejWg4Zev8mK3hlK3eIxHd/FCT+oer+0cxHJIeuCMxPvxs+mfpy5KvB+5IlHwc2uRNmOnnwidvwgSV3k90b7+/Y0QP3JHQ/R0j6lhOQA4AAICFq+5IAFVvbaurlz95Ttc1u7oYnPuy5SlqeWqu9/Sp61Pav/G8VjR7C36+x18Pa8zPHUmN+Zb+R194wc81wfWkTbsalXRtzdzcsJR0bW3a1Sh34S+vrFZfkdErd57TrVeNybF82fLlWL4+evWYXrnznFZfkSn5mnYPODp2NvdOwLGzhd+EAQAAAEzxmyjKrt6R/muXedn7QpxOmO1DDV4o3H7VfQcjSrq5NwKSrqWeQxFtWVPdI9vCofkfLSiGnX3hnJ3TpWzG/LH+sDrynGEOAAAAFAKZcswpmZbueSY6/qegxme+bEm3Pdykr78cqdis7/CoWb3x2ZSltCt97UBEtz3cpJt3NM37tX37eFgm5f87jxcuO4+s0YzZ5z0asGkCAAAAFAtBOWZ18M2QPvzQJXr+9ERH7eCg0pNV8eekW+rNuqovqfMLdgbcNIh3S1/dXfPqQ2afd71Dt30AAACUR+VFTSi7dEa6+6nG8cnf88kgVu456XcuMVvQT8/bBTsDbtqczilxE7TFoKs9rbCdO+AO29lO6gAAAEA5LPhMeSwWWyrp1yRdKek1Sd+Ox+MVFoohH1v2RgOL1S+Ws88dtFfiOenfbE/rwKCjsRznjG35CrqB83ltG9rS6j0SUdAM7642AsNC62h1tb0lM+sIvAkrWzJa28p5cgAAAJSHUQ4vFov9XiwW2xOLxS6f9vVfVjYQ3y7pq5Iek/R8LBYzG2yNivTi2yXruZiVtJf6nHTGk55+w9E9zzbo7qcadc+zDXrmpPP2yKuOVlfvbcldJ25ZUiFf2+bVKUUDyqMnZrCjsGxL6lk3olXL3BkZ87Dta9UyVz3rRhhtBgAAgLIxzZT/rqRwPB7/12lf/0dJzZL+vaSXJXVK+iNJWyX9RaEWidIKmtmcj1Kekx5KWure06BjZ0NTOm4fGHS0vSWjnnUjWhr11bNuZNbrwravlS0ZnXjLVsKg8Zfpa3NsaUdnYnws2vTNDF9Rxy/IDHbMbmnUV29nQnsGHD3eH9aoa6neyZasr21l1jgAAADKyzQo/4CkHZO/EIvFbpTULuk/xePxL41/eVcsFnuXpN8SQXnVsq3CBealOift+VL3noZZy5TTnqXDZxx172lQb2ciMEi7/ZEmJQyqmfN5bSuaPe3feF7bDkb05Imw3Ez2+7va0uq+IWUUkKddaduhiHadCMv1ssH+hra0Nq82+/7FzLak9ctdrWfsGQAAACqMaVD+DklvTPva/6bsweKHp339WUlfXuC6UEY3XTmmfaeCStiDz5SX8pz07gFHx87mjpKPnQ1p74CjjuVuziCtWGfAHVvaemNKW2/Mv0y9f9ieNdPeeySiR18Pa0dnQiuaaeUAAAAAVBvT/NrPJTVN+9rNkjKSDk37+nnNr2U3KsS9a5NGJ8rDVuWck97ZF55Sij6btGfpsf7gc+CVdgbc9TQekBemGzwAAACAymEalB+R9PGJP8RisRZJH5H0SjweH5l2baukNwuzPJRDOCQ9cEdClnxpRh92X5Z8PXBHQo/8RkJRx5v1mqjjlfSc9GjGbB9o1OCs+MQZ8Ep5bfcdjIxnyOc20Q0eAAAAQHUxLV//hrLnxXdL2i9pg6SopG/Ncu2vSfphYZaHcll9RUav3HlOW/ZG9eLpOnl+9lzuR64a0zduTyo8Xim+0HPShVIfMjsEXx+QAZeyHdyPv2Xrhl/IqG/Y1lDSlmP5qq+TfnNF6V/bt4+HZdoNvpLGzwEAAAAIZhSUx+Px78Zisa2S/lTSWklJSX8ej8enN3+7SdIaSf+p0AtF6YVD0jfXJ5X9uGe3kHPShdQ1Pn88Vwl72M42c8tlrg7utiW9qymj339/uuRN1UzL0kvZ6R4AAABAYRiHF/F4/F5JyyS9U9KSeDw+W3f1H0q6XNlRaUDJdLS6Whkwf3xlS0ZrW+fuvj25g/v04H5yB/dCjowzYboJUKpO99UomZbueSaqD22/RB/4h0v0oe2X6I+fjSrNRgYAAADKzLR8XZIUj8czkn6W4+9zp1WBIrEtBc4f71k3knMmtUkH91fPhPSJJxr1B7+UUkeJZlwXqxv8YnHwzZDufqpxvDtA9j30fGnfqTqt6a3TA3cktPoKonMAAACUh1FQHovF3pHvA8fj8X/JfznA/AXNHw8KoE06uPuy9Nqwoy/sC2n7eKC/NFrc1Pnm1Sk9+no4Z7O3UnaDrybpjMYD8tneu2wrw7ufatQrd557u08CAAAAUEqmmfI3NbMNdRB+xUXJ5Zo/HiSow/lkk8vZezsTRc2YT3SDn21OebYbvF/SbvC5JNPS1u9F9dLgxeaAN105pnvXJssS9G7ZGw38h8uXtPW5qHo6KPIBAABA6ZkG5X+h/INyoGJlvGy5+hP9YY1mLNWHfJ2+kH9kfexsSHsHHHXMYxMgHyuavYrpdD+XSiwTf/F0nUw6179wqk7lPHkz2/3Y1Z4u2REJAAAAlI9p9/U/K/I6gJKZq8P6fPad0p6lx/rDRQ/KpcrpdD+bSi0TN23KV+rmfZMNJS19bneDjg6FlJn0/r14ytF1SzP6m47iH5EAAABA+eTV6C0Wi0UkfVLSr0pqk9Qk6bykPklPS3ooHo/TbQoVa3KH9Znml5IczaPsvVZVapm4bZkF3OXKRnu+9NlnG3T07Mz7MSNLrw45+uyzDXr4Y8U9IgEAAIDyMS56jcViqyQdlfSfJf2OskF5w/j//q6kByT9OBaLXVeEdQIFYdJhPV/1DlnM/MrES+emK8cUXAHh6yNXjZViOTM8c9LRawH342tnQ9p9Mq/9UwAAAFQRo6A8FostkfSEpF+Q9EVJ74rH483xePxd8Xi8WdK7JP2/kq6U9GQsFmss1oKBhTDpsJ6PsJ3t7r7YVWqZ+L1rkwZbBdI3bi/PefK/+1FkjpL/i3xZ+tvDkRKtCAAAAKVmmn75tKRrJK2Lx+Pfm/6X8Xj8lKSvxGKxA5KelfQpSfcXaI1AwYxmClsDvLIlo7WtxT9PXukqtUw8HJIeuCMxowFdVjYcfuCORNnGoQ0mzIqVBi9UQCc/AAAAFIXpb3qdkp6ZLSCfLB6P71U2KP/YAtcFzEvalb52IKLbHm7SzTuadNvDTfr6yxG5Xvbv60NmqVprRsnz1D+HbV+rlrnqWTfCWV9Vdpn46isyeuXOc7r1qjE5li9bvhzL10evHtMrd54reUd4AAAAYDLTTPkqST2G1+6V9H/ObznA/PUP27PO8u49EtGjr4e1ozOhrva0Dgw6OUvYw7av37supRM/D2nUzY6nuvayjI6/FVIqY6neyZasr2Vc1dvuXZvUmt66nGF5OcvEwyHpm+uTKufYs9m8c4mnn58N3hu9colXgtUAAACgHEyD8hZJbxpe+7Px64GScT2NB+SzBTiWkq6lTbsa9fzvndfKlswc3dezVrZktHl1ioA7D+GQ9Bc3j+hP9zeMf2XmqLm/uHmkbGXileruVSn9238K5TxXbsnX3asqbwweAAAACsO0fD0iybTu1JUUnt9ygPm572BkPEM+t6Rr6Zs/iKhn3YhWLXMVtilJLxTXk758IKpsMD79zct+7csHom8fI0DW+uWull+a+01ZfqmnjuX0LQAAAKhV+czZuTYWi91ocF3bfBcDzNe3j4dlMpJrZ39YW9ak1NuZ0J4BR4/3h7Ml6pSkL4jppkjPoYi2rCHrO8HzpcELud+3wQuWPL98s9QBAABQXPkE5X8+/l8QS8Edn4CCujBmFrFMXGdb2SzleoMMZNqVth2KaNeJsFxPcmxpQ1tam1en5BSoKXYpnqOYjDdFjocJyie572AkcCLAaIbNDAAAgFqWz0g0oGKNGZZFm143waR53IrmhdVkl+I5is20LN2l0fkUO18328z4H8fYzAAAAKhVRkF5PB7fXuyFAAvhyJcbGNxIufuDT2XaPG7/xvPzzmaX4jlKwXRtDo3epjgXUPKf73UAAACoPhX8az5grrHO7LqGPFoQ5nNOer5K8RyFNtss+CsaMjKZU97Vli7FEgEAAICqQVCOmvAb7WmZBIUfbzcPCvM5Jz1fpXiOQuoftnXLI0168GhEwylb58dsDadsHR0OLrqJOr66b6AEGwAAAJiMoBw1YfPqlKJO7qA836CwFOekxwy/d6wCJmJNLbWffexZdmNk+ufgK+p42tGZqOgSfAAAAKAc+BUZNcGxpR2dCUUdTwsNCjOe9PQbTmBZ+dvPvYBz0qYn3P0KOFJsUmovSe9rcdVc76mpzlNzvadPXZ/S/o3nK75ZXTlEbbP3pCHEewcAAFCr8hmJBlS0Fc2e9n3ivP6v56J68XTd27Odb75qTP/h9qTCBsHzUNJS954GHTsbkmsUCS/snPSlEV8Jgyx4c6T8UwZNS+0HR0L6p0+cL8WSqt4H3uHppTeDb8wPXE5QDgAAUKvIlKNmDCUtfeapRn1/sE6ub8mTJde39NLpOn36u40aSuYOKD1f6t7ToMNnHKU9s9T0Qs9JX7nELNi+orH8QTljzwrvt96blh1QL2HL12+/lwZ5AAAAtYqgHDUhV0Cd9iwdPuOoe0+DvBzxz+4BR8fOmtaiF+acdNA5+Levqyt/UM7Ys8Jbe42rOjv3Z1tn+/roNRXQVAAAAABFQVCOmmASUB87G9LegblPbOzsCxtlyB3LL9g56a72tMIBQVnY9vXxFeXPlHZea9bh/jeuLf9aq8UzJx2lAu65lGdp90lOGgEAANQqgnLUBJOAOu1Zeqx/7tFioxmzkvUP/kJGW9akCtJJvKPV1cqW3PXeK1syWtta/kzpqsvN6tLfb3jdfMw2I/3rL0eMS+srTc+hepmc09/2g/pSLAcAAABlQFCOmmAaUI/m6B5eHzIrEa83LDk3YVtSz7oRrVrmzsiYh21fq5a56lk3IrsCuq+bNnp7okgz1eeakd57JKKbH2pS/3D1/XP2s4TZmn92ofpeGwAAAMxQE4makG9AnfGyJe9P9Ic1mrFUH/K1/NKMXjrlyM0ReNZZhS8lXxr11duZ0J4BR4/3hzXqWqp3ss+zttWtiIBcKszGx3xNnZE+naWka2nTrkbt33i+Ymahz3aPdbWn1THpMzVN8FdpIQAAAAAMEJSjJnS1p3VgMHfX9Imz2ZPHnk2+/sCgo6DCaydUnKZbtiWtX+5q/fLyl6nPpRyVBBNMZqQnXUs9hyLasmb+3fALJdc9tr0lo551I1oa9WVZkm/wdlkVsjEDAACAwquQnBKwMB2trtouyx1St12W0UevcXN2afcDyrNdz9L3frI497LK2ZTOtHR+Z5FK5/ORzySAZVGzHPjlhtcBAACg+izO6AI1yQ1o9OZ6lvbkNfZspjHP0mN9YXVUSEbbpES6UDpaXW1vyejwmbn/2ShWU7pqmpGezySA5oivfxkJfszLInMfuyjW5w0AAIDSIChHTXj2pKP+t3IXfvS/ZeuBwxGjsWe5DBo25yo20xLpQploSjfbc4ZtXyvHn7MYwWE1zUjPZxLA+bTZm3VhzNJQ0tIf727Q0aGQvElVA/tPObpuaUbf7Cjs513p2KAAAAC1omKC8lgs9ieSvizp/ng8/sflXg+qywOHI4Gl574s/eTcwgPqodHy/8Y/uUR6uskl0r2diYIGKOVqSrehLa3eIxHlLmH31dVW/hnp+TTEu2AYlJ8btfSHTzeo762Zn7cnSz8ecvSHTzfov3UV9vOuVKXekAIAACimikj5xWKxD0v6Q0k/KvdaUJ0GDUdGpQ0DplxaIuX/ZT+fEulCS41Jj79ep5dOOTr0ZkgvnXL0eF9dUWeFb16dUjSggVzU8dV9Q/mbvOXVEM/wdhzLSH1v5f68+94K6Zk3KmaftWjyObMPAABQDcoelMdisUsl7ZD0GUnDZV4Oalyd7Qc2KwvyziWFjz7TrvS1AxHd9nCTbt7RpNsebtLXX47MGejmUyJdSAffDOnDD12i50/XyfUtebLk+pb2narTmt5LdPBN8/rxjCc9/Yaje55t0N1PNeqvjqzQMyedWYMpx5Z2dCYUdTxJ0y/wFXU87ehMVMQ4tHwa4jUYBvBp35JJo7v/+IN6s0VWsXJuSAEAABRDBfwKq/8s6dF4PP5cuReC6vXORrNA+ZpLPK1smX83sDrL179pL2yJdP+wrVseadKDRyMaTtk6P2ZrOGWr90hENz/UpP7hmT+m5ZgZns5Idz/VOH5MYPrjZjvX3/1Uo9IGb+9Q0tJd32nUF59v0POn6vTKm45+MHyZvrCvQXfuatRQcua6VzR72r/xvO56X0rN9Z6a6jw113v61PUp7d94XiuaK6NDeUerG3iPTTTEGzW8FU1f2c8qpN9BMZVrQwoAAKBYyppKiMVi/7ukFZI+afo9fX19xVtQDVos79evX96s14avVdCZ449dflLvu/SCvjK6QicTUY35FzNudVZGtiWlvLmzcMsbE3pX+jUV6m11PemuAx9UypstmLKUdC393pMN+sdf/uGULHAmtULSZYGPn0ldUF9ff0HW+uUft83IUU/nS/qjJzP6k+tPzHmN50tf+NF71Xdh7vPwf7jL1pd/8bVZz0d3NUtdN0z92hvHg9dfSpvf7cx5jy1vTGrzu/t1vN/V+fSHCvq8Gd+v+Z/54fPvkVQXfN25ZNHfi1p/r1FbuF9RTbhfsdiULSiPxWIrlW3s9pF4PD5m+n3t7e3FW1SN6evrWzTvV9sK6btnMjp6du5b+rqWjD75K5fLti7Xf3tfRnsGRmc0K/uld2T0+b25uot7Whot3Hv69ZcjcwTkF6U8W989935tWXPxvPTGOkev7vNzZgzDtq9P/lKd2pcXZr3//OIlMimh/p8/b8553z1z0tHASEPORxkYadT/Cl9XMaPn5mOue2xta0a29e7sRS9aM6vxF8CyrJr/mW8+GZXOGVx3SbSo78Vi+vcV1Y/7FdWE+xXVpFAbSOXMlP+KpGWSfhyLxSa+FpJ0aywW+yNJjfF4vPxdm1AVbEv61voRfW58ZFRmUvAYkq/rlmb0Nx0Xx3XZlrR+uav1swR9pewu/u3jYZkEujuPh6cE5eWYGW7aOCvounzKj6cH5dU0BivXPTah3vGVGCvcwpfVV0YJfzF1tad1YHBmk7fJJs7sAwAAVINyBuWPSzo47Wt/L6lP2Qw6v1EhL0ujvh7csPCA2iSYKhTTjuXutLPH5ZgZbprQDbouaXjOPTktWK3FMVgfb0/rQYNRb++MehpMBjfRa6+Qc/XFVI4NKQAAgGIqW1Aej8ffkvTW5K/FYrGEpLPxePzV8qwK1c73xzO1E7GZb94kqxxMu4U7s8RjpZ4ZHpIv12CGVyggLL9guN12YdKhlnLNZS+2LatTir8WUTrHTRq2pc+vGdUX9zXkfP8d+fqt99T+XmY5NqQAAACKiZkxqBnFyqQWs2R6Q1tavQaZ0q622YOtUmb1w6GZGfu5rsvJ8D2bfFk+Y7Cq6Ry65ytnQC5l//72a1xdtyx3dvi6ZYsnO1zqDSkAAIBiqqigPB6Pf7Tca0B1KlYmtdgl05tXp/To6+GcJd1Rx1f3DeVvrxBxpBGDoDwS0Bh7SXDjbElS46SJVgs5h17JNu+JGl33fz8XJTs8TbVVxQAAAMylooJyYL6KkUktRcm0Y0s7OhPatKtxPDCf/EC+oo6vHZ0J4zL3YlpoVn9C1DHbxJh8XTnmspfCC6frZNLob9+pOi2NJskOj6vF/gIAAGDxqoBf9YGFyyeTaiqfQH8hVjR7ei52Xu9tdpVN+2X/u67F1T994rxWVEjzrs2rU4EBtUlWv6s9rbCd+3HCtq+PXZvW1w5EdNvDTfrBz4KbnEnZbubVJN/meRPHFe7vGNEDdyR0f8eIOpYvroB88mbZ9J/5yZtlptMCAAAAyo2gHDWhGJnUYgT6s+kftnV7vEmvDTvKZk2z/x096+i2R5rUP1wZP6YTWf2I7WlmOOkrYntGWf2OVlcrW3LXwbdektGXXozqwaMRDadsZfzgz40xWItDqTbLAAAASqUyftsHFqg+ZJYWyyeTWoqSadfTeOm6rZllzJaSrq1NuxqNR6eVgj3HvxpzfX3GdePds1ctc2dkzMO2r+tbXP2v8/Yc78ncrlpSfY3OQoYvz1lEmfAgpdosAwAAKBWCctQE05LofDKpxQj0p7vvYCRwbnfStdRzKDLv5yiUQm4gTHTP/sqtI7rl6jGtucLVh5rf0ldvHdEHr3CNN0QmO3kuVFGbFyZWXGbQOU/SiubsdRlPevoNR/c826C7n2rUPc826JmTzqIq1a7V/gIAAGDxIihHTTApiV7Zkl8mtRiB/nTfPh6WSaOvncfLn/Ur9AbC9PPRX3xfvzqWu9pl9J7M5Eva+pxZN/NK8Qe/mFLwyXJff7AqpaGkpbQrxz4AACAASURBVLu+06gvPt+g50/V6ZU3HT1/qk5f2NegO3c1aii5OILQUmyWAQAAlBJBOWpCUEn0qmVu3iOjihHoTzdmlijVWAVUZZdqA2H+2W5L3/tpXVVlj9cvd3VdwD123fg9RnOzrFJslgEAAJQSQTlqxmwl0bdcPaav3jqi3s5E3iOSihHoT2fcfbsCkqCl2kBY2Pg3q6qyx7YlfWv9iN6/zJVjTb0bHMvX+5e5+tb6Ee39Cc3NJnS0umoLKPtvu6z6+gsAAIDFq/Z/g8OiMlESvd5wFvmEn49Iv7urUacTFwOfqxoz+u+/mSjqbOhLI74SBkttjpQ/BVqqDQSzeei5FWqOfCksjfp6MOAey6e5WUee9z4AAADKi6Aci97OPkd/ur9h/E8XA59TiZA+vOMS/eXNI+pqzz/QN3HlEl+nE8HXXdFY/qC8VBsIm1en9Ojr4cDz6yYmsseVHqgGbSaZvhfJsQrefSiQ3QOOjr+Vu2rg+FvV8bkDAABIlK9jkRsZ03hAPjEffLLs1/50f4NGxorz/FHDZlTRuvIH5VcuMVvDQjcQJuahR53Z5qHnp1ZGY10wPB59oUj3aSVhJBoAAKg1ZMqxqP3W441G1/32zkZ957cNUtp56mpP68DgzOZdk1VK06pSbiCsaPa0f+N5bTsY0ZMnwnIzkicp8XYm2DwjXK7RWBkvm9V9oj+s0Yyl+pCvrva0OuZz7MHw+onLCvrcFYaRaAAAoNYQlGNRO5UIyaSj+E8v5C6Xna+OVlfbWzI6fGbuH8WFdngvlK72tL4/6GgsxwZCXQE3EBxb2npjSltvTL39tXRG2rI3qhdP18n1JZNotRyjsYaSlj63u0FHh0LKTFrji6ccXbc0o7/pGMmr8eCSOrPrGsPZ5+7e06BjZ0NTNnsODDra3pJRz7r8nrvSMBINAADUGoJyoIwmOrzPFkSFbV8rx4OoubKbpcyIrr3GVcjyNZYjEA5Zvj56jdkGwvS1Z1IrtLHOeXvtaVfadiiiXSfCcr1skL6hLa1t65Jy7KSeOenoC/saKq7KwPOlzz7boKNnZ/7zmpGlV4ccffbZBj38MfMGdMZVCiH/7dFp01VT87tcqqm6BAAAwARBOVBmE6Pc8u3wXuqM6O4BJ7B0eDRjae+Ao199d+7AfChp6XN7GvTaUEju2+3aL9OP/snXe5dmtGX1qO7Z3TDe4Ozic/YeiejR18Pa0Zmo2CqDZ046ei1gfNlrZ0PafTL4fZpgGoi++7KMHjkayflY1dL8bi6V+rkDAADMF43esKhd1ZhRcDMxX1cvMRzSPU8T3bfv7xjRA3ckdH/HiDqWzx2Qe77ezohOD9QmZ0S9Albw/t2PTMaUWfrbw7mDwolM8qtnnEkBeZbrW3r1jKO7n2pU0rVneT5LSdfWpl2N8vxslcH7l7oKTfsMQ/L1/qULnyM/H3/3o4j8gPfJN3ifJutodbWyJfc9uLIloxPDoZpvgjZRXbJqmauwPfVzD9u+Vi0rz+cOAAAwXwTlWNT++2+aNW97tKvwTd4WYveAo2MB2diJjGihDCbM/rkYvJD7umcNMslBewlJ11LPofGg1pKsaQGYNVsz/RIp1Ps0mW1Jf35zco6O9L6ijqc/vzlp3ASt2kenTVSXfOXWEd1y9ZjWXOHqlqvH9NVbR9TbmajqM/MAAGDxoXwdi1pDnfSXN4/MOqd8Ivj5y5tH1GDYaKtU8hkLVWllyg8cDs4km2Tk/+HHEfUeiSjjz7x2IuNejvPTxQgHPV/60v7oePXAdJaSrqUv7Y8qbfhR18LotKDZ7gAAANWCTDkWva52V9/fdG5SKXv2v6uXZPT9TefU1V55v/SXYyzU5Q2e0XVXLsl9XT4Z4tysWQPyyQpdLWBiieFIuKawefhuWhlx3vDzru48OQAAQG0hUw4omzH/7u9UVol6LqUeC+X5UtpoI8DX3atSwZeVSDmqBZrCvmRwK5kG75J5ZUQibRZuN1bvkXIAAICaQ6YcqEJd7ekZTa6mK+RYqN0Djt40OCt91RIvMAB+Z6NZxr1QClktYKLJMOBdkkdgnDR8Db5hnG86Yg0AAADFR1AOVCHTbtyFGgtlkqmVpHc1eYHntz/ziylZBh3vC6VQ1QKmjGeK55Epv2C4t9IU8Uu6WQMAAICFIygHqpBpN+5CNTgzzdS6BoH7ry539d6ADYVCKUcAWpQqBsPP8ZKwX9LNmnLKeNLTbzi659kG3f1Uo+55tkHPnHQKOgYQAACgFAjKgSo0tRv37LO8v7Q/WrAAxTRTa9LV27akb62fe754yPJVqFZk5QhAi1HFsMSw+39jeHHM8B5KWrrrO4364vMNev5UnV5509Hzp+r0hX0NunNXo4aSVf4CAQDAokJQDlShks8pN4xxTEOhpVFfD25I6GsfvThn+kPNb+nrHx0p0HnnwlcLmLKtbGD8/mWuHGvqa3EsX++fR2BsXBLv+Foa9fUPv5bQ7743peaIp6Y6T80RT5+4LqXtv179M7w9X+re06DDZ5wZRyrSnqXD46PwyJgDAIBqQfd1oAqVek65aaY2lEegOX3OdF9fv9qXt+svX5rHAme4OLu71HPK3+bPbLzmT0zcy1NXe1rfH3Q0luMzrxsviR9KWure06BjZ0NT7pFHjkb0w5856lk3UtWBeT4bUqXsuo/iynjZz/6J/rBGM5bqQ7662tPqaHWrvvIDAAAy5UAV+v/bu/84uer63uPvM3N29ldCfmyshNYkkCwBTNCWgG39US9J0HaBoO09tViQitVbQASLWrHXH1dre6/eKqH48KoIGLD21Fv5aYUkXjFGRUAtCYS4G0io+UFg84Nkf8zszJz7x8yE3dnZOd/5ec6ZeT0fDx5hd8/OfmfnzNnz+X6+38/HdI+36XF+TDO1Tw3Hla6xuPqFS1OqV6G3IPqUFzK524dtZYrWDmRkaftw5Znc8xelZfvsU7djnt70qnTLZ5ErmZBCa2C7AgCg1RGUAxFksndbkkbqVONsXX9KMYNAOStp/eOdNf2s61cl69ayK4jgrBFbC77/nK20T5/4dMbS+p93NndbQ5NMLur2y4Pln19Bs1vhoTHYrgAAaAcE5UAUGd6A1us+dc3itCyjGMfSPbtqC4LtmHTXwMgMleUr1+zgrBGZ3HsGE5rwyj/mhGfpvqHWyyIXZ0mPTZj92Wp2Kzw0RtPrZwAAEACCciCCZhnGVKZ7wf3ELKkrbhbkpOvQ7WzZvKy2XnpMl5+V1LyubP5nVxdkNTs4G/fJaJ84roLJAtPHNN06EJUscrksaTn0Ym8dbFcAALQDgnIggoyrcXfULyC1Da8WttnqYqOfd8N5ST38jmM69+S0qmmTFkRwZjp5Uclkgeljmr5GUckim2RJS2mVXuxozCQXAABhQ1AORNC6/tS0PtTF6hmQZr1cdW9/ntYtrX8QbHpjXiyI4KwRr43pY160rLnnRaOZZEkna6Ve7MhpxCQXAABhQ1AORNCaxWktn19+nXg9A9JNe2y9lPK/XHTEPF17TrIuPzOVlv7XI536g3+erZ8/X2m2NLg+5Y14bUwf87pzkk09LxrNdDJmlp3VGfPTWjo3o8649PEfdeuh3TbFv1rAxctSsny2rlgNmgwEAKBZCMqBCIpZ0vrVo1q5ID0tM9qIbKFpxvLsV2SMl1CXM3Q4pjd+a7bu3NGpw8mYMj5FzqazNJaO6RNbu5semDXitTF9TDsmfer1YzMUyQtuoqKcyZXVr/xer67e2HMioDbNksqSnjkS145Dth57nnZZrYR5FQBAO6BcKRBRfd2eNgyMaPMeW3cPJTSettRl55Ymn784XdfAyzRjGTMr0V5WOiu984FejaVrj+4LVZnXLPHPDGeyuRUB9w4lNJ6x1BX3tK4/pTVV/C4b8dqYPGbWkz6xtXuG352lsbSlT2zt1oaBkVAE5sNjlq7d3KOdh+JTJn0e2W/rjvkZXbIspS17bZWvJ+DpeImK7JPbZYXl+aJy9w0l5PnUk/DyXR8uODUaK0AAAChGUA5EWMyS1i5Ja61B0FmLZu7r/MJjnRqrU9GmQlVmv6DcLzhcv3pUfd2VPbdGvDaelwu8T6QPvVxv+IJK2keZTFQ00uTK6sUKAfXeY7WfB2F5vqiO6bVgbIJZFwBAdBGUA/B1sWHGsh77Ou/flfD5OZXxq8psEhyGIdtqMnFQSfuooINUkwmEQ8mY/M+FaDxfVOe44SXl+ERjx4Gp6rmyCABAUA7AgOm+7Hrs3zbttW3KL3sfheyy6cRBp2E9vDC0jzKrU1CfcYbh+aJKhi8dr3DzDI9ZumZTj3YMx5Wd9JvfutfWWQsyurmKlUUA0O4o9AbA123bO2WSscwdV5t6FIorMGn/VUl2OSgmEwfbXozrccMq9WFoH1Vtm7tqdJoWjEPozOowO643uLdnW8l60nsf7NWTw/aUgFySsrK0/UVb732wl84HAFAhMuUAfO0/bhYp7zM8rpwLl6a04SmTSQB/Ju2/Rg33opoe1wimWWWT++Cw9Ck3rqxeM0+2RYQQVd2GE0imx6E2Dz5ra/BI+ev84JGYNu629RYK7zUc2wiA1kGmHICvZt7uXr8qWfMNtm15WmHYemz/iNmdywHD4xqhnlnlsPQpX9efmtbebbp6nHmWfvkC889RZXKehGWiqR3c9HiXTFZN5Y5DIw2PWbr8u7362JYebdnboUcP0A4SiDKCcgC+ZnWYBUe9idqDKDsm3TUwMkOvbTPe5ArlPo4mzW5cjowHd4NTj6xyI/rX12LN4rSWz8+UPcb0vPOTqXOdAjSPyXkSlommdvD8iNlt437D41CdyXVGildRTa4zwjYCIDq4agLwZRocza7TEtJl87LaeukxXX5WUvO6sprVkVW8giXIGVnaPmx2U5IyzEL7Lx9vHLOscmkxeXrjb03oH940qg0DI6EpwBSzpPWrR7VyQXracytMIPz1qvGqn/dktmEBPISPyXkSlommdlB+euRlBIONVUmBUgDRwLsVgK+0YUCa9up3Z2zHpBvOS+qG85KSZm4JVs7TBlXTJwxvHicCzLauWZzWrXMz2nGo8ku2ZUm3rBltwKhq19ftacPAiDbvsXX3UELjaUtddm4pciHz+W+DiZJV51/mqRmt+hAcv/OEgDx8iMkbK0rtLwGYISgH4Ouw4RLvQ4bHVaP4xvyJg3EdTZVf7DORtfSdwfI3JbYlpQ3uIDsieuPvhfzuOGZJa5ektXaG12j96tGSkzGJmKf+uRntOhoru+e+2/Z07TnJuo8bzeV3nqA5TK+XdkSvl1FhWmeEdpBAdBCUA/A1v8vTYYO4pq+rsRHg5Bvzt31nlo4aJED99jbO6vB0JOV/49Jbp/3N1di0x9auI9Wtwe6ow/LvIPllSZ85EtM7H+jVWNrS1Iy5p27b010DI3Vtswe0s9kJz2iSdnYd6otgZqZ1RsLQ/hKAGYJyAL4Wzspq11H/oPDk3uat8T5kWHjNrwLtRctMWrB5uqQ/uCXQZi3RSjulN/o3ZeWypKfOyerjvzemm3/epYOjMXnK3Yj+cX9K161KEpADdTSwNKU7Da6XF7FlpKHW9af0yP7pRd4moysBEC0E5QB8nTonox/tteV3I7ZsrmkZoNqZZoA7fTIK169K6l93JsouB+yKB7sEupaWaL95UuuWHp+pzsBERvrFQVtHk6nQFLYzQc9hhN2ZPpXwTxzX17y/Be1ozeK07pifKVtvg64EQLQQlAPw9eyRuEx60w5VucS6GqZV0/0C2pglvWq2p8EjMx/zqtleoEFR9S3RPF0SwkxJPYLPyS2BihVaAr1/U4/uvHAkEgHtTBMMj+y3dcf8jNavHo3UBANa0y2/MOtT/k+/6NLA0uPNGFJbilnSp14/pnfe36uxTImtO3FPn3r9WCSufQByCMoB+EoaLp1O1pDRrdT8bk9HDOJNv33um/bY2vNS+TXOe16K+VZxb6SLl6X0o722PN+b4anCeD82PGbp/Zt79PRwfEq1/p/ss3VGX0Y3GwafJi2BnhyO655BW287PdzZIpMJhms392jDgP8EA9l2NNLBUbP9IKb9zFGdrCd95OEejWVK/Z4tjWUsfeThHv3ruuO874GI4KoJwFcYi8qcYrh/feGs8sdV0lomKNXnyS3dsyu4cRfLetJVG3u0/UV7Wvu8tGdp+4u2rtro31teMnvdPFn63KPdoe+ZXK+ew8Njli7/bq8+tqVHW/Z26NEDtrbs7dCNP+zRZQ/0+tZXAPyYvpVC/paLvAeftTV4pPwt/OCRmDbuJvcGRAVBOQBf6/pTSvjs4W52UZlLTk+pwyo/pg7L09t9CrRFobXMfUOJirPkBWMT4QnENu629bRP8LnjUFz/+9FO30Da9HUbmbB8g9l6GktJVz/Urd+54yS99vaT9Dt3nKRrNnYrVWaLbT0mhiZn24sfa3K2PewTFAg301ZnFFhsrJseN9tGkDsOQBRw2QTga83itJb7FPhpdlGZNYvTOsOnmNAZff5jCuMqgGK1FHo7HqIt5bdu6zSYXLC04alO38yu6evmqXmrHB47ENfrvnmStuzrUNqzlJWltGfph3s7tGrDSXrsQOkJiXpMDNUr2w6UM6/LbIXS/M7WLTAZBs8bbiPwawkKIDx4twLwFbOk9atHtXJBelrGPBHztHJBWutXjzZ171q9xnTh0pT8F1t6ujjAFj/VF3qTjocoU77/uOmfHP/M7kXLTF63nGasckhlpHd/r1e57FXxz8t97t3f6y2ZMa/HxFAUtmEg+roM38Jdzav5CQAtgSlzAEb6uj1tGBjR5j227h5KaDxtqcvOLVk/P6AiUvUY07YXzO4et78Q11tODaZgmElP2pkcS4UnKK90aqGQ2S1VYK+SZ+XXFq8ert/cbXTcX3+/WzevHZvyuXr0HI7CNgxE3wvjZufPQcPjUJ2uuKcRg/dykCu8AFSGoByAsZglrV2S1tqAqpCXUuuYHngmIZO9efc+k9BfnxdMr3KTnrRRMKvD00sVLDgoZHZLBeX3Dpm8bpLkKe5Te6Aetu7rkMl5tGVvh6SpQXk9eg5HYRsGoi/rmQXbnuFxqM7b+lO6c0enyl9zPP2JT00VAOHB8nUAbS1tuPUxXX77ekOVW6rvZ6FhlfpmmJ2oPCCcKbNrvs/e0k/3dVT8cytl+lsudVzh9V3Rl1a8aD1BXJ5W9PlvxTBbzu9pXYDbMKRcy7YHn7V19cYeXfm9Xl29sUcP7bYpQBcZhrUcPF7QRvrguUnfibiuuKcPrApmIhlA5aKddgGAGplWCbYD3iNZvFT/V4di+WI/5TMlr1sYnlUNs6vYzjxTZreSffbJ8MxLlGdJlqUpcY9Vaot6CaYxUJCh0vCYpWs392jnofiUpfqP7Ld1x/yM1hv2qEdwTDPl2Sq7RcCMHZO+tHZUV36vN/+envz7zpXT/NLaUargAxHC2xVAWzMt9BZ0hlF6ean+LWtG1T83K5Pl0ruOhqfiUneFS6eL91FPzrLuPR5TmLohz+s0G8v8runHFdqZlevf7tfO7LbtfktZJcnS17d1Go2z3mjZ1homDCe4JgJcWdQOsp70hce68uH39MKSnix94bEu3k9AhBCUA2hr169K+gaL3bana88J1zLApGHRt2QN7dTq7eJlKVkVBNKT91EPj1m6/Lu9+tiWHm3Z26FnjsZlWu6ts8Il/9X48HljMpnc+fC5Y9M+W492ZvsMK9ubHldvtGxrDbVs00D98H4CWg9BOYC2ZsekuwZG1B3PanpQ5ak7ntVdAyOhWwZoGmg2o/K4KdORxDS1pV25LKvJT/3T5Y1f5WAZDqvUvvB6tDNLG/5eJqqo4F8PtGxrDeaVHMKpVWoa8H4CWg9TaADa3rwuT6fNzWrHcHxKhicm6bS5Wc0rseQ4aKfOzehH+2z57SlfNjc860jvG0rkl1uWt3x+RhsGRk4EsJv22Hp6uLpl+J0xT9ed2/hVDrdvM1w+vr1Tbz1t6j7/erQzy61A8H8cqwmV6EuhZVvzZbK59869QwkdPna65u3u1rr+lNbU0MKyw/KUMthX3hHQeVZOK9U04P0EtJ6Q5X4AoLkKWdgnh+1pxYmysvTkcDj3uj5rtHzb0q4j4dlTbnojOSsxNaN8968SmqiqxZKnO5u0ymH/iNkP2V9i+Xg92pn9Ro/ZY7wyoKCDlm3NVbzdY/tLJ2nL3g7d+MMeXfZAr4bHqgvWejrMXp9ew+OapdVqGvB+AloPQTmmaZXlXYCJqO7NM90rbt46rPGqXXK/zzDgLeWBZxrfDq1WFxnstbd8ig3+5iyzXbynzA5mt++6/pQ6fF7/jqLCfqhOIwNQ020Spsc1S1Sv8zNZ15/ybY9ZXCgTQLgRlGOK4tn1Rw/YdZldB8IqqnvzopgpOXVuRibF0IqX3B8er/a6Y+meXc153RYaBsW9Hd60Cc96tHS+5PSUbJ8lw7bl6e39wdykn78orbjP+OKWpzcvCk8Lv6hqZACaMTxXTY9rlqhe52eyZnFaS322Ji2d+3KhTADhR1COE1pteRdgYsxwz93YRLgmpKKYKal2yX2pNmKm0k3aUr/q5LRMJhwOjMSmTXh++ifdvnvtPZ8JhvMXpWX7nA92LLigd9Me23fVxnjGikymMswaGYCa7kW3QnZ3aXydj9Ae7LTPPKDf1wGES8gumwhSqy3vAkwcM4xZj4cntpWUy5Qsn18+4pzcUiwMql1yb5qFLqVZVfOfPWw24VBctyCVtXR8wmyQ5QKGsAe9tz5hVgjva08E00e9lTSyCNjJvWYTZCcb1jholuMTZseNhOw6P5OHdtsa8qkXMnQkrk27uV8DooKgHCe02vIuwMSxlNmN6fGQZcpjlrR+9ahWLkhPy5gnYlNbioVFtUvuL16WknlDtck8nf2K5kxK7B9t/J/TcgHD102D3m3BBL3PvWRWcPC5Y+EpTBhVjdzaYjpBdkoNE2mN4BkOJyorAb/+RKfR6pqg3u8AKscUGk6gxQbakWmwbRq8N1Nft6cNAyPavMfW3UMJjactddm5Jevn19D2qFHW9af0yP7yvcZLLbm38v9Vfr9s6UCJaueNUP2+d3Plnn8t1d+bIWm4jWA8PAs7Iqva95mJS/KPXa7ffUfM09sCql0wk0OG789mvI/rIezvdwCVCywodxzno5LeLmm5pKSkn0r6qOu624MaU7uLYuEooFbRuAWbWcyS1i5Ja+2S8Eczaxandcf8jLa9OPOfnlJL7u817G9eynNNuiltRoatN/yF5Nva5L7g4xlLXXGv5r7g1aj2fVavxz4jZNtmJOlw0uw6cGicIBZAMIK8+rxZ0pck/b6k8yWlJW1yHGd+gGNqa1EsHAXUamGv2brGWvY1I6faJfejNWwdmGhSS7iRJmxvGC2zLzbsy4qL29zNxHRyOGzC1LmkkVtborhtRpLihuOxQzbumYT9/Q6gcoFlyl3Xfcvkjx3HuUzSUUmvl3RfIINqc42cXQfC6t1nJ/WRh+NlM7GWPL1nZbKJo2pd1Sy53z9S/Z2y5dOGq17ilqe019g7+nLP5F2vTuqjW/yKzXm6YkUw5/GchKexMf/j5nZGLyif3Lmk2OTOJRsGRpoWrPZ1e7r9D0f0xcc7df+uhJLprDrtmC5altJ15yRrKoAYtW0zknmLtnRETr8rVyb1YYO/W1fydwuIjDDtKZ+tXOb+cNADaVeFGfBrN/do56H4lP1oiZin5fMzoZwBB2pxwZK0btuW0Y5D5ZdjronA8vCoqHTJ/dEa9vN3NGk9WDwmqcFJqZ4yf7GfHDYrkPbki3H94WnNP5eNW2RF8O/Lpj22nvbpXPJ0vnNJs64jw2NW0d/ymEYz0rd2dOoXz9tav3pUfd3VR6B+7+GwLOUvMA22oxKUr12S1hn83QJaSpiC8psk/VLST8odNDg42JzRtIhqfl8fP116ZHiuvn9wgVKZmBLxrFb/xos6r++IDv1aOtSAcQJScO/vDy219dnkMj0z0q2sXr65jimj03rH9KGlQ9o1xM1NUCbSv131987vGGvYeTX5cWfFX63RdLfBd3maHHnGlZVleUp7/kH1r49OzPhc7v3Va2RSff3uX8W1bl7z32cvjb9Gkv9zPDqejdzf+bueXFa28JkkTWQt3fnLCS2eGGr4eLKedOMTZ2jw+MyZ+/c+ENNnz366IQHy0ZStzz61TM+MdE25nm7dG9NpvWO68awhzUk0+3p6jvGRUTn/3reoSx85cqaS2Zimvvc9dcayet+ip7VraDyo4dUsKq8DUC+hCModx/lHSW+Q9AbXdcvWaO3v72/OoFrA4OBg1b+v5ZIul/TyDeQr8v8BjVHL+VoP3z4ro817xkssx8woZp0a2LggZX9cfbp7yfxEQ86r4vP1VYMdOniwmkeylDFc9j6S7ZzxuXg/M8uUe1Y8kPdZ9qdm48sqmPHV4qXts4yOO+rNbspze2i3rT2jPWWP2TPaq/9MnFn3TGrWk/7svl4NjUy/vcwqrqGRWfrcrhX654uat5RfkrTV/NAonH9ZT/rkA71KZku9rywls3Hd+uszmrplop6Cvh8AKlGvCaTAg3LHcb4g6R2S/ovrus8EPR4A7SlKVczbzfyurA6OVd6/usPy9PYmtWbq7jBd9zr1DjlTwXrtckea7hG2A2oDnjWceKi2yn6QDoyajdn0uFrdM5go2w5NymXMvzOUqHtQ/tBuWzt8lvLvOBTXpt22Lji1edda090lUam9vmmPrZ0+v+edTd4yAaA2gV5/HMe5SdKfSTrfdd2ngxwLACCc+udWt1n7jL7mFaY8dU5G1XRSr0RHmXvwgdNSBj/f08WnBdM9I2ZYcC/W4N9hI6QMT7GUYa/2mWSy0oPP2rp6Y4+u/F6vrt7Yo4d229Pa8Y0bdhwYT9d/kuDWJzplso3ia0901v1nl2M6F2VapT1oU7S4+wAAGCxJREFUlUy8AIiGIPuU3yLpMkmXSDrsOM7J+S8dd133eFDjAgCEy9uWp/TIfltpwyxqEIUpnz3iV/m8Vp7WLZ05oF75ioy0w/9RzlpQY2RYpY6YNGbwo2upCh4U0+0HWZ8gqpzhMUvv39SjHcPxKasrfrzX1pl9Gd285uXCbaZt5brs+k+APHfMLPw1Pa5espaM5syi0kAsyIkXoBZhKwIZJkEuX78q/+/mos9/StInmzsUAEBYrVmc1pkLyrdrnNWR1Rl9WXUH1JrJ9Ca5Wt22p2vPmbm90W3bzTKUt2/vDKT6+sJZWb102D/ijmJf5UafZllPumpjT8lK2xlZ2j5s66qNPSf2aa/rT+mn+2xNlJks6LBy75N6M10NkGzy3JDpaxSVmCDIiRegWsNjlq7JTy5mJ73btu61ddaCjG6usStE1AXZpzwq1z4AQIBilvSp14/pnQ/0aixtqbjScLft6Rt/NKJl84IL6I7XJb4p3IxMf353DYyUzSLvecksxWx6XL2de3JaOw/791F/3cLo7X81jS+rjUMf2m3Wcq2wT/v8RWnZcU8TZbKkdtzTmxfV/3cdt8x6gjd7mXjxEv9ajwvauv7c6qFyS9gTscZMvADVyHrSex/s1eCR6deyrCxtf9HWex/s1b+uO962GfMILhQDALSTrCd9Ymu3xtLFrX8kydJYOqZPbO0O9oa6DjcRnTFPl5+V1LyurGZ3ZDWvK6srXp3U1kuP+U44TBhm6lMNzujPZPdRk+X9lnYdDagSXYh9/YlO3wJ4nix9bVtun/b3n7OV9lkqn85a+sFz9c/LdMTM3oSmx9VLh2FNA9PjgrZmcVrL55ef5lk+v3k1NQA/Dz5ra/BI+bBz8EhMG3cHXoM8MATlAIBQq6TScFBMg+Jy4jHphvOSevgdx7T1ncf08DuO6YPnJo32WZsGOYkmB0MFScP91MmAJg1qYXojVe0N177jZt+571juuLsHE0Z9078zWP8iYD2Gy6VnGXcrqI/uDsPjIlIXLWZJ61ePauWC9LT3dCLmaeWCdFNragB+bnq8SyYTs198rKsZwwml9p2OAABEQpAtnkwdGq/97ndeV/WByqLZWe30yUJI0qtOCmaJfyvvgT25N6t9I/4Z/mr3y5vu057IH7ffMIjfP1L/vIzfZECB6SRNvVy0LKUNT/nVXfB0SYSWe/d1e9owMKLNe2zdPZTQeNpSV0A1NQA/BwyvN424LkVF+z5zAEAkRKHScC0BdcHJvdU/xrtWJmXSEu0vVsxcLK6R1vWnfLP0Ud0D+4FzxmXyu88dVznTUL4Qu5tOEA3XYSKp2HzDIk19dXi/VOL6VUl1+0z4+BVTDKOYJa1dktYta0Z161tHdMuaUa1ZQkCO8DG9jkWv1Gf9EJQDAEItiCyraU/oglN6a7+V6KlhSe9Tw2Z7sU2Pq7dW3gO7dklanT6/1s64tLrK52baSs3LV1s3nSCa31n/wNj0fbCwyVX27Zh018CIuu2spk+geOq2s77FFAFUr9U6IDQClx8AQKg1O8s6PGbp8u/26mNberRlb4cePWBry94O3fjDHl32QK+Gx6bfNlxyeqqmIlG1jv+BXQmZ7Ne775lgNs228h7Y7z9nK+sTY2azqrqwmmkoP5H/tQYZGK/rT8lk1UAQy8SXzctq66XHqi6mCKB6J/cYXpcMj2tF7CkHAITamsVp3TG/fJ/yemVZs5507eaekj8rlbW07UVb127u0cdPLzHGvvJjLKfW8U+Y7jsOMBHdqntg7xlMlO0JLkkTXvNqHqzrT+lH+2wFsX/ay//UcmF5kC+znS+meMN50VqmDkTdaXMy2j9aqoPKZJ6Wzqm2eWT0EZQDAEKtkGW9dnOPdh6KTyn6loh5Wj4/U7cs66Y9Zj2hfzY8V8srGOPSubkbjV1HGjN+0xy9T+zYcIU9sGsDKshnIpPNnQf3DiU0nrHUFfe0rj+lNTNMHISt5kGQPbnvG0oYtW+7Z1dCF5wa3nMAQH09eqBDJqu5Hnm+Q9JYE0YUPgTlAIDQa1aW1bSd1ObnF+iyCscoqWHjn9PpacQgxplXYh9xpUFoKxses0pOrDyy39Yd+cmTvqJiZmGrLH/bdr8q45Jk6bbtnXrrafUNjMM2QQEgHFKGl79U+65eJygHAERDM7Kspu2kXkgmJE1M+7zfGBs1/lNmedo34n9ccYX3aoLQVmW6dWHDwMiUyYp1/Sk9st8u27avmZXlC/3K/ew1PNcrEbYJCgCICgq9AQCQd2DULIP3YjKYgmkz8Wv3dOK4SRXeJwehxQHl5CC0Ecucw2jTHls7fbYu7DwU1/f3TA3aw1ZZPshsdSu3vgPQeO28hoagHACAPL8q2ieOC1mguq7fv/p7hzU1GKo2CG1V9wwmyma7pdxkxXeGpk7INLqyfK/hhMus/IRL2vAcNj2uEmGboAAQLSH709pU7fGXFgAAI9Gcpz9/UVp23NNEmeynHff05kUvB0OVBKHNqBoetFoyzI2seWA6UZTJHxeTZPItjagX0MyijADQSgjKAQDIy3qFpk5+x4Urqvj+c7bSPgF2OmvpB8/ZJwJsinJN1Wm6H3qG4xpV88A0gC0sfTRNgDdqtUertr4DUD3jycJGDyTECMoBAMjLmC5fb+wwKnaPYdX4yVlvinJNdeqcjH6017+/d6G9XdOYBrGxyg5vZGwchdZ3AJqnI+Yp6fM3qnBcu2rnCQkAAKbw67Fc6XHNUk3Wu5p96K3smSNxmbQSGzxcfh9+vc0p0caulEK7u/a9pQUQVn5bpSo9rhURlAMAkJcwzB4nYuHKlVeT9T5/UVpxn6xEPDZ1H3orGzxidks0ZHhcvZwyy+y1LbS7M12Gb3ocANTKeBtO+8bkBOUAABQsnmMWbJ/SPd7gkVSmmlZUm/fYvhn28YwV+urrmaz04LO2rt7Yoyu/16urN/bood12xXumXxg1uyU6aHhcvVTa7m7RSWbnsOlxAFAr22dVVqXHtSKCcgAA8q5cmZT/AmBPb/vNA80YjrFqWlHduq1TJsu1c8eF0/CYpcu/26uPbenRlr0devSArS17O3TjD3t02QO9Gh4zT7uY3go2+5ax0gmX95xtdg7njgOAxjOdXOxpkxompRCUAwCQt3ZJWmf6BLdnzs/odxccadKIzFTTK3v/cbNbgH2GxzVb1pOu3dyjbS/a0/YhprKWtr1o69rNPaHrKV+pSidcTM/hdmhzByAcMoYdS0yPa0Xh/EsLAEAAYpb0pbWjWrEgPW0ZnW15WrEgrS+tDWef5UIrqr9/06je+FsTOvfktN74WxP6hzeNasPAiPq6Ix6dFtm0x9bOQ+WLru08FA/98ns/lU64RPkcBtCaTK83Vhtfl6L9lwoAgDrr6/Z0p0+f5UNBD3IGlbSiWtib1dGU/9z8wlnh3Ht8z2DCt1JvqqgNXLFMNhfc3zuUUFyeMgZV9U/uaf7vo9Le38Xn8OGXxjTvpG56hQMIRNT/3jQDQTkAAEXaoc/yFSuT+psf+rUB8/TuFeHce1xNG7jJhscsvX9Tj3YMx42C8RxPH1wVTJG/Ss/JyccPDg6qv7+/wSMEgNLefXZSH3k4XradqCVP71kZzr83zcDydQAA2lDUk6WmLb1KtYvLetJVG3u0fdiuICCX+udmdMGprTtRAwCNcMGStM7wqXVxRpvXuiAoBwCgDd1mWH3969vDWX391DkZmVQZXzp3+o3gQ7tt7fDZjz6ZJU+vnp/WV97CXmwAqNSJWhd9acWLrttxeVrRR60Llq8DANCG9o+YzcubVmlvtmeP+C29lyRLQ0emB9+3PmEyISF1x7NatTDDXmwAqFFft6c7LzSvjdFuCMoBAEDkJH2KvJ04rsTe8/88ZpYltyxLt6wZrWhcAIDS2qFeS7XCOf0NAAAayrTK7SkhrYZbaq94yePs6ccly29trPg4AABqQVAOAEAbunJlUpbPnmxLnq4MaTXcdf2paX27iyViuaWR0z5vuJ2803zbOQAAVSMoBwCgDa2NeDXcNYvTWu4z/uXzMzp/8fTxL5ptlgI3PQ4AgFoQlAMA0IZOVMNdkJZtTc0425anFQvCXQ03ZknrV49q5YL0tIx5IuZp5YK01q8uPf4rz07KpHL7e84O5yoBAEBrodAbAABtqq/b050D0a2G29ftaUMV479gSVq3bctox6GZb4PODPEqAQBAayEoBwCgjUW9Gm414y+sEnj/ph7tGI4rM6k9WlyezuzL6OY14V0lAABoLQTlAACg7dAzFwAQFgTlAACgLUV9lQAAoDVQ6A0AAAAAgIAQlAMAAAAAEBCCcgAAAAAAAkJQDgAAAABAQAjKAQAAAAAICNXXMaNUWvri45164JmE0lnJjkkXLk3p+lVJ2UznAAAAAEDNCMpR0tDhmN75QK/G0pakl5u1bniqU9/+VUJ3DYxo2bxscAMEAAAAgBZAvhPTpLPKB+QxTQ7IcyyNpXMBe5qYHAAAAABqQlCOab7wWGc+Qz6zsbSl9Y93NmlEAAAAANCaWL6Oae7fldD0DHkxS7c/2amMJ/aYAwAAAECVCKUwjfmydEsbnurU6785W0OHOZUAAAAAoFJEUpimsqw3e8wBAAAAoFoE5ZjmwqUpSV5F38MecwAAAACoHEE5prl+VVLddmVBuWTpnl2JhowHAAAAAFoVQTmmsWPSXQMj6razqiRjns40bkwAAAAA0IoIylHSsnlZbb30mC4/KynTwNyON3ZMAAAAANBqCMoxIzsm3XBeUpcZBeae1i1NNWNYAAAAANAyCMrhy2SPebft6dpzkk0aEQAAAAC0BoJy+Cq/x9xTt53VXQMjFbZSAwAAAAAQRsHI5D3m87qymt2R1byurK54dVJbLz2mZfNoUg4AAAAAlbKDHgCio7DH/IbzWKYOAAAAAPVAphwAAAAAgIAQlAMAAAAAEBCCcgAAAAAAAkJQDgAAAABAQAjKAQAAAAAICEE5AAAAAAABISgHAAAAACAgBOUAAAAAAASEoBwAAAAAgIAQlAMAAAAAEBDL87ygx+Dr6NGj4R8kAAAAAKBtzZkzx6rm+8iUAwAAAAAQEIJyAAAAAAACEonl6wAAAAAAtCIy5QAAAAAABISgHAAAAACAgNhBDwDVcxznKkkfkrRQ0pOSrnNdd4vB971B0g8kPe267oqGDhLIq+R8dRznzZL+X4kvnem67tMNGySQV+n11XGchKS/lXSZpFMkPS/p867rrm/CcNHmKry+3i7pXSW+NOq6bm/DBgnkVXF9vVTShyWdLuklSZsk3eC67oEmDBdtrorz9WpJ10haIuk5SX/nuu43/H4OmfKIchznTyXdJOmzkn5b0o8l/bvjOIt8vm+epG9I2tzwQQJ51Z6vkl6t3EWw8N9gI8cJSFWfr9+S9FZJ75W0XNJ/lfREg4cKVHO+fkBTr6sLJT0jyW38aNHuKj1fHcd5vaQNku5Q7p7gEklnSbqrKQNGW6vifP0rSf9T0v9Q7nz9hKRbHMe5yO9nkSmPrg9Kut113a/mP36/4zhvlfRXkj5a5vtuVe7CZkn6k8YOETih2vP1oOu6LzZ8dMBUFZ2vjuNcIGm1pKWTztfdzRgooArPV9d1j0o6Wvg4H/ScptwqD6DRKr0f+D1Jv3Zd9wv5j591HOdmSTc3fqhAxefrZZK+6rruP+c/fsZxnHMlfUTSfeV+EEF5BOWXSZ4j6fNFX3pI0u+X+b6rJL1S0mck/feGDRCYpNrzNe8xx3E6JT0l6TOu65Za0g7UTZXn6yWSHpX0QcdxLpc0JunfJd3ouu7xRo0VqPH6WvCXkp50XffH9RwbUKzK83WrpM/mM433S+qT9A5J323UOAGp6vO1U9J40efGJJ3nOE6H67oTM/08lq9H0wJJceX2LE72vKSTS32D4zgrlVtC8eeu62YaOzxgiorPV0n7lZuF/GNJb5e0U9Jmx3He2KhBAnnVnK+nSXqDpNcod85eo9xS9tsbM0TghGrO1xMcx5kjyZH0Vb9jgTqo+Hx1XfcnygXhd0lKSXpBudWepeoiAPVUzfX1QUnvdhznXMdxLMdxVkl6j6SO/OPNiEx5G8hnGv9FuaIYzwY9HsCP67o7lQvEC37iOM4S5Qpt+BYzBJosJsmTdGl+abAcx7lG0oOO47zSdd3iP+hAWPy5cufvhqAHApTiOM5Zyi1V/7RyAc9CSZ+T9H8kXR7g0IBSPq1cwP5j5SaPnldu2/CHJWXLfSNBeTS9KCmj3FL0yV4pqVQlyoWSzpR0m+M4t+U/F5NkOY6TlvRHrus+1KjBou1Ver7O5BHlZsuBRqrmfN0vaW8hIM/bkf93kabPsgP1Uuv19S8l/V/XdQ/Ve2BACdWcrx+V9DPXdT+X//gJx3FGJG1xHOdG13V/3ZihApWfr67rjimXKX9f/rj9yhWAPabcKo8ZsXw9glzXTUl6XNLaoi+tVW5mptheSSslvXbSf1+WNJT/f/aRoWGqOF9n8lrlLm5Aw1R5vm6VdIrjOLMmfe70/L976jtC4GW1XF8dxzlPuS0XLF1HU1R5vvYoFxhNVviYOAYNU8v11XXdCdd1f53fMvwOSfe7rkumvEX9o6QNjuP8TLkbwv+mXG/cL0uS4zjfkCTXdS/PFxXYPvmbHcc5KCnpuu6UzwMNYny+5j++Trnq1U9KSii3xPIS5fbrAo1W0fkq6ZvKFc+8zXGcT0qaq1wLlW+7rnuwuUNHG6r0fC14r6RB13V/0LyhAhWfr/dJ+mq+1VRh+foXJf3cdd3nmjx2tJ9K719Pl/Q6ST+VNE+56u0rZFADgRmmiHJd918kXSfpbyX9UrkiQ3/kum4hK7Mo/x8QuCrO14Rye8aeUG4P+RskDbiu+29NGzTaVqXna77C+hpJc5Srwu5KeljSu5s4bLSpau4HHMeZrVz25mtNHCpQzfX1duUCm2uUSzB9W9KvJK1r3qjRrqq4vsaVO1//Q9JGSV2Sft913d1+P8vyPK9+IwcAAAA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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "housing.plot(x='NOX', y='DIS', kind='scatter', \n", + " color='dodgerblue', figsize=(15,7), s=100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## OPTIONAL: Understanding Matplotlib (Figures, Subplots, and Axes)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib uses a blank canvas called a figure." + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.subplots(1,1, figsize=(16,8));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within this canvas, we can contain smaller objects called axes." + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2,3, figsize=(16,8));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas allows us to plot to a specified axes if we pass the object to the ax parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2,3, figsize=(16,8))\n", + "df.plot(ax=axes[0][0]);\n", + "df['col1'].plot(ax=axes[0][1]);\n", + "df['col2'].plot(ax=axes[1][1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's use a bit more customization.\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2,2, figsize=(16,8))\n", + "\n", + "# We can change the ticks' size.\n", + "df['col2'].plot(figsize=(16,4), color='purple', fontsize=21, ax=axes[0][0])\n", + "\n", + "# We can also change which ticks are visible.\n", + "# Let's show only the even ticks. ('idx % 2 == 0' only if 'idx' is even.)\n", + "ticks_to_show = [idx for idx, _ in enumerate(df['col2'].index) if idx % 2 == 0]\n", + "df['col2'].plot(figsize=(16,4), color='purple', xticks=ticks_to_show, fontsize=16, ax=axes[0][1])\n", + "\n", + "# We can change the label rotation.\n", + "df.plot(figsize=(15,7), title='Big Rotated Labels - Tiny Title',\\\n", + " fontsize=20, rot=-50, ax=axes[1][0])\\\n", + "\n", + "# We have to use \".set_title()\" to fix title size.\n", + "df.plot(figsize=(16,8), fontsize=20, rot=-50, ax=axes[1][1])\\\n", + " .set_title('Better-Sized Title', fontsize=21, y=1.01);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "## OPTIONAL: Additional Topics" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Saving a plot to a file\n", + "drinks.beer.plot(kind='hist', bins=20, title='Histogram of Beer Servings');\n", + "plt.xlabel('Beer Servings');\n", + "plt.ylabel('Frequency');\n", + "plt.savefig('beer_histogram.png'); # Save to file!" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['seaborn-dark',\n", + " 'seaborn-darkgrid',\n", + " 'seaborn-ticks',\n", + " 'fivethirtyeight',\n", + " 'seaborn-whitegrid',\n", + " 'classic',\n", + " '_classic_test',\n", + " 'fast',\n", + " 'seaborn-talk',\n", + " 'seaborn-dark-palette',\n", + " 'seaborn-bright',\n", + " 'seaborn-pastel',\n", + " 'grayscale',\n", + " 'seaborn-notebook',\n", + " 'ggplot',\n", + " 'seaborn-colorblind',\n", + " 'seaborn-muted',\n", + " 'seaborn',\n", + " 'Solarize_Light2',\n", + " 'seaborn-paper',\n", + " 'bmh',\n", + " 'tableau-colorblind10',\n", + " 'seaborn-white',\n", + " 'dark_background',\n", + " 'seaborn-poster',\n", + " 'seaborn-deep']" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# List available plot styles\n", + "plt.style.available" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [], + "source": [ + "# Change to a different style.\n", + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "### Summary\n", + "\n", + "In this lesson, we showed examples how to create a variety of plots using Pandas and Matplotlib. We also showed how to use each plot to effectively display data.\n", + "\n", + "Do not be concerned if you do not remember everything — this will come with practice! Although there are many plot styles, many similarities exist between how each plot is drawn. For example, they have most parameters in common, and the same Matplotlib functions are used to modify the plot area.\n", + "\n", + "We looked at:\n", + "- Line plots\n", + "- Bar plots\n", + "- Histograms\n", + "- Box plots\n", + "- Special seaborn plots\n", + "- How Matplotlib works" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Data Visualization and Pipelines/Data Visualization_and_Pipeline.ipynb b/Data Visualization and Pipelines/Data Visualization_and_Pipeline.ipynb index ad870d3..9417d0f 100644 --- a/Data Visualization and Pipelines/Data Visualization_and_Pipeline.ipynb +++ b/Data Visualization and Pipelines/Data Visualization_and_Pipeline.ipynb @@ -1505,9 +1505,7 @@ { "cell_type": "code", "execution_count": 197, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3620,9 +3618,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "\n", "## OPTIONAL: Additional Topics" @@ -3710,9 +3706,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "metadata": {}, "source": [ "\n", "### Summary\n", @@ -3754,9 +3748,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.8.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Intro to Python/.ipynb_checkpoints/Intro_to_Python-checkpoint.ipynb b/Intro to Python/.ipynb_checkpoints/Intro_to_Python-checkpoint.ipynb new file mode 100644 index 0000000..c5813ba --- /dev/null +++ b/Intro to Python/.ipynb_checkpoints/Intro_to_Python-checkpoint.ipynb @@ -0,0 +1,2841 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **Jupyter slideshow:** This notebook can be displayed as slides. To view it as a slideshow in your browser, run the following cell:\n", + "\n", + "> `> jupyter nbconvert [this_notebook.ipynb] --to slides --post serve`\n", + " \n", + "> To toggle off the slideshow cell formatting, click the `CellToolbar` button, then `View > Cell Toolbar > None`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "# ! jupyter nbconvert Intro_to_Python.ipynb --to slides --post serve" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Did you install Anaconda?\n", + "\n", + "- Install Anaconda (https://www.anaconda.com/distribution/)\n", + "\n", + "> Make sure to install the latest (python 3.7) version \n", + "\n", + "\n", + "\n", + "- We will go over the Python Programming language in next session" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "\n", + "\n", + "\n", + "# Intro to Python \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Why Python?\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "### Learning Objectives\n", + "*In this lesson, we will go over the following:*\n", + "\n", + "- Data types in Python.\n", + "- Functions in Python.\n", + "- Control flows\n", + "- Examples Programs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Lesson Guide\n", + "\n", + "- [Into to jupyter (10–15 min)](#survey1)\n", + "- [Common Python Types (15 min)](#types-def)\n", + "- [Common Types Code-Along (20 min)](#types-codealong)\n", + "- [Python Control Flows (20 min)](#functions-def)\n", + "- [Common Python Functions Code-Along (20 min)](#functions-codealong)\n", + "- [Recap and Requests (5–10 min)](#recap-requests)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "## Common Python Types\n", + "\n", + "---\n", + "\n", + "**In Python, what do you understand a \"type\" to be?**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "### What is a type?\n", + "\n", + "> There are several _standard_ data types within Python (and typically any programming language, although it varies as to what is or isn't in a type for each). \n", + "\n", + "> A type tells the computer what can or can't be done with a given piece of information in Python (or whichever programming language you are using). For example, we can **add** and **subtract** numbers and we can **count** the number of letters in a word. Rather than try (and fail!) to add two words together or count the number of numbers in a number, types let the computer know efficiently what can and cannot be done in a given situation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "### Types in Python\n", + "\n", + "\n", + "There are two basic groups of types in Python — single elements (one item or piece of information) and collections (groups of things, which can be either single elements themselves, other collections, or a mix of both).\n", + "\n", + "**Single Elements**\n", + "\n", + "- **Integers:** Whole numbers ranging from negative infinity to infinity, such as 1, 0, -5, etc.\n", + "- **Floats:** Short for \"floating point number;\" usually used with decimals, such as 2.8 or 3.14159.\n", + "- **Strings:** A set of letters, numbers, or other characters, e.g., \"The fox is quick.\" — any set of values that contains a non-numeric character.\n", + "\n", + "**Collections**\n", + "\n", + "- **Tuples:** An ordered sequence with a fixed number of elements; e.g., in `x = (1, 2, 3)`, the parentheses makes it a tuple. `x = (\"Kirk\", \"Picard\", \"Spock\")` — once you've defined this, you can't change it.\n", + "- **Lists:** An ordered sequence without a fixed number of elements, e.g., `x = [1, 2, 3]`. Note the square brackets. `x = [\"Lord\", \"of\", \"the\", \"Rings\"]` — this can be changed as you like.\n", + "- **Dictionaries**: An unordered collection of key-value pairs, e.g., `x = {'Mark': 'Twain', 'Apples': 5}`. To retrieve each value (the part after each colon), use its key (the part before each colon). For example, `x['Apples']` retrieves the value `5`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**In your own words, what is the difference between a list and a dictionary? Can you think of any real-life examples of lists or dictionaries?**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "### list vs. tuple vs. dictionary \n", + "\n", + "Typically, lists and dictionaries are most popular because of their ease of use and applicability to numerous situations (by contrast, because tuples are immutable/cannot be modified; they are used more sparingly). Lists have an inherent order (the first element is at index `0`, the second element is at index `1`, etc.) and we can call each element by that ordinal number (such as `x[0]` or `x[100]`). Dictionaries do not have an order (so `x[0]` will fail), but they use the name of the key to return that element.\n", + "\n", + "**Example**: Think about the difference between a sign-up list and an address book:\n", + "- **Sign-up list**: We would refer to the order of who signed up when (i.e., \"the eighth person to sign up\").\n", + "- **Address book**: We would refer to a person by looking for their name (i.e., \"the contact info for Bill Personson\")." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Variables\n", + "\n", + "Variables are names that have been assigned to specific values or data. These names can be almost anything you want, but there are some restrictions and best practices.\n", + "\n", + "\n", + "#### Best Practices\n", + "- Usually people use _ instead of spaces in a variable name\n", + "\n", + "my_number = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + ">**Instructor Note:** List their rules on the whiteboard and then bring up some of the concepts below.\n", + "\n", + "#### Restrictions\n", + "\n", + "- Variable names cannot be just a number (i.e., 2, 0.01, 10000).\n", + "- Variables cannot be assigned the same name as a default or imported function (i.e., 'type', 'print', 'for').\n", + "- Variable names cannot contain spaces.\n", + "\n", + "#### Best Practices\n", + "\n", + "- Variable names should be lowercase.\n", + "- A variable's name should be representative of the value(s) it has been assigned. For example, instead of `a` or `money`, something like `money_usd` tells the user both what is stored in that variable *and* what units it's in." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "## Common Types (Code-Along)\n", + "\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "In this section, we'll practice establishing some types and common practices. To run each cell, use `shift+enter` or the `run` button in the Jupyter Notebook toolbar.\n", + "\n", + "Some common reasons why we need to keep types in mind:\n", + "\n", + "1) Different types can lead to different results:
\n", + " - 1 + 1 results in 2, while '1' + '1' results in the string 11.\n", + "2) Operations may not work with specific types:
\n", + " - len('a word') will return the number of characters in a word, while `len(25)` will return an error because numbers do not have a length." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Type The above question answer here \n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "float" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assigning a float:\n", + "x = 1.0\n", + "type(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assigning an int:\n", + "y = 1\n", + "type(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assigning a string:\n", + "z = '1'\n", + "type(z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Remember that, when we're assigning variables, we are not stating that \"x equals 1,\" we are stating that \"x has been assigned the value of 1.\"**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operators\n", + "\n", + "Operators can be used in a mathematical sense to calculate (or create) the sum, difference, product, or quotient of values or variables.\n", + "\n", + "Note that `print()` below will print out the values of whatever is inside of the parentheses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "-1\n", + "2\n", + "0.5\n" + ] + } + ], + "source": [ + "# Addition:\n", + "print(1 + 2)\n", + "# Subtraction:\n", + "print(1 - 2)\n", + "# Multiplication:\n", + "print(1 * 2)\n", + "# Division:\n", + "print(1 / 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " There is also `//` division, whose output will be the rounded-down whole number." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "-2.0\n" + ] + } + ], + "source": [ + "# Division of float numbers:\n", + "print(3.0 // 2)\n", + "print(-3.0 // 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exponent power operator:\n", + "2 ** 3" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The modulo operator can be used to get the remainder — what's left over after the term has been cleanly divided:\n", + "# 7= 3*2 + 1 \n", + "7%2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Booleans and Boolean Evaluation Operators" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + ">**Instructor Note:** Guide students to the following definition:\n", + "\n", + "Booleans exist as either true or false and are generally used as a means of evaluation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Using Booleans\n", + "\n", + "Booleans are frequently used to filter data or conditions. Sometimes, we may want all countries with populations greater than 4,000,000 or all people named Bob. Both of these result in a `True` or `False` condition that split our data into the groups we want.\n", + "\n", + "In Python, there are several built-in commands for deciding how to filter results:\n", + "\n", + "- `and`: Are both A and B true?\n", + "- `not`: Is A the same as B?\n", + "- `or`: Is A or B true?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "True and False" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "not False" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "True or False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Comparisons**\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(True, False, False, False, True, True)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 > 1, 2 < 1, 2 > 2, 2 < 2, 2 >= 2, 2 <= 2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Equality:\n", + "[1,2] == [1,2]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[1,2] == [1,2] or [2,2] == [2,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hands_on Challenge" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now You Try!\n", + "\n", + "\n", + "\n", + "With a partner, create three comparisons using `!=`, `>=`, and `<`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "## Type your answer here\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Strings\n", + "\n", + "**What are strings? How would we use them? Can you think of any examples of strings?**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + ">**Instructor Note:** Guide your students to the following definition:\n", + "\n", + "Strings are essentially any character combination in between quotes. They are most often used as a way of storing text. Strings are used frequently, because most of the data that humans create are text-based, such as restaurant reviews or emails." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = \"Hello world\"\n", + "type(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ss='Hi World!'\n", + "type(ss)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Strings have a lot of associated methods and attributes that allow us to better understand and manipulate them.\n", + "\n", + "**In your own words, why would we want to manipulate or change strings?**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + ">**Instructor Note:** Some examples include:\n", + "- Fixing misspelled words.\n", + "- Changing casing (upper, lower).\n", + "- Looking for specific words." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world\n" + ] + }, + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Finding the length of the string:\n", + "print(s)\n", + "len(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello test\n" + ] + } + ], + "source": [ + "# Replacing an element of a string:\n", + "s2 = s.replace(\"world\", \"test\")\n", + "print(s2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### String Indexing\n", + "\n", + "In some cases, we may want a part of the string (like the first character for alphabetizing or categorizing). Indexing helps us do that.\n", + "\n", + "We can extract characters at specific index locations in a string using indexing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### In Python we use [ ] for Indexing (for lists, tuples, disctionaries, ...)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Mon'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_string='Monty Python'\n", + "\n", + "test_string[0:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world\n" + ] + }, + { + "data": { + "text/plain": [ + "'H'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Indexing the first (index 0) character in the string:\n", + "print(s)\n", + "s[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "The number you enter after the variable name in brackets (the `[0]`) is called the index (its plural is indices).\n", + "\n", + "_Counting in Python and many other programming languages begins at zero, as opposed to one. This is called zero-based indexing._" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hel'" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This is called \"splicing.\" We start at the left index \n", + "# and go up to but not include the right index.\n", + "\n", + "# Objects at indexes 0, 1, and 2:\n", + "s[0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Indexing\n", + "\n", + "[start_index : end_index : step_size]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Most ranges, or functions with ranges, have upper ends that are not inclusive. So, a range of `[0:5]` starts at `0` and stops before `5`.\n", + "\n", + "A good mental trick is to look at something like `[5:25]` and say out loud \"Starting at five and going up to (but not including) 25.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world\n" + ] + }, + { + "data": { + "text/plain": [ + "'world'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# From index 6 up to the end of the string:\n", + "print(s)\n", + "s[6:]" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world'" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# No start or end specified:\n", + "s[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'d'" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Can we index from the right side?\n", + "s[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "In addition to specifying a range, you can include a step size or character skip rate. This might be helpful if you want every other letter, for example. \n", + "\n", + "These indexing methods can also be used on lists, where asking for every other number might be a good use case." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world\n" + ] + }, + { + "data": { + "text/plain": [ + "'Hlowr'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Every second character starting at 0 and ending at 10:\n", + "print(s)\n", + "s[0:10:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hlowrd'" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define a step size of 2; i.e., every other character:\n", + "s[::2]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 2, 4, 6]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The same, but for a list of numbers:\n", + "[0, 1, 2, 3, 4, 5, 6][::2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Concatenating\n", + "\n", + "**In your own words, what is concatenating? When might you use it?**\n", + "\n", + "To add two strings together, type the first string, a `+` sign, and then the second string." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "> **Instructor Note:** One easy example is adding a greeting to someone's name: `'Hello ' + name`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = 'Hello '\n", + "y = 'world'\n", + "\n", + "x + y" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You rolled a 3.\n" + ] + } + ], + "source": [ + "# Conversion from int to str is required!\n", + "\n", + "dice_roll = 3\n", + "\n", + "#print('You rolled a ' + dice_roll + '.') \n", + "print('You rolled a ' + str(dice_roll) + '.') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now You Try!\n", + "\n", + "\n", + "Create your own string of at least 12 characters or more and:\n", + "1) Test to make sure that it is at least 12 characters long.
\n", + "2) Replace all of the vowels with the string `vowel`.
\n", + "3) Use concatenation to add another string to it.
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Type your answer here\n", + "\n", + "my_string= 'This is the first time Im writing python code'\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Lists\n", + "\n", + "**What are some examples of lists? What do you remember from before?**\n", + "\n", + "Lists can be composed of ints, floats, strings, or other lists, as well as other data types we haven't covered yet." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[1, 2, 3, 4]\n" + ] + } + ], + "source": [ + "m = [1, 2, 3, 4]\n", + "\n", + "print(type(m))\n", + "print(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# The a variable's contents can be reassigned to another variable:\n", + "a = m" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4]\n" + ] + } + ], + "source": [ + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Carol', 'Anne', 'Jessica']\n" + ] + } + ], + "source": [ + "# List of strings:\n", + "names = ['Carol', 'Anne', 'Jessica']\n", + "print(names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Methods\n", + "\n", + "Many types have what are known as \"methods:\" built-in functionality that allows them to do certain things. We've already seen a couple, such as the `.replace()` method, which lets you replace words in strings. \n", + "\n", + "Lists also have several methods that allow us to alter them, such as the `.append()` method, which allows us to add another element to the end of a list." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Carol', 'Anne', 'Jessica', 'Michelle']" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names.append('Michelle')\n", + "names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lists can indexed the same way strings — this allows us to target a specific value or range of values in a list without having to create a new one." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Anne', 'Jessica']\n", + "['Carol', 'Jessica']\n" + ] + } + ], + "source": [ + "print(names[1:3])\n", + "print(names[::2]) # Increments the index by 2 each time (skips alternate elements)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Carol', 'Anne', 'Jessica', 'Michelle']" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Anne'" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'nne'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can slice a value in a list as well:\n", + "names[1][1:]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Note that we always read indexing from left to right. In the example above, the interpreter looks up names and gets the first element, which is the string `\"Anne\"`. Then, the slice (`[1:]`) adds the first index of that string to the end of the original string, evaluating to `\"nne\"`.\n", + "\n", + "Interestingly, the following works in the same way. Instead of having to look up the value of names, the list is directly specified (just read the line from left to right!)." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'nne'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "['Carol', 'Anne', 'Jessica', 'Michelle'][1][1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 'a', 1.0, (1-1j)]\n" + ] + } + ], + "source": [ + "# Lists don't have to be the same type:\n", + "l = [1, 'a', 1.0, 1-1j]\n", + "print(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can create a list of values in a range using the range() function:\n", + "start = 10\n", + "stop = 30\n", + "step = 2\n", + "print(type(range(start, stop, step)))\n", + "\n", + "# range() produces a \"generator,\" which is beyond the scope of this introduction!\n", + "# It is often convenient to have the generator \n", + "# generate all of its values by converting it to a list:\n", + "list(range(start, stop, step))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 3, 5, 7, 9]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(range(1,10,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Use the `.insert()` method to add values at specific indices." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Carol', 'Anne', 'Jessica', 'Michelle']" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Carol', 'Anne', 'Ellen', 'Jessica', 'Michelle']" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names.insert(2, 'Ellen')\n", + "names" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "The `.remove()` method can be used to remove specific values if they appear in a list." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Carol', 'Anne', 'Ellen', 'Jessica', 'Michelle', 'Jeremy']\n", + "['Carol', 'Anne', 'Ellen', 'Jessica', 'Michelle']\n" + ] + } + ], + "source": [ + "names.append('Jeremy')\n", + "print(names)\n", + "names.remove('Jeremy')\n", + "print(names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now You Try!\n", + "\n", + "\n", + "\n", + "\n", + "Create a list of five elements and do the following:\n", + "\n", + "1) Print the last three elements.
\n", + "2) Insert two new elements at index 2 and append one element to the end.
\n", + "3) Remove one element of your choice.
\n", + "4) Print every other element in your list.
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 3, 4, 'hello', [4, 5, 7]]\n" + ] + } + ], + "source": [ + "##### Type your answer here\n", + "mn=[1,3,4,'hello',[4,5,7]]\n", + "mn[2:]\n", + "mn.insert(2,'insert here')\n", + "del mn[2]\n", + "print(mn)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Tuples\n", + "\n", + "Tuples are similar to lists in that they store a sequence of various separate values. However, tuples are not mutable in that, once they are created, their values cannot be changed.\n", + "\n", + "**In your own words, why would creating something that cannot be changed later be helpful?**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + ">**Instructor Note:** Guide students to think about safety — what if you absolutely needed to make sure that one or two numbers or values stayed the same?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 20)\n", + "\n" + ] + } + ], + "source": [ + "point = (10, 20)\n", + "print(point)\n", + "print(type(point))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# They can be sliced, just like lists and strings:\n", + "point[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m23\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + ] + } + ], + "source": [ + "point[0]=23" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Dictionaries\n", + "\n", + "Dictionaries are a non-ordered Python data type. Instead of using an ordered index to access data stored in a dictionary, we use a system of key-value pairs.\n", + "\n", + "**In your own words, why would we use this when we could just use a list?**\n", + "\n", + "- A key is similar to a variable name.\n", + "- A value is similar to the value assigned to the variable.\n", + "- Curly braces (`{ }`) enclose dictionaries. The first input in a dictionary pair is the \"key.\" The second input in a dictionary pair is the \"value.\" Remember to make `key:value` pairs!\n", + "\n", + "The general format looks like this:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + ">**Instructor Note:** Some answers might include:\n", + "- When there are lots of elements and we don't want to remember which element number is which.\n", + "- When we don't care about the order in which things happen.\n", + " - As opposed to a list, which is kind of like a queue." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "{'key1': 1.0, 'key2': 2.0, 'key3': 3.0}\n" + ] + } + ], + "source": [ + "params = {'key1' : 1.0,\n", + " 'key2' : 2.0,\n", + " 'key3' : 3.0,}\n", + "\n", + "print(type(params))\n", + "print(params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The keys stay the same, but the values are changeable. You can also only have one occurrence of a key in a dictionary, but you can have all of the values be the same." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Value for parameter2 in the params dictionary:\n", + "params['key2']" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding a new dictionary entry:\n", + "params['key4'] = 'D'" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'key1': 1.0, 'key2': 2.0, 'key3': 3.0, 'key4': 'D'}\n" + ] + } + ], + "source": [ + "print(params)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Reassigning the value of a key-value pair in the dictionary:\n", + "params['key1'] = 'A'\n", + "params['key2'] = 'B'" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'key1': 'A', 'key2': 'B', 'key3': 3.0, 'key4': 'D'}\n" + ] + } + ], + "source": [ + "print(params)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Key 1 = A\n", + "Key 2 = B\n", + "Key 3 = 3.0\n", + "Key 4 = D\n" + ] + } + ], + "source": [ + "print('Key 1 = ' + str(params['key1']))\n", + "print('Key 2 = ' + str(params['key2']))\n", + "print('Key 3 = ' + str(params['key3']))\n", + "print('Key 4 = ' + str(params['key4']))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_items([('key1', 'A'), ('key2', 'B'), ('key3', 3.0), ('key4', 'D')])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params.items()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('key1', 'A'), ('key2', 'B'), ('key3', 3.0), ('key4', 'D')]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Dictionaries also have methods.\n", + "\n", + "# Convert a dictionary to a list of tuples (key-value pairs).\n", + "# This is later used to conveniently loop through a dictionary:\n", + "list(params.items())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['key1', 'key2', 'key3', 'key4'])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_values(['A', 'B', 3.0, 'D'])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params.values()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "## Practice: Types\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "_Identify the variable types of the following five items:_\n", + "\n", + "- `1`\n", + "- `-1.0`\n", + "- `1,000,000`\n", + "- `'10'`\n", + "- `('twenty-four')`\n", + "\n", + "\n", + "### Challenge \n", + "_Create a list of all numbers between `1` and `100`, inclusive, using the `range()` function discussed above. Can you slice the list so that we see every fifth number, starting at `4` and ending at `82`?_" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "### Type your answer here\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "## Common Python Functions and Control Flow\n", + "---\n", + "\n", + "\n", + "\n", + "\n", + "---\n", + "In this section, we're going to tackle some common design patterns in Python. The first is the concept of control flow — this is how our programs will return different results based on specific input. Second, we'll cover basic functions — these let us create snippets of code that we can call later in a script, which creates code that's easier to read and maintain. Remember, we're going to be reading code much more often than writing it!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## `if… else` Statements\n", + "\n", + "In Python, indentation matters! This is especially true when we look at the control structures in this lesson. In each case, a block of indented code is only run some of the time. There will always be a condition in the line preceding the indented block that determines whether the indented code is run or skipped.\n", + "\n", + "### `if` Statement\n", + "The simplest example of a control structure is the `if` statement. We start with `if`, followed by something that can evaluate to `True` or `False` (such as any of the comparison operators we discussed earlier)." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The integer 1 is equal to the integer 1.\n", + "Is the next indented line run, too?\n" + ] + } + ], + "source": [ + "if 1 == 1:\n", + " print('The integer 1 is equal to the integer 1.')\n", + " print('Is the next indented line run, too?')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "if 'one' == 'two':\n", + " print(\"The string 'one' is equal to the string 'two'.\")\n", + "\n", + "print('---')\n", + "print('These two lines are not indented, so they are always run next.')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Notice that, in Python, the line before every indented block must end with a colon (`:`). In fact, it turns out that the `if` statement has a very specific syntax:\n", + "\n", + "```\n", + "if :\n", + " \n", + "```\n", + "\n", + "When the `if` statement is run, the expression is evaluated to `True` or `False` by applying the built-in `bool()` function. If the expression evaluates to `True`, the code block is run; otherwise, it is skipped." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now You Try!\n", + "\n", + "\n", + "\n", + "Create your own string called `test_string`, then fill in the blanks here to create an `if... else` statement for whether or not the first character in `test_string` is a lowercase `a`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#### Type your answer here\n", + "test_string='dapple is a company' \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `if` ... `else`\n", + "\n", + "In many cases, you may want to run some code if the expression evaluates to `True` and some other code if it evaluates to `False`. This is done using `else`. Note how it is at the same indentation level as the `if` statement, followed by a colon, followed by a code block. Let's see it in action." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50 >= 30.\n", + "The else code block was run instead of the first block.\n", + "---\n", + "These two lines are not indented, so they are always run next.\n" + ] + } + ], + "source": [ + "if 50 < 30:\n", + " print(\"50 < 30.\")\n", + "else:\n", + " print(\"50 >= 30.\")\n", + " print(\"The else code block was run instead of the first block.\")\n", + "\n", + "print('---')\n", + "print('These two lines are not indented, so they are always run next.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `if` ... `elif` ... `else`\n", + "\n", + "Sometimes, you might want to run one specific code block out of several. For example, perhaps we provide the user with three choices and want something different to happen with each one.\n", + "\n", + "`elif` stands for `else if`. It belongs on a line between the initial `if` statement and an (optional) `else`. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your health is average.\n", + "Exercise and eat healthily!\n", + "---\n", + "These two lines are not indented, so they are always run next.\n" + ] + } + ], + "source": [ + "health = 55\n", + "\n", + "if health > 70 :\n", + " print('You are in great health!')\n", + "elif health > 40:\n", + " print('Your health is average.') \n", + " print('Exercise and eat healthily!')\n", + "elif health > 20:\n", + " print('Your health is ok.')\n", + " print('Dont eat meat!')\n", + "else:\n", + " print('Your health is low.')\n", + " print('Please see a doctor now.')\n", + "\n", + "print('---')\n", + "print('These two lines are not indented, so they are always run next.')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "This code works by evaluating each condition in order. If a condition evaluates to `True`, the rest are skipped.\n", + "\n", + "**Let's walk through the code.** First, we let `health = 55`. We move to the next line at the same indentation level — the `if`. We evaluate `health > 70` to be `False`, so its code block is skipped. Next, the interpreter moves to the next line at the same outer indentation level, which happens to be the `elif`. It evaluates its expression, `health > 40`, to be `True`, so its code block is run. Now, because a code block was run, the rest of the `if` statement is skipped." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## `for` Loops\n", + "---\n", + "\n", + "\n", + "\n", + "\n", + "One of the primary purposes of using a programming language is to automate repetitive tasks. One example is the `for` loop.\n", + "\n", + "The `for` loop allows you to perform a task repeatedly on every element within an object, such as every name in a list.\n", + "\n", + "\n", + "Let's see how the pseudocode works:\n", + "\n", + "```python\n", + "# For each individual object in the list\n", + " # perform task_A on said object.\n", + " # Once task_A has been completed, move to next object in the list.\n", + "```\n", + "\n", + "Let's say we wanted to print each of the names in the list, as well as \"is Awesome!\" In this case, we'd create a temporary variable for each element in the collection (`for name in names` would put each name, in sequence, under the temporary variable `name`) and then do something with it." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rebecca Bunch Is Awesome!\n", + "Paula Proctor Is Awesome!\n", + "Heather Davis Is Awesome!\n" + ] + } + ], + "source": [ + "names = ['Rebecca Bunch', 'Paula Proctor', 'Heather Davis']\n", + "\n", + "for item in names:\n", + " print(item + ' Is Awesome!')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "We can also combine `if... else` statements and `for` loops:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Rebecca Bunch', 'Paula Proctor', 'Heather Davis']" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rebecca Bunch Is Awesome!\n", + "Paula Proctor Is REALLY AWESOME!\n", + "Heather Davis Is Awesome!\n" + ] + } + ], + "source": [ + "for item in names:\n", + " if item == 'Paula Proctor':\n", + " print(item + ' Is REALLY AWESOME!')\n", + " else:\n", + " print(item + ' Is Awesome!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now You Try!\n", + "\n", + "Create a new `if... elif... else` and `for` loop combination, using a list of your own choice. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "### Type your answer here\n", + "\n", + "nums=[1,23,4,6,7,76, 77,35]\n", + "# only print the even numbers\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Functions\n", + "---\n", + "\n", + "**When would you want to call the same code over and over again? What benefit does that have in programming?**\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + ">**Instructor Note:** Guide students to think about how using functions can make programming easier and ensure that we will only need to make changes/define options once in a script instead of multiple times." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Similar to the way we can use `for` loops as a means of performing repetitive tasks on a series of objects, we can also create functions to perform repetitive tasks. Within a function, we can write a large block of action and then call the function whenever we want to use it. \n", + "\n", + "\n", + "Let's write some pseudocode, which is code that Python will not run successfully, but illustrates the basic idea without worrying about correct syntax:\n", + "```python\n", + "# Define the function name and the requirements it needs.\n", + " # Perform actions.\n", + " # Optional: Return output.\n", + "```\n", + "\n", + "A function is defined like this:\n", + "\n", + "```python\n", + "def function_name(arguments):\n", + " # Do things here.\n", + " return value\n", + "```\n", + "\n", + "We start with `def` and the name of our function, then a set of parentheses. The terms we put in the parentheses will be passed into the function and stored in those variables. Finally, if we want to store the results of the function, we use `return`, which will let us take some value and store it once the function has run, like this:\n", + "\n", + "```python\n", + "x = function_name(20)\n", + "```\n", + "\n", + "Whatever follows `return` when the function is defined will be passed out of the function and stored in `x`.\n", + "\n", + "Let's create a function that takes two numbers as arguments and returns their sum, difference, and product. " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n", + "-2\n", + "15\n" + ] + } + ], + "source": [ + "def arithmetic(num1, num2):\n", + " print(num1 + num2)\n", + " print(num1 - num2)\n", + " print(num1 * num2)\n", + "\n", + " \n", + "arithmetic(3,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Common Functions Code-Along\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "In this section, we'll run through some basic functions and how we might use them." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + ">**Instructor Note:** In this section, we'll run through some basic function generation so students can see the process of how to create and use functions. It can be helpful to walk through the code and ask students what each line does. A good suggestion would be to delete the latter sections of code and create them for your students in person so that they can see your thought process. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write a function that takes the length of a side of a square as an argument and returns its area." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16\n" + ] + } + ], + "source": [ + "def area_square(length):\n", + " return length**2\n", + "\n", + "print(area_square(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write a function that takes the height and width of a triangle and returns its area." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.0\n" + ] + } + ], + "source": [ + "def area_triangle(height, width):\n", + " return height * (0.5*width)\n", + "\n", + "print(area_triangle(2, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write a function that takes a string as an argument and returns a tuple consisting of two elements:\n", + "\n", + "- A list of all of the characters in the string.\n", + "- A count of the number of characters in the string." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(['L', 'i', 's', 'a', ' ', 'S', 'i', 'm', 'p', 's', 'o', 'n'], 12)\n" + ] + } + ], + "source": [ + "def list_and_count(word):\n", + " list_of_characters = []\n", + " for char in word:\n", + " list_of_characters.append(char)\n", + " return list_of_characters, len(word)\n", + "\n", + "print(list_and_count('Lisa Simpson'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now You Try!\n", + "\n", + "Write a function that takes two integers, passed as strings, and returns the sum, difference, and product as a tuple (with all values as integers)." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "#### Type your answer here\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(120, -80, 2000)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def integerify(string1, string2):\n", + " int1 = int(string1)\n", + " int2 = int(string2)\n", + " val_sum = int1 + int2\n", + " val_diff = int1 - int2\n", + " val_prod = int1 * int2\n", + " return val_sum, val_diff, val_prod\n", + "\n", + "integerify('20', '100')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write a function that takes a list as the argument and returns a tuple consisting of two elements:\n", + "\n", + "- A list with the items in reverse order.\n", + "- A list of the items in the original list that have an odd index." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "> **Instructor Note:** This function is a good example for introducing a couple of more advanced topics and could be written together with students versus walking them through it as it appears." + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Rebecca Bunch', 'Paula Proctor', 'Heather Davis']" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(['Heather Davis', 'Paula Proctor', 'Rebecca Bunch'], ['Paula Proctor'])" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def reverse_and_odd(input_list):\n", + " reversed_list = input_list[::-1]\n", + " odd_indices = []\n", + " for i in range(len(input_list)):\n", + " if i % 2 == 1:\n", + " odd_indices.append(input_list[i])\n", + " return reversed_list, odd_indices\n", + "\n", + "reverse_and_odd(names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_Write a function that calculates the factorial of an input number_\n", + "\n", + "4 ! = 4 * 3 * 2 * 1 ==> 24\n", + "\n", + "5 ! = 5 * 4 * 3 * 2 * 1 ==> 120\n", + "\n", + "1 != 1\n", + "\n", + "\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def factorial (input):\n", + " # add your code here\n", + " output=1 \n", + " \n", + " return output\n", + " \n", + " \n", + "factorial (4) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Different Approaches to Solving this problem," + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "24" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def factorial (input):\n", + " if input == 1:\n", + " return(1)\n", + " else:\n", + " return(input * factorial (input-1))\n", + " \n", + " \n", + "factorial (4) " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "24" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Write your code here\n", + "def factorial (input_number):\n", + " result=1\n", + " for item in range(1,input_number+1):\n", + " result=result*item\n", + " return(result)\n", + "\n", + "factorial(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24\n" + ] + } + ], + "source": [ + "def factor(x):\n", + " fact = x\n", + " while x > 1:\n", + " fact = fact * (x-1)\n", + " x = x-1\n", + " return fact\n", + "print(factor(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Take Home Practice: Functions\n", + "\n", + "\n", + "\n", + "---\n", + "\n", + "Can you tackle these three challenges on your own?\n", + "\n", + "_Write a function that takes a word as an argument and returns the number of vowels in the word._\n", + "\n", + "_Write a function that takes in a list of animals. Have it print out each animal's name in FULL CAPITAL LETTERS._ \n", + "\n", + "- **Note:** You may need to do some outside research to find out how Python can capitalize all letters in a string! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next : we will learn how to manipulate Data in Python " + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Intro to Python/Intro_to_Python.ipynb b/Intro to Python/Intro_to_Python.ipynb index c5813ba..68c7c6b 100644 --- a/Intro to Python/Intro_to_Python.ipynb +++ b/Intro to Python/Intro_to_Python.ipynb @@ -2,7 +2,11 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "source": [ "> **Jupyter slideshow:** This notebook can be displayed as slides. To view it as a slideshow in your browser, run the following cell:\n", "\n", @@ -11,6 +15,22 @@ "> To toggle off the slideshow cell formatting, click the `CellToolbar` button, then `View > Cell Toolbar > None`." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Test above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 1, @@ -2833,9 +2853,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.8.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Intro to Stats/.ipynb_checkpoints/Intro_to_Stats-checkpoint.ipynb b/Intro to Stats/.ipynb_checkpoints/Intro_to_Stats-checkpoint.ipynb new file mode 100644 index 0000000..1253a96 --- /dev/null +++ b/Intro to Stats/.ipynb_checkpoints/Intro_to_Stats-checkpoint.ipynb @@ -0,0 +1,3452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **Jupyter slideshow:** This notebook can be displayed as slides. To view it as a slideshow in your browser, run the following cell:\n", + "\n", + "> `> jupyter nbconvert [this_notebook.ipynb] --to slides --post serve`\n", + " \n", + "> To toggle off the slideshow cell formatting, click the `CellToolbar` button, then `View > Cell Toolbar > None`." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# ! jupyter nbconvert this_notebook.ipynb --to slides --post serve" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "# Intro to Statistics\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Learning Objectives\n", + "- Compute dot products, matrix multiplications, and vector norms by hand and using NumPy.\n", + "- Code summary statistics using NumPy and Pandas: mean, median, mode, max, min, quartile, inter-quartile range, variance, standard deviation, and correlation.\n", + "- Create basic data visualizations, including scatterplots, box plots, and histograms.\n", + "- Describe characteristics and trends in a data set using visualizations.\n", + "- Describe the bias and variance of statistical estimators.\n", + "- Identify a normal distribution within a data set using summary statistics and data visualizations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lesson Guide\n", + "- [Where Are We in the Data Science Workflow?](#where-are-we-in-the-data-science-workflow)\n", + "- [Linear Algebra Review](#linear-algebra-review)\n", + "\n", + "- [Linear Algebra Applications to Machine Learning](#linear-algebra-applications-to-machine-learning)\n", + "- [Code-Along: Examining the Titanic Data Set](#codealong-examining-the-titanic-dataset)\n", + "- [Descriptive Statistics Fundamentals](#descriptive-statistics-fundamentals)\n", + "- [Our First Model](#our-first-model)\n", + "- [A Short Introduction to Model Bias and Variance](#a-short-introduction-to-model-bias-and-variance)\n", + "- [Correlation and Association](#correlation-and-association)\n", + "- [The Normal Distribution](#the-normal-distribution)\n", + "- [Determining the Distribution of Your Data](#determining-the-distribution-of-your-data)\n", + "- [Lesson Review](#topic-review)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Where Are We in the Data Science Workflow?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional : install ipywidgets\n", + "\n", + "Copy and paste the command below in terminal\n", + "- conda install -c conda-forge ipywidgets" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# https://ipywidgets.readthedocs.io/en/latest/examples/Using%20Interact.html\n", + "# from ipywidgets import interact\n", + "\n", + "import scipy.stats\n", + "\n", + "\n", + "plt.style.use('fivethirtyeight')\n", + "\n", + "# This makes sure that graphs render in your notebook.\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Linear Algebra Review\n", + "---\n", + "**Objective:** Compute dot products, matrix multiplications, and vector norms by hand and using NumPy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Why Use Linear Algebra in Data Science?\n", + "\n", + "Linear models are efficient and well understood. They can often closely approximate nonlinear solutions, and they scale to high dimensions without difficulty.\n", + "\n", + "Because of these desirable properties, linear algebra is a need-to-know subject for machine learning. In fact, it forms the basis of foundational models such as linear regression, logistic regression, and principal component analysis (PCA). \n", + "\n", + "Unsurprisingly, advanced models such as neural networks and support vector machines rely on linear algebra as their \"trick\" for impressive speedups. Modern-day GPUs are essentially linear algebra supercomputers. And, to utilize their power on a GPU, models must often be carefully formulated in terms of vectors and matrices.\n", + "\n", + "More than that, today's advanced models build upon the simpler foundational models. Each neuron in a neural net is essentially a logistic regressor! Support vector machines utilize a kernel trick to craftily make problems linear that would not otherwise appear to be.\n", + "\n", + "Although we do not have time in this course to comprehensively discuss linear algebra, we highly recommend you become fluent!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Scalars, Vectors, and Matrices\n", + "\n", + "A **scalar** is a single number. Here, symbols that are lowercase single letters refer to scalars. For example, the symbols $a$ and $v$ are scalars that might refer to arbitrary numbers such as $5.328$ or $7$. An example scalar would be:\n", + "\n", + "$$a$$\n", + "\n", + "A **vector** is an ordered sequence of numbers. Here, symbols that are lowercase single letters with an arrow — such as $\\vec{u}$ — refer to vectors. An example vector would be:\n", + "\n", + "$$\\vec{u} = \\left[ \\begin{array}{c}\n", + "1&3&7\n", + "\\end{array} \\right]$$" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 3, 7])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a vector using np.array.\n", + "u = np.array([1, 3, 7])\n", + "u" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Science Models take a numpy array as input feature vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An $m$ x $n$ **matrix** is a rectangular array of numbers with $m$ rows and $n$ columns. Each number in the matrix is an entry. Entries can be denoted $a_{ij}$, where $i$ denotes the row number and $j$ denotes the column number. Note that, because each entry $a_{ij}$ is a lowercase single letter, a matrix is an array of scalars:\n", + "\n", + "$$\\mathbf{A}= \\left[ \\begin{array}{c}\n", + "a_{11} & a_{12} & ... & a_{1n} \\\\\n", + "a_{21} & a_{22} & ... & a_{2n} \\\\\n", + "... & ... & ... & ... \\\\\n", + "a_{m1} & a_{m2} & ... & a_{mn}\n", + "\\end{array} \\right]$$\n", + "\n", + "Matrices are referred to using bold uppercase letters, such as $\\mathbf{A}$. A bold font face is used to distinguish matrices from sets." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 3, 7],\n", + " [4, 6, 3],\n", + " [2, 5, 6]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a matrix using np.array.\n", + "m = np.array([[1, 3, 7], [4, 6, 3], [2, 5, 6]])\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that in Python, a matrix is just a list of lists! The outermost list is a list of rows." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How adding a dimension can help" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Basic Matrix Algebra\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Addition and Subtraction\n", + "Vector **addition** is straightforward. If two vectors are of equal dimensions (The vectors are shown here as column vectors for convenience only):\n", + "\n", + "$\\vec{v} = \\left[ \\begin{array}{c}\n", + "1 \\\\\n", + "3 \\\\\n", + "7\n", + "\\end{array} \\right], \\vec{w} = \\left[ \\begin{array}{c}\n", + "1 \\\\\n", + "0 \\\\\n", + "1\n", + "\\end{array} \\right]$" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "v = np.array([1, 3, 7])\n", + "w = np.array([1, 0, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\vec{v} + \\vec{w} =\n", + "\\left[ \\begin{array}{c}\n", + "1 \\\\\n", + "3 \\\\\n", + "7\n", + "\\end{array} \\right] + \\left[ \\begin{array}{c}\n", + "1 \\\\\n", + "0 \\\\\n", + "1\n", + "\\end{array} \\right] = \n", + "\\left[ \\begin{array}{c}\n", + "1+1 \\\\\n", + "3+0 \\\\\n", + "7+1\n", + "\\end{array} \\right] = \n", + "\\left[ \\begin{array}{c}\n", + "2 \\\\\n", + "3 \\\\\n", + "8\n", + "\\end{array} \\right]\n", + "$\n", + "\n", + "(Subtraction is similar.)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 3, 8])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add the vectors together with +.\n", + "v+w" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Scalar Multiplication\n", + "We scale a vector with **scalar multiplication**, multiplying a vector by a scalar (single quantity):\n", + "\n", + "$ 2 \\cdot \\vec{v} = 2\\left[ \\begin{array}{c}\n", + "1 \\\\\n", + "3 \\\\\n", + "7\n", + "\\end{array} \\right] = \n", + " \\left[ \\begin{array}{c}\n", + "2 \\cdot 1 \\\\\n", + "2 \\cdot 3 \\\\\n", + "2 \\cdot 7\n", + "\\end{array} \\right] = \n", + " \\left[ \\begin{array}{c}\n", + "2 \\\\\n", + "6 \\\\\n", + "14\n", + "\\end{array} \\right]$ " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 6, 14])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Multiply v by 2.\n", + "2 * np.array([1, 3, 7])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Dot Product\n", + "\n", + "Measure of similarity\n", + "\n", + "\n", + "The **dot product** of two _n_-dimensional vectors is:\n", + "\n", + "$ \\vec{v} \\cdot \\vec{w} =\\sum _{i=1}^{n}v_{i}w_{i}=v_{1}w_{1}+v_{2}w_{2}+\\cdots +v_{n}w_{n} $\n", + "\n", + "So, if:\n", + "\n", + "$\\vec{v} = \\left[ \\begin{array}{c}\n", + "1 \\\\\n", + "3 \\\\\n", + "7\n", + "\\end{array} \\right], \\vec{w} = \\left[ \\begin{array}{c}\n", + "1 \\\\\n", + "0 \\\\\n", + "1\n", + "\\end{array} \\right]$\n", + "\n", + "$ \\vec{v} \\cdot \\vec{w} = 1 \\cdot 1 + 3 \\cdot 0 + 7 \\cdot 1 = 8 $" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v = np.array([1, 3, 7])\n", + "w = np.array([1, 0, 1])\n", + "\n", + "# Calculate the dot product of v and w using np.dot.\n", + "v.dot(w)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Matrix Multiplication\n", + "**Matrix multiplication**, $\\mathbf{A}_{mn}$ x $\\mathbf{B}_{ij}$, is valid when the left matrix has the same number of columns as the right matrix has rows ($n = i$). Each entry is the dot product of corresponding row and column vectors.\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dot product illustrated above is: $1 \\cdot 7 + 2 \\cdot 9 + 3 \\cdot 11 = 58$. Can you compute the rest of the dot products by hand?\n", + "\n", + "If the product is the $2$ x $2$ matrix $\\mathbf{C}_{mj}$, then:\n", + "\n", + "+ Matrix entry $c_{12}$ (its FIRST row and SECOND column) is the dot product of the FIRST row of $\\mathbf{A}$ and the SECOND column of $\\mathbf{B}$.\n", + "\n", + "+ Matrix entry $c_{21}$ (its SECOND row and FIRST column) is the dot product of the SECOND row of $\\mathbf{A}$ and the FIRST column of $\\mathbf{B}$.\n", + "\n", + "Note that if the first matrix is $m$ x $n$ ($m$ rows and $n$ columns) and the second is $i$ x $j$ (where $n = i$), then the final matrix will be $m$ x $j$. For example, below we have $2$ x $3$ multiplied by $3$ x $2$, which results in a $2$ x $2$ matrix. Can you see why?" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 58, 64],\n", + " [139, 154]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array([[1, 2, 3], [4, 5, 6]])\n", + "B = np.array([[7, 8], [9, 10], [11, 12]])\n", + "\n", + "A.dot(B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure you can compute this by hand!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### N-Dimensional Space\n", + "\n", + "We often refer to vectors as elements of an $n$-dimensional space. The symbol $\\mathbb{R}$ refers to the set of all real numbers (written in uppercase \"blackboard bold\" font). Because this contains all reals, $3$ and $\\pi$ are **contained in** $\\mathbb{R}$. We often write this symbolically as $3 \\in \\mathbb{R}$ and $\\pi \\in \\mathbb{R}$.\n", + "\n", + "To get the set of all pairs of real numbers, we would essentially take the product of this set with itself (called the Cartesian product) — $\\mathbb{R}$ x $\\mathbb{R}$, abbreviated as $\\mathbb{R}^2$. This set — $\\mathbb{R}^2$ — contains all pairs of real numbers, so $(1, 3)$ is **contained in** this set. We write this symbolically as $(1, 3) \\in \\mathbb{R}^2$.\n", + "\n", + "+ In 2-D space ($\\mathbb{R}^2$), a point is uniquely referred to using two coordinates: $(1, 3) \\in \\mathbb{R}^2$.\n", + "+ In 3-D space ($\\mathbb{R}^3$), a point is uniquely referred to using three coordinates: $(8, 2, -3) \\in \\mathbb{R}^3$.\n", + "+ In $n$-dimensional space ($\\mathbb{R}^n$), a point is uniquely referred to using $n$ coordinates.\n", + "\n", + "Note that these coordinates of course are isomorphic to our vectors! After all, coordinates are ordered sequences of numbers, just as we define vectors to be ordered sequences of numbers. So, especially in machine learning, we often visualize vectors of length $n$ as points in $n$-dimensional space." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Vector Norm\n", + "\n", + "The **magnitude** of a vector, $\\vec{v} \\in \\mathbb{R}^{n}$, can be interpreted as its length in $n$-dimensional space. Therefore it is calculable via the Euclidean distance from the origin:\n", + "\n", + "$\\vec{v} = \\left[ \\begin{array}{c}\n", + "v_{1} \\\\\n", + "v_{2} \\\\\n", + "\\vdots \\\\\n", + "v_{n}\n", + "\\end{array} \\right]$\n", + "\n", + "then $\\| \\vec{v} \\| = \\sqrt{v_{1}^{2} + v_{2}^{2} + ... + v_{n}^{2}} = \\sqrt{v^Tv}$\n", + "\n", + "E.g. if $\\vec{v} = \n", + "\\left[ \\begin{array}{c}\n", + "3 \\\\\n", + "4\n", + "\\end{array} \\right]$, then $\\| \\vec{v} \\| = \\sqrt{3^{2} + 4^{2}} = 5$\n", + "\n", + "This is also called the vector **norm**. You will often see this used in machine learning." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.array([3,4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculate the norm of the vector x with np.linalg.norm.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.0" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Write your code here\n", + "np.linalg.norm(x)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A visual Example\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Linear Algebra Applications to Machine Learning\n", + "---\n", + "\n", + "\n", + "### Distance Between Actual Values and Predicted Values\n", + "We often need to know the difference between predicted values and actual values. In 2-D space, we compute this as:\n", + "$$\\| \\vec{actual} - \\vec{predicted} \\| =\\sqrt{(actual_1 - predicted_1)^2 + (actual_2 - predicted_2)^2}$$\n", + "\n", + "Note that this is just the straight-line distance between the actual point and the predicted point.\n", + "\n", + "\n", + "### Mean Squared Error\n", + "Often, it's easier to look at the mean of the squared errors. Where $\\hat{y}(\\mathbf{X})$ is a vector of predicted values (a function of the data matrix $\\mathbf{X}$) and $\\vec{y}$ is the actual values:\n", + "\n", + "$$MSE = \\frac{1} {n} \\| \\hat{y}(\\mathbf{X}) - \\vec{y} \\|^2$$\n", + "\n", + "\n", + "### Least squares\n", + "Many machine learning models are based on the following form:\n", + "\n", + "$$\\min \\| \\hat{y}(\\mathbf{X}) - \\vec{y} \\|$$\n", + "\n", + "The goal is to minimize the distance between model predictions and actual data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Our First Model\n", + "---\n", + "\n", + "In this section, we will make a **mathematical model** of data. When we say **model**, we mean it in the same sense that a toy car is a **model** of a real car. If we mainly care about appearance, the toy car model is an excellent model. However, the toy car fails to accurately represent other aspects of the car. For example, we cannot use a toy car to test how the actual car would perform in a collision.\n", + "\n", + "In data science, we might take a rich, complex person and model that person solely as a two-dimensional vector: _(age, smokes cigarettes)_. For example: $(90, 1)$, $(28, 0)$, and $(52, 1)$, where $1$ indicates \"smokes cigarettes.\" This model of a complex person obviously fails to account for many things. However, if we primarily care about modeling health risk, it might provide valuable insight.\n", + "\n", + "Now that we have superficially modeled a complex person, we might determine a formula that evaluates risk. For example, an older person tends to have worse health, as does a person who smokes. So, we might deem someone as having risk should `age + 50*smokes > 100`. \n", + "\n", + "This is a **mathematical model**, as we use math to assess risk. It could be mostly accurate. However, there are surely elderly people who smoke who are in excellent health.\n", + "\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the `fare` column from the Titanic data and store it in variable `y`:" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "titanic = pd.read_csv('data/titanic.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " survived pclass name \\\n", + "0 0 3 Braund, Mr. Owen Harris \n", + "1 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", + "2 1 3 Heikkinen, Miss. Laina \n", + "3 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", + "4 0 3 Allen, Mr. William Henry \n", + "\n", + " sex age sibsp parch ticket fare cabin embarked \n", + "0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n", + "1 female 38.0 1 0 PC 17599 71.2833 C85 C \n", + "2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 female 35.0 1 0 113803 53.1000 C123 S \n", + "4 male 35.0 0 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print out the column names:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['survived', 'pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n", + " 'fare', 'cabin', 'embarked'],\n", + " dtype='object')" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Answer:\n", + "titanic.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print out the dimensions of the DataFrame using the `.shape` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(891, 11)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Preview data dimensions.\n", + "titanic.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print out the data types of the columns using the `.dtypes` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "survived int64\n", + "pclass int64\n", + "name object\n", + "sex object\n", + "age float64\n", + "sibsp int64\n", + "parch int64\n", + "ticket object\n", + "fare float64\n", + "cabin object\n", + "embarked object\n", + "dtype: object" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What are the column data types?\n", + "titanic.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the built-in `.value_counts()` function to count the values of each type in the `pclass` column:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3 491\n", + "1 216\n", + "2 184\n", + "Name: pclass, dtype: int64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count the values of the plcass variable.\n", + "titanic['pclass'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Pull up descriptive statistics for each variable using the built-in `.describe()` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " survived pclass name sex age \\\n", + "count 891.000000 891.000000 891 891 714.000000 \n", + "unique NaN NaN 891 2 NaN \n", + "top NaN NaN Molson, Mr. Harry Markland male NaN \n", + "freq NaN NaN 1 577 NaN \n", + "mean 0.383838 2.308642 NaN NaN 29.699118 \n", + "std 0.486592 0.836071 NaN NaN 14.526497 \n", + "min 0.000000 1.000000 NaN NaN 0.420000 \n", + "25% 0.000000 2.000000 NaN NaN 20.125000 \n", + "50% 0.000000 3.000000 NaN NaN 28.000000 \n", + "75% 1.000000 3.000000 NaN NaN 38.000000 \n", + "max 1.000000 3.000000 NaN NaN 80.000000 \n", + "\n", + " sibsp parch ticket fare cabin embarked \n", + "count 891.000000 891.000000 891 891.000000 204 889 \n", + "unique NaN NaN 681 NaN 147 3 \n", + "top NaN NaN CA. 2343 NaN B96 B98 S \n", + "freq NaN NaN 7 NaN 4 644 \n", + "mean 0.523008 0.381594 NaN 32.204208 NaN NaN \n", + "std 1.102743 0.806057 NaN 49.693429 NaN NaN \n", + "min 0.000000 0.000000 NaN 0.000000 NaN NaN \n", + "25% 0.000000 0.000000 NaN 7.910400 NaN NaN \n", + "50% 0.000000 0.000000 NaN 14.454200 NaN NaN \n", + "75% 1.000000 0.000000 NaN 31.000000 NaN NaN \n", + "max 8.000000 6.000000 NaN 512.329200 NaN NaN " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pull up descriptive statistics for each variable.\n", + "titanic.describe(include='all')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Diagnosing Data Problems\n", + "\n", + "- Whenever you get a new data set, the fastest way to find mistakes and inconsistencies is to look at the descriptive statistics.\n", + " - If anything looks too high or too low relative to your experience, there may be issues with the data collection.\n", + "- Your data may contain a lot of missing values and may need to be cleaned meticulously before they can be combined with other data.\n", + " - You can take a quick average or moving average to smooth out the data and combine that to preview your results before you embark on your much longer data-cleaning journey.\n", + " - Sometimes filling in missing values with their means or medians will be the best solution for dealing with missing data. Other times, you may want to drop the offending rows or do real imputation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Descriptive Statistics Fundamentals\n", + "---\n", + "\n", + "- **Objective:** Code summary statistics using NumPy and Pandas: mean, median, mode, max, min, quartile, inter-quartile range, variance, standard deviation, and correlation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A Quick Review of Notation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sum of a constant, $k$, $n$ times:\n", + "$$\\sum_{i=1}^nk$$" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# k + k + k + k + ... + k" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> It is often helpful to think of these sums as `for` loops. For example, the equation can be compactly computed like so:\n", + "\n", + "```\n", + "total = 0\n", + "\n", + "# For i from 1 up to and including n, add k to the sum.\n", + "for i in range(1, n+1):\n", + " total += k\n", + "```\n", + "\n", + "> Or, even more succinctly (using a generator comprehension):\n", + "\n", + "```\n", + "total = sum(k for i in range(1, n+1))\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sum of all numbers from 1 up to and including $n$:\n", + "$$\\sum_{i=1}^ni$$" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "# 1 + 2 + 3 + ... + n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> ```\n", + "total = sum(i for i in range(1, n+1))\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sum of all $x$ from the first $x$ entry to the $n$th $x$ entry:\n", + "$$\\sum_{i=0}^nx_i$$" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# x_0 + x_1 + x_2 + x_3 + ... + x_n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> ```\n", + "total = sum(xi in x) # or just sum(x)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Measures of Central Tendency" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Mean: the average of a dataset.\n", + "- Median: the middle of an ordered dataset; less susceptible to outliers.\n", + "- Mode: the most common value in a dataset; only relevant for discrete data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Mean\n", + "The mean — also known as the average or expected value — is defined as:\n", + "$$E[X] = \\bar{X} =\\frac 1n\\sum_{i=1}^nx_i$$\n", + "\n", + "It is determined by summing all data points in a population and then dividing the total by the number of points. The resulting number is known as the mean or the average.\n", + "\n", + "Be careful — the mean can be highly affected by outliers. For example, the mean of a very large number and some small numbers will be much larger than the \"typical\" small numbers. Earlier, we saw that the mean squared error (MSE) was used to optimize linear regression. Because this mean is highly affected by outliers, the resulting linear regression model is, too." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Median\n", + "The median refers to the midpoint in a series of numbers. Notice that the median is not affected by outliers, so it more so represents the \"typical\" value in a set.\n", + "\n", + "$$ 0,1,2,[3],5,5,1004 $$\n", + "\n", + "$$ 1,3,4,[4,5],5,5,7 $$\n", + "\n", + "To find the median:\n", + "\n", + "- Arrange the numbers in order from smallest to largest.\n", + " - If there is an odd number of values, the middle value is the median.\n", + " - If there is an even number of values, the average of the middle two values is the median.\n", + "\n", + "Although the median has many useful properties, the mean is easier to use in optimization algorithms. The median is more often used in analysis than in machine learning algorithms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Mode\n", + "The mode of a set of values is the value that occurs most often.\n", + "A set of values may have more than one mode, or no mode at all.\n", + "\n", + "$$1,0,1,5,7,8,9,3,4,1$$ \n", + "\n", + "$1$ is the mode, as it occurs the most often (three times)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How Mean, Median and Mode show the Skewness?\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Code-Along\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean titanic fare: 32.2042079685746\n" + ] + } + ], + "source": [ + "# Find the mean of the titanic.fare series using base Python:\n", + "print(\"Mean titanic fare: {}\".format(sum(titanic['fare'])/float(len(titanic['fare']))))" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean titanic fare: 32.2042079685746\n" + ] + } + ], + "source": [ + "# Find the mean of the titanic.fare series using NumPy\n", + "print(\"Mean titanic fare: {}\".format(np.mean(titanic['fare'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32.2042079685746" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find the mean of the titanic.fare series using Pandas:\n", + "titanic.fare.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.4542" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What was the median fare paid (using Pandas)?\n", + "titanic.fare.median()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 8.05\n", + "dtype: float64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use Pandas to find the most common fare paid on the Titanic:\n", + "titanic.fare.mode()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot the distribution \n", + "\n", + "\n", + "1. What king of Skew do we have here?\n", + "2. Plot the right visualization to show it" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "## do your plot here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## is the titanic fare distribution symmetrical? " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "## yes / no , right skewed or left skewed. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculating Mode using scipu library" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ModeResult(mode=array([5]), count=array([4]))" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import mode\n", + "mode([3, 4, 5, 3, 5, 5, 4, 3, 5, 7, 6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Math Review\n", + "\n", + "#### How Do We Measure Distance?\n", + "\n", + "One method is to take the difference between two points:\n", + "\n", + "$$X_2 - X_1$$\n", + "\n", + "However, this can be inconvenient because of negative numbers.\n", + "\n", + "We often use the following square root trick to deal with negative numbers. Note this is equivalent to the absolute value (if the points are 1-D):\n", + "\n", + "$$\\sqrt{(X_2-X_1)^2} = | X_2 - X_1 |$$\n", + "\n", + "#### What About Distance in Multiple Dimensions?\n", + "\n", + "We can turn to the Pythagorean theorem.\n", + "\n", + "$$a^2 + b^2 = c^2$$\n", + "\n", + "To find the distance along a diagonal, it is sufficient to measure one dimension at a time:\n", + "\n", + "$$\\sqrt{a^2 + b^2} = c$$\n", + "\n", + "More generally, we can write this as the norm (You'll see this in machine learning papers):\n", + "\n", + "$$\\|X\\|_2 = \\sqrt{\\sum{x_i^2}} = c$$\n", + "\n", + "What if we want to work with points rather than distances? For points $\\vec{x}: (x_1, x_1)$ and $\\vec{y}: (y_1, y_2)$ we can write:\n", + "\n", + "$$\\sqrt{(x_1 - y_1)^2 + (x_2 - y_2)^2} = c$$\n", + "or\n", + "$$\\sqrt{\\sum{(x_i - y_i)^2}} = c$$\n", + "or\n", + "$$\\| \\vec{x} - \\vec{y} \\| = c$$\n", + "\n", + "> You may be more familiar with defining points as $(x, y)$ rather than $(x_1, x_2)$. However, in machine learning it is much more convenient to define each coordinate using the same base letter with a different subscript. This allows us to easily represent a 100-dimensional point, e.g., $(x_1, x_2, ..., x_{100})$. If we use the grade school method, we would soon run out of letters!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Measures of Dispersion: \n", + "\n", + "Standard Deviation and Variance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Standard deviation (SD, $σ$ for population standard deviation, or $s$ for sample standard deviation) is a measure that is used to quantify the amount of variation or dispersion from the mean of a set of data values. A low standard deviation means that most of the numbers are close to the average. A high standard deviation means that the numbers are spread out.\n", + "\n", + "Standard deviation is the square root of variance:\n", + "\n", + "$$variance = \\frac {\\sum{(x_i - \\bar{X})^2}} {n-1}$$\n", + "\n", + "$$s = \\sqrt{\\frac {\\sum{(x_i - \\bar{X})^2}} {n-1}}$$\n", + "\n", + "> **Standard deviation** is often used because it is in the same units as the original data! By glancing at the standard deviation, we can immediately estimate how \"typical\" a data point might be by how many standard deviations it is from the mean. Furthermore, standard deviation is the only value that makes sense to visually draw alongside the original data.\n", + "\n", + "> **Variance** is often used for efficiency in computations. The square root in the SD always increases with the function to which it is applied. So, removing it can simplify calculations (e.g., taking derivatives), particularly if we are using the variance for tasks such as optimization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating Variance by Hand" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's walk through an example of computing the variance by hand.\n", + "\n", + "Suppose we have the following data:\n", + "\n", + "$$X = [1, 2, 3, 4, 4, 10]$$\n", + "\n", + "First, we compute its mean: \n", + "\n", + "$$\\bar{X} = (1/6)(1 + 2 + 3 + 4 + 4 + 10) = 4$$\n", + "\n", + "Because this is a sample of data rather than the full population, we'll use the second formula. Let's first \"mean center\" the data:\n", + "\n", + "$$X_{centered} = X - \\bar{X} = [-3, -2, -1, 0, 0, 6]$$\n", + "\n", + "Now, we'll simply find the average squared distance of each point from the mean:\n", + "\n", + "$$variance = \\frac {\\sum{(x_i - \\bar{X})^2}} {n-1} = \\frac {(-3)^2 + (-2)^2 + (-1)^2 + 0^2 + 0^2 + 6^2}{6-1} = \\frac{14 + 36}{5} = 10$$\n", + "\n", + "So, the **variance of $X$** is $10$. However, we cannot compare this directly to the original units, because it is in the original units squared. So, we will use the **standard deviation of $X$**, $\\sqrt{10} \\approx 3.16$ to see that the value of $10$ is farther than one standard deviation from the mean of $4$. So, we can conclude it is somewhat far from most of the points (more on what it really might mean later).\n", + "\n", + "---\n", + "\n", + "A variance of $0$ means there is no spread. If we instead take $X = [1, 1, 1, 1]$, then clearly the mean $\\bar{X} = 1$. So, $X_{centered} = [0, 0, 0, 0]$, which directly leads to a variance of $0$. (Make sure you understand why! Remember that variance is the average squared distance of each point from the mean.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember how we just calculated mean squared error to determine the accuracy of our prediction? It turns out we can do this for any statistical estimator, including mean, variance, and machine learning models.\n", + "\n", + "We can even decompose mean squared error to identify where the source of error comes from." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**That can be a lot to take in, so let's break it down in Python.**\n", + "\n", + "#### Assign the first 5 rows of titanic age data to a variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 22.0\n", + "1 38.0\n", + "2 26.0\n", + "3 35.0\n", + "4 35.0\n", + "Name: age, dtype: float64\n" + ] + } + ], + "source": [ + "# Take the first five rows of titanic age data.\n", + "first_five = titanic.age[:5]\n", + "\n", + "print(first_five)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate the mean by hand:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "31.2" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate mean by hand.\n", + "mean = (22 + 38 + 26 + 35 + 35) / 5.0\n", + "\n", + "mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate the variance by hand:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "46.699999999999996" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate variance by hand\n", + "(np.square(22 - mean) +\n", + "np.square(38 - mean) +\n", + "np.square(26 - mean) +\n", + "np.square(35 - mean) +\n", + "np.square(35 - mean)) / 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate the variance and the standard deviation using Pandas:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "46.699999999999996\n", + "6.833739825307955\n" + ] + } + ], + "source": [ + "# Verify with Pandas\n", + "print(first_five.var())\n", + "print(first_five.std())" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "211.0191247463081" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.age.var()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.526497332334044" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.age.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2469.436845743117" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.fare.var()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write a function that calculates the Variance\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "# Type your answer here \n", + "input_list=[22 ,38 ,26 ,35 , 35]\n", + "import numpy as np\n", + "\n", + "# mean=np.mean(input_list)\n", + "# nums_squared = list(map(lambda x: x**2-mean, input_list))\n", + "# nums_squared" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def varcalc(input_list):\n", + " # write code here\n", + " \n", + " return 1\n", + " \n", + "varcalc(input_list)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Covariance and Correlation coefficient\n", + "https://towardsdatascience.com/essential-statistics-for-data-science-ml-4595ff07a1fa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Covariance is a measure of the joint probability of two random variables. It shows the similarity of those variables, which means that if the greater and lesser values of the one variable mainly correspond to the ones from the second variable, the covariance is positive. If the opposite happens then the covariance is negative. If it is approximately or equal to zero, the variables are independent from each other. It is often represented as cov(X, Y), σxʏ or σ(X, Y) for two variables X and Y and its formal definition is: the expected value of the product of their deviations from their individual expected values(arithmetic mean).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By “unpacking” the outer Expected value, but with equal probabilities pᵢ between X=xᵢ and Y=yᵢ, for (i = 1,…,n), we get:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and more generalized:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is the Population covariance. We can calculate the Sample covariance with the same rules that apply to the Sample variance. We just use the unbiased version: Take the same amount of observations of size (n) from each variable, calculate their Expected value and replace 1/n with 1/(n-1)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A special case of covariance is when the two variables are identical (Covariance of a variable with itself). In that case, it is equal with the variance of that variable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is the Population covariance. We can calculate the Sample covariance with the same rules that apply to the Sample variance. We just use the unbiased version: Take the same amount of observations of size (n) from each variable, calculate their Expected value and replace 1/n with 1/(n-1).\n", + "\n", + "\n", + "Now if we divide the covariance of two variables with the product of their standard deviation we will obtain the Pearson’s correlation coefficient. It is a normalization of the covariance so that it has values between +1 and -1 and it is used to make the magnitude interpretable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Covariance and Correlation Matrix\n", + "It is a square matrix that describes the covariance between two or more variables. The covariance Matrix of a random vector X is typically denoted by Kxx or Σ. For example, if we want to calculate the covariance between three variables (X, Y, Z), we must construct the matrix as follows:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Every cell is the covariance between a row variable with its corresponding column variable. As you may have noticed, the diagonal of the matrix contains the special case of the covariance(Covariance of a variable with itself) and thus represents the variance of that variable. Another thing that you may have observed is that the matrix is symmetric and the covariance values under the diagonal are the same as those over it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the same logic, one can construct the Pearson’s correlation coefficient matrix, in which every covariance is divided by the product of the standard deviation of its corresponding variables. In that case, the diagonal always equals 1 which denotes total positive linear correlation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## A Short Introduction to Model Bias and Variance \n", + "\n", + "---\n", + "\n", + "- **Objective:** Describe the bias and variance of statistical estimators." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In simple terms, **bias** shows how accurate a model is in its predictions. (It has **low bias** if it hits the bullseye!)\n", + "\n", + "**Variance** shows how reliable a model is in its performance. (It has **low variance** if the points are predicted consistently!)\n", + "\n", + "These characteristics have important interactions, but we will save that for later.\n", + "\n", + "![Bias and Variance](./images/biasVsVarianceImage.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examples of Bias and Variance" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "heights = np.random.rand(200) + 5" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_means(sample_size):\n", + " true_mean = np.mean(heights)\n", + " \n", + " mean_heights = []\n", + " for n in range(5,sample_size):\n", + " for j in range(30):\n", + " mean_height = np.mean(np.random.choice(heights, n, replace=False))\n", + " mean_heights.append((n, mean_height))\n", + " \n", + " sample_height = pd.DataFrame(mean_heights, columns=['sample_size', 'height'])\n", + " sample_height.plot.scatter(x='sample_size', y='height', figsize=(14, 4), alpha=0.5)\n", + " \n", + " plt.axhline(y=true_mean, c='r')\n", + " plt.title(\"The Bias and Variance of the Mean Estimator\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_variances(sample_size):\n", + " true_variance = np.var(heights)\n", + " \n", + " var_heights = []\n", + " for n in range(5,sample_size):\n", + " for j in range(30):\n", + " var_height1 = np.var(np.random.choice(heights, n, replace=False), ddof=0)\n", + " var_height2 = np.var(np.random.choice(heights, n, replace=False), ddof=1)\n", + " var_height3 = np.var(np.random.choice(heights, n, replace=False), ddof=-1)\n", + " var_heights.append((n, var_height1, var_height2, var_height3))\n", + " \n", + " sample_var = pd.DataFrame(var_heights, columns=['sample_size', 'variance1', 'variance2', 'variance3'])\n", + " sample_var.plot.scatter(x='sample_size', y='variance1', figsize=(14, 3), alpha=0.5)\n", + " plt.axhline(y=true_variance, c='r')\n", + " plt.title(\"The Bias and Variance of the Population Variance Estimator (n)\")\n", + " \n", + " sample_var.plot.scatter(x='sample_size', y='variance3', figsize=(14, 3), alpha=0.5)\n", + " plt.axhline(y=true_variance, c='r')\n", + " plt.title(\"The Bias and Variance of the Biased Sample Variance Estimator (n+1)\")\n", + " \n", + " sample_var.plot.scatter(x='sample_size', y='variance2', figsize=(14, 3), alpha=0.5)\n", + " plt.axhline(y=true_variance, c='r')\n", + " plt.title(\"The Bias and Variance of the Sample Variance Estimator (n-1)\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# interact(plot_means, sample_size=(5,200));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- The red line in the chart above is the true average height, but because we don't want to ask 200 people about their height, we take a samples.\n", + "\n", + "- The blue dots show the estimate of the average height after taking a sample. To give us an idea of how sampling works, we simulate taking multiple samples.\n", + "\n", + "- The $X$ axis shows the sample size we take, while the blue dots show the likely average heights we'll conclude for a given sample size.\n", + "\n", + "- Even though the true average height is around 7 feet, a small sample may lead us to think that it's actually 6.7 or 7.3 feet. \n", + "\n", + "- Notice that the red line is in the center of our estimates. On average, we are correct and have no bias.\n", + "\n", + "- If we take a larger sample size, we get a better estimate. This means that the variance of our estimate gets smaller with larger samples sizes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Correlation and Association\n", + "---\n", + "\n", + "- **Objective:** Describe characteristics and trends in a data set using visualizations.\n", + "\n", + "Correlation measures how variables related to each other.\n", + "\n", + "Typically, we talk about the Pearson correlation coefficient — a measure of **linear** association.\n", + "\n", + "We refer to perfect correlation as **colinearity**.\n", + "\n", + "The following are a few correlation coefficients. Note that if both variables trend upward, the coefficient is positive. If one trends opposite the other, it is negative. \n", + "\n", + "It is important that you always look at your data visually — the coefficient by itself can be misleading:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Corrolation vs. Causation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Think of various examples of studies you’ve seen in the media related to food:\n", + " - \"[Study links coffee consumption to decreased risk of colorectal cancer](https://news.usc.edu/97761/new-study-links-coffee-consumption-to-decreased-risk-of-colorectal-cancer/)\"\n", + " - \"[Coffee does not decrease risk of colorectal cancer](http://news.cancerconnect.com/coffee-does-not-decrease-risk-of-colorectal-cancer/)\"\n", + "\n", + "There's a whole book series based on these [Spurious Correlations](http://www.tylervigen.com/spurious-correlations)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Structure of Causal Claims\n", + "\n", + "- If X happens, Y must happen.\n", + "- If Y happens, X must have happened.\n", + " - (You need X and something else for Y to happen.)\n", + "- If X happens, Y will probably happen.\n", + "- If Y happens, X probably happened.\n", + "\n", + "> **Note:** Properties from definitions are not causal. If some a shape is a triangle, it's implied that it has three sides. However, it being a triangle does not _cause_ it to have three sides." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"Confounder\" Effect?\n", + "\n", + "Let’s say we performed an analysis to understand what causes lung cancer. \n", + "\n", + "We find that people who carry cigarette lighters are 2.4 times more likely to contract lung cancer than people who don’t carry lighters.\n", + "\n", + "Does this mean that the lighters are causing cancer?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Code-Along: Correlation in Pandas\n", + "\n", + "**Objective:** Explore options for measuring and visualizing correlation in Pandas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display the correlation matrix for all Titanic variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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survivedpclassagesibspparchfare
survived1.000000-0.338481-0.077221-0.0353220.0816290.257307
pclass-0.3384811.000000-0.3692260.0830810.018443-0.549500
age-0.077221-0.3692261.000000-0.308247-0.1891190.096067
sibsp-0.0353220.083081-0.3082471.0000000.4148380.159651
parch0.0816290.018443-0.1891190.4148381.0000000.216225
fare0.257307-0.5495000.0960670.1596510.2162251.000000
\n", + "
" + ], + "text/plain": [ + " survived pclass age sibsp parch fare\n", + "survived 1.000000 -0.338481 -0.077221 -0.035322 0.081629 0.257307\n", + "pclass -0.338481 1.000000 -0.369226 0.083081 0.018443 -0.549500\n", + "age -0.077221 -0.369226 1.000000 -0.308247 -0.189119 0.096067\n", + "sibsp -0.035322 0.083081 -0.308247 1.000000 0.414838 0.159651\n", + "parch 0.081629 0.018443 -0.189119 0.414838 1.000000 0.216225\n", + "fare 0.257307 -0.549500 0.096067 0.159651 0.216225 1.000000" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A:\n", + "titanic.corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use Seaborn to plot a heat map of the correlation matrix:\n", + "\n", + "The `sns.heatmap()` function will accomplish this.\n", + "\n", + "- Generate a correlation matrix from the Titanic data using the `.corr()` method.\n", + "- Pass the correlation matrix into `sns.heatmap()` as its only parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SZqTlv5E0utGYnYjMzEqkkXvNSRpOcTebrwJjgRN6H9dT4TTg7YjYHbgKuLzRmJ2IzMxKpMFb/EwEXo6IVyLiY+AO4OiqOkcDt6byTOCPpcZu5e1EZGZWIg12ze0ArKh4vTLNq1knItYA7wJbNxKzByuYmZVIJz4Yz4nIzKxEGvxB6ypgp4rXO6Z5teqsTE9EGAW82chO3TVnZlYiDXbNzQXGSNpV0obA8UBXVZ0u4JRUPhZ4LCIaurNQaVtEu63uwF91VXlreO4ImmOXYaNyh9AU753yrdwhNM0Wt/7P3CE0xRbjz8kdQttpJCNExBpJZ1LcpHo4cEtELJF0IcVTtLuAfwRuk/Qy8BZFsmpIaRORmdlQ1NPgbU8j4gHggap5UyvKvwO+2dBOqjgRmZmViAcrmJlZVp14ix8nIjOzEvFjIMzMLKtGrxHl4ERkZlYinZeGnIjMzErF14jMzCyr7g5sEzkRmZmViFtEZmaWlQcrmJlZVp2XhpyIzMxKxV1zZmaWlQcrmJlZVp14jajjnkckabSkxbnjMDNrRzGAqV20bYtI0oj0PHQzM6uTW0RVUuvlBUnTJS2VNFPSppKmSporabGkGyUp1Z8l6e8lzQPOlrStpLslLUjTl9Omh0u6SdISSQ9L2mQwj8PMrFM0+ITWLFrRNfd54PqI2AN4D/g+cG1E7B8RewKbAH9WUX/DiJgQET8DrgGeiIi9gf2AJanOGOC6iBgHvAMc04LjMDNrezGA/9pFKxLRioiYncrTgIOBr0j6jaRFwGHAuIr6MyrKhwE3AEREd0S8m+a/GhHzU/kZYPRgBW9m1km6ibqndtGKa0TVRxvA9cCEiFgh6QJg44rlH9Sxzd9XlLspWlVmZkNeO3W51asVLaKdJR2YyicCT6byG5JGAseuY91Hge8BSBouadTghWlm1vl6Iuqe2kUrEtEy4AxJS4EtKbrabgIWAw8Bc9ex7tkU3XiLKLrgxg5yrGZmHc3Dt2tbExEnV807P01riYhJVa//HTi6xjb3rKhzZRNiNDMrhU4cvt22vyMyM7OBa6fRcPUa1EQUEcupaL2YmdngWuNEZGZmOblFZGZmWXXi8G0nIjOzEok2GpZdLyciM7MS8ag5MzPLqp1u3VMvJyIzsxJxi8jMzLLyNSIzM8vKo+bMzCyrTvwdUStuempmZi3SQ9Q9DZSkrSQ9Iuml9O+WNersIulZSfPTU7RP72+7TkRmZiXSHT11T+vhPODRiBhD8Zie82rUeR04MCL2Ab4EnCdp+3VttLRdc+M3erf/Sm1u31XP5g6hKaZvPSl3CE3x7LwNc4fQNFuMPyd3CE2x30LffL/aIHfNHQ1MSuVbgVnAuWvtP+LjipcbUUeDp7SJyMxsKBrkB95tGxGvp/L/BbatVUnSTsD9wO7ADyPitXVt1InIzKxEGk1Dkv438J9qLPqva+0nIiTV3F1ErADGpy65eyTNTM+Xq8mJyMysRBr9QWtETO5rmaR/l7RdRLwuaTvgt/1s6zVJi4FDgJl91fNgBTOzEhnMUXNAF3BKKp8C3FtdQdKOkjZJ5S2Bg4Fl69qoE5GZWYkM8qi5y4DDJb0ETE6vkTRB0s2pzh7AbyQtAJ4AroyIRevaqLvmzMxKZDBHzUXEm8Af15g/D/hOKj8CjB/Idp2IzMxKxPeaMzOzrHz3bTMzy8otIjMzy6q7A++/7URkZlYig3xnhUHhRGRmViKd+BgIJyIzsxJxi8jMzLLqxBZR1jsrSDpL0lJJ03PGYWZWFj0RdU/tIneL6PvA5IhY2V9FSSMiYk0LYjIz61jreeuerLIlIkm/AHYD/kXSNOA/AxsDHwHfiohlkk4F/hwYCQwHDpX0Q+A4igcu3R0RP84Rv5lZO+rErrlsiSgiTpd0JPAV4GPgZxGxRtJk4BLgmFR1P2B8RLwl6QhgDDARENAl6Y8i4v9kOAQzs7YTbhGtt1HArZLGUDzXaYOKZY9ExFupfESankuvR1IkJiciMzN8i59GXAQ8HhHfkDSa4jnovT6oKAu4NCL+oXWhmZl1jk68xU+7PI9oFLAqlU9dR72HgG9LGgkgaQdJfzjIsZmZdYxBfjDeoGiXFtFPKbrmzgfu76tSRDwsaQ9gjiSA94GT6edxtWZmQ0V3j68RDUhEjE7FN4DPVSw6Py3/J+Cfqta5Grh68KMzM+s8HjVnZmZZdeI1IiciM7MSaadrP/VyIjIzKxG3iMzMLCsPVjAzs6zcNWdmZlm5a87MzLJqp8c71MuJyMysRPw7IjMzy8otIjMzy6rHj4EwM7OcPFjBzMyy6sREpE4M2szMyqNdnkdkZmZDlBORmZll5URkZmZZORE1QNJRks5r0rbeb8Z26tzXJEn3tWp/9mmSbpY0NpVb9t63E0mjJS3OHMNZkpZKmp4zjqHOo+b6IWlERKyptSwiuoCuFodkJRAR38kdQ6us62+oDXwfmBwRK/ur2ObH0dGGTItI0maS7pe0QNJiSVMkLZe0TVo+QdKsVL5A0m2SZgO3SXpK0riKbc1K9U+VdK2kUZL+TdKwin2tkLSBpM9KelDSM5J+LekLqc6ukuZIWiTp4iYc32hJL0ianv4Pb6akTSXtL+lf03E/LWnzqvUmpjieS/U+n+aPS/XnS1ooaUytc9ho3AM8xnvSeVwi6a/SvNMkvZhivUnStWn+ZyT9StLcNB3Uylir4q712ZslaUJFnavScT0q6TNp3lmSnk/n/440r/ezOUfSS5K+26Jj6OvzNTWd38WSbpSkVH+WpL+XNA84W9K2ku5O52CBpC+nTQ9P79sSSQ9L2qQVx5Ni/AWwG/Avks7t4+/gVEldkh4DHk3zfpiOeaGkn7Qq3lKLiCExAccAN1W8HgUsB7ZJrycAs1L5AuAZYJP0+r8AP0nl7YBlqXwqcG0q3wt8JZWnADen8qPAmFT+EvBYKncBf5nKZwDvN3h8o4EADkqvbwH+FngF2D/N24KiFTwJuK9yXipPBn6Vyj8HTkrlDYFNap3DFr+HW6V/NwEWAzuk93ArYAPg1xXvx+3Awam8M7C0zT57s4AJ6XVUnOupFcfwGrBRKv9BxWdzQToH2wArgO1bcAy1Pl/n9L4nad5twNdTeRZwfcWyGcBfp/LwdA5GA2uAfdL8O4GTW/zeLE/nsa+/g1OBlRWfvSOAGwFR/I/8fcAf5fpslWUaMi0iYBFwuKTLJR0SEe/2U78rIj5K5TuBY1P5OGBmjfozKBIQwPHADEkjgS8Dd0maD/wDRSIDOAj4X6l824CPprYVETE7lacBfwK8HhFzASLivfh018KoFN9i4Cqgt+U3B/g7SecCu6RzMdBz2GxnSVoAPAXsBPwF8EREvBURq4G7KupOBq5N570L2CK9Hzn0d956KD4/ULxvB6fyQmC6pJMpvrB73RsRH0XEG8DjwMRBjL1S9efrYOArkn4jaRFwGJ98fuCTYyItuwEgIrorzsGrETE/lZ+hSE459PV3APBIRLyVykek6TngWeALwJhWBlpGQyYRRcSLwH4UXwoXS5pK8cfdew42rlrlg4p1VwFvShpPkWxm8GldwJGStgK+CDyWtv1OROxTMe1RGVYTDq1S9fbeq2Odi4DHI2JP4Ouk8xARtwNHAR8BD0g6rI9z2BKSJlEklwMjYm+KL4IX1rHKMOCAivO+Q0RkGRSwHuet9338GnBdWneupBFVy6vrD7Za+70eODYi9gJuYu2/ow/o3+8ryt3ku25d8+8gqTwOAZdWfK52j4h/bGWgZTRkEpGk7YEPI2IacAXFH/dyiqQBRffJusyg6OoaFRELqxemL7m5wNUU3V7dEfEe8Kqkb6YYJGnvtMpsipYTwEnrfWBr21nSgal8IkXLYTtJ+6f9b17xZdZrFLAqlU/tnSlpN+CViLiGottxfB/nsFVGAW9HxIcqrrMdAGwGHCppy3Rcle/hw8APel9I2qeFsa6ljvM2jE9a3CcCT6q43rhTRDwOnEtx/L0tuqMlbSxpa4pu1rmDfAi9qj9fT6byG6m1eWzt1YCii/p7AJKGSxo1eGGul5p/BzU8BHy7t3UtaQdJfzjIsZXekElEwF7A06mr5sfAxcBPgKvTBdXuftafSZE47lxHnRnAyazdYjoJOC11KS0Bjk7zzwbOSF0aOwzwWPqyLG1zKbAlxXWeKcDP0/4f4dMtv58Cl0p6jrX/b/Q4YHE6X3sCv6T2OWyVB4ER6dguo0iyq4BLgKcpEvtyoLfL5yxgQrqg/DxwegtjrdbfefsAmJi6hQ4DLqS4jjItfT6eA66JiHdS/YUUXXJPARdFxGstOAb49OfrBopW0GKKL+h1JcSzKbrxFlF0wY0d5FgHqq+/g7VExMMU1x/npGOZCWzeV32rj+81VxKSRlO0xPbMHEpLSRoZEe+nFtHdwC0RcXfuuAaLpAsoBrZc2eL9jmYIfr6sNYZSi8jK6YLU0lgMvArckzkeMxsgt4jMzCwrt4jMzCwrJyIzM8vKicjMzLJyIjIzs6yciMzMLCsnIjMzy+r/A+cxCWtyu8KmAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use Seaborn to plot a correlation heat map\n", + "sns.heatmap(titanic.corr())" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "# Take a closer look at the survived and fare variables using a scatter plot\n", + "\n", + "# Is correlation a good way to inspect the association of fare and survival?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## The Normal Distribution\n", + "---\n", + "\n", + "- **Objective:** Identify a normal distribution within a data set using summary statistics and data visualizations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Math Review\n", + "- What is an event space?\n", + " - A listing of all possible occurrences.\n", + "- What is a probability distribution?\n", + " - A function that describes how events occur in an event space.\n", + "- What are general properties of probability distributions?\n", + " - All probabilities of an event are between 0 and 1.\n", + " - The probability that something occurs is almost certain, or 1.\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### What is the Normal Distribution?\n", + "- A normal distribution is often a key assumption to many models.\n", + " - In practice, if the normal distribution assumption is not met, it's not the end of the world. Your model is just less efficient in most cases.\n", + "\n", + "- The normal distribution depends on the mean and the standard deviation.\n", + "\n", + "- The mean determines the center of the distribution. The standard deviation determines the height and width of the distribution.\n", + "\n", + "- Normal distributions are symmetric, bell-shaped curves.\n", + "\n", + "- When the standard deviation is large, the curve is short and wide.\n", + "\n", + "- When the standard deviation is small, the curve is tall and narrow.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Why do we care about normal distributions?\n", + "\n", + "- They often show up in nature.\n", + "- Aggregated processes tend to distribute normally, regardless of their underlying distribution — provided that the processes are uncorrelated or weakly correlated (central limit theorem).\n", + "- They offer effective simplification that makes it easy to make approximations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot a histogram of 1,000 samples from a random normal distribution:\n", + "\n", + "The `np.random.randn(numsamples)` function will draw from a random normal distribution with a mean of 0 and a standard deviation of 1.\n", + "\n", + "- To plot a histogram, pass a NumPy array with 1000 samples as the only parameter to `plt.hist()`.\n", + "- Change the number of bins using the keyword argument `bins`, e.g. `plt.hist(mydata, bins=50)`" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot a histogram of several random normal samples from NumPy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Skewness\n", + "- Skewness is a measure of the asymmetry of the distribution of a random variable about its mean.\n", + "- Skewness can be positive or negative, or even undefined.\n", + "- Notice that the mean, median, and mode are the same when there is no skew.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot a lognormal distribution generated with NumPy.\n", + "\n", + "Take 1,000 samples using `np.random.lognormal(size=numsamples)` and plot them on a histogram." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot a lognormal distribution generated with NumPy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Real World Application - When mindfullness beats complexity\n", + "- Skewness is surprisingly important.\n", + "- Most algorithms implicitly use the mean by default when making approximations.\n", + "- If you know your data is heavily skewed, you may have to either transform your data or set your algorithms to work with the median." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Kurtosis\n", + "- Kurtosis is a measure of whether the data are peaked or flat, relative to a normal distribution.\n", + "- Data sets with high kurtosis tend to have a distinct peak near the mean, decline rather rapidly, and have heavy tails. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Real-World Application: Risk Analysis\n", + "- Long-tailed distributions with high kurtosis elude intuition; we naturally think the event is too improbable to pay attention to.\n", + "- It's often the case that there is a large cost associated with a low-probability event, as is the case with hurricane damage.\n", + "- It's unlikely you will get hit by a Category 5 hurricane, but when you do, the damage will be catastrophic.\n", + "- Pay attention to what happens at the tails and whether this influences the problem at hand.\n", + "- In these cases, understanding the costs may be more important than understanding the risks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More examples of Distributions\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Determining the Distribution of Your Data\n", + "---\n", + "\n", + "**Objective:** Create basic data visualizations, including scatterplots, box plots, and histograms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](./assets/images/distributions.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the `.hist()` function of your Titantic DataFrame to plot histograms of all the variables in your data.\n", + "\n", + "- The function `plt.hist(data)` calls the Matplotlib library directly.\n", + "- However, each DataFrame has its own `hist()` method that by default plots one histogram per column. \n", + "- Given a DataFrame `my_df`, it can be called like this: `my_df.hist()`. " + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all variables in the Titanic data set using histograms:\n", + "#titanic.fare.plot(kind='density')\n", + "titanic.fare.plot(kind='hist',bins=8)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "titanic.fare.plot(kind='density')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the built-in `.plot.box()` function of your Titanic DataFrame to plot box plots of your variables.\n", + "\n", + "- Given a DataFrame, a box plot can be made where each column is one tick on the x axis.\n", + "- To do this, it can be called like this: `my_df.plot.box()`.\n", + "- Try using the keyword argument `showfliers`, e.g. `showfliers=False`." + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting all histograms can be unweildly; box plots can be more concise:\n", + "titanic.fare.plot(kind='box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Exercise\n", + "\n", + "1. Look at the Titanic data variables.\n", + "- Are any of them normal?\n", + "- Are any skewed?\n", + "- How might this affect our modeling?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional : Building a model (What we will do in 2nd week)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.datasets import load_iris\n", + "X, y = load_iris(return_X_y=True)\n", + "iris=load_iris(return_X_y=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5.1, 3.5, 1.4, 0.2],\n", + " [4.9, 3. , 1.4, 0.2],\n", + " [4.7, 3.2, 1.3, 0.2],\n", + " [4.6, 3.1, 1.5, 0.2],\n", + " [5. , 3.6, 1.4, 0.2],\n", + " [5.4, 3.9, 1.7, 0.4],\n", + " [4.6, 3.4, 1.4, 0.3],\n", + " [5. , 3.4, 1.5, 0.2],\n", + " [4.4, 2.9, 1.4, 0.2],\n", + " [4.9, 3.1, 1.5, 0.1],\n", + " [5.4, 3.7, 1.5, 0.2],\n", + " [4.8, 3.4, 1.6, 0.2],\n", + " [4.8, 3. , 1.4, 0.1],\n", + " [4.3, 3. , 1.1, 0.1],\n", + " [5.8, 4. , 1.2, 0.2],\n", + " [5.7, 4.4, 1.5, 0.4],\n", + " [5.4, 3.9, 1.3, 0.4],\n", + " [5.1, 3.5, 1.4, 0.3],\n", + " [5.7, 3.8, 1.7, 0.3],\n", + " [5.1, 3.8, 1.5, 0.3],\n", + " [5.4, 3.4, 1.7, 0.2],\n", + " [5.1, 3.7, 1.5, 0.4],\n", + " [4.6, 3.6, 1. , 0.2],\n", + " [5.1, 3.3, 1.7, 0.5],\n", + " [4.8, 3.4, 1.9, 0.2],\n", + " [5. , 3. , 1.6, 0.2],\n", + " [5. , 3.4, 1.6, 0.4],\n", + " [5.2, 3.5, 1.5, 0.2],\n", + " [5.2, 3.4, 1.4, 0.2],\n", + " [4.7, 3.2, 1.6, 0.2],\n", + " [4.8, 3.1, 1.6, 0.2],\n", + " [5.4, 3.4, 1.5, 0.4],\n", + " [5.2, 4.1, 1.5, 0.1],\n", + " [5.5, 4.2, 1.4, 0.2],\n", + " [4.9, 3.1, 1.5, 0.2],\n", + " [5. , 3.2, 1.2, 0.2],\n", + " [5.5, 3.5, 1.3, 0.2],\n", + " [4.9, 3.6, 1.4, 0.1],\n", + " [4.4, 3. , 1.3, 0.2],\n", + " [5.1, 3.4, 1.5, 0.2],\n", + " [5. , 3.5, 1.3, 0.3],\n", + " [4.5, 2.3, 1.3, 0.3],\n", + " [4.4, 3.2, 1.3, 0.2],\n", + " [5. , 3.5, 1.6, 0.6],\n", + " [5.1, 3.8, 1.9, 0.4],\n", + " [4.8, 3. , 1.4, 0.3],\n", + " [5.1, 3.8, 1.6, 0.2],\n", + " [4.6, 3.2, 1.4, 0.2],\n", + " [5.3, 3.7, 1.5, 0.2],\n", + " [5. , 3.3, 1.4, 0.2],\n", + " [7. , 3.2, 4.7, 1.4],\n", + " [6.4, 3.2, 4.5, 1.5],\n", + " [6.9, 3.1, 4.9, 1.5],\n", + " [5.5, 2.3, 4. , 1.3],\n", + " [6.5, 2.8, 4.6, 1.5],\n", + " [5.7, 2.8, 4.5, 1.3],\n", + " [6.3, 3.3, 4.7, 1.6],\n", + " [4.9, 2.4, 3.3, 1. ],\n", + " [6.6, 2.9, 4.6, 1.3],\n", + " [5.2, 2.7, 3.9, 1.4],\n", + " [5. , 2. , 3.5, 1. ],\n", + " [5.9, 3. , 4.2, 1.5],\n", + " [6. , 2.2, 4. , 1. ],\n", + " [6.1, 2.9, 4.7, 1.4],\n", + " [5.6, 2.9, 3.6, 1.3],\n", + " [6.7, 3.1, 4.4, 1.4],\n", + " [5.6, 3. , 4.5, 1.5],\n", + " [5.8, 2.7, 4.1, 1. ],\n", + " [6.2, 2.2, 4.5, 1.5],\n", + " [5.6, 2.5, 3.9, 1.1],\n", + " [5.9, 3.2, 4.8, 1.8],\n", + " [6.1, 2.8, 4. , 1.3],\n", + " [6.3, 2.5, 4.9, 1.5],\n", + " [6.1, 2.8, 4.7, 1.2],\n", + " [6.4, 2.9, 4.3, 1.3],\n", + " [6.6, 3. , 4.4, 1.4],\n", + " [6.8, 2.8, 4.8, 1.4],\n", + " [6.7, 3. , 5. , 1.7],\n", + " [6. , 2.9, 4.5, 1.5],\n", + " [5.7, 2.6, 3.5, 1. ],\n", + " [5.5, 2.4, 3.8, 1.1],\n", + " [5.5, 2.4, 3.7, 1. ],\n", + " [5.8, 2.7, 3.9, 1.2],\n", + " [6. , 2.7, 5.1, 1.6],\n", + " [5.4, 3. , 4.5, 1.5],\n", + " [6. , 3.4, 4.5, 1.6],\n", + " [6.7, 3.1, 4.7, 1.5],\n", + " [6.3, 2.3, 4.4, 1.3],\n", + " [5.6, 3. , 4.1, 1.3],\n", + " [5.5, 2.5, 4. , 1.3],\n", + " [5.5, 2.6, 4.4, 1.2],\n", + " [6.1, 3. , 4.6, 1.4],\n", + " [5.8, 2.6, 4. , 1.2],\n", + " [5. , 2.3, 3.3, 1. ],\n", + " [5.6, 2.7, 4.2, 1.3],\n", + " [5.7, 3. , 4.2, 1.2],\n", + " [5.7, 2.9, 4.2, 1.3],\n", + " [6.2, 2.9, 4.3, 1.3],\n", + " [5.1, 2.5, 3. , 1.1],\n", + " [5.7, 2.8, 4.1, 1.3],\n", + " [6.3, 3.3, 6. , 2.5],\n", + " [5.8, 2.7, 5.1, 1.9],\n", + " [7.1, 3. , 5.9, 2.1],\n", + " [6.3, 2.9, 5.6, 1.8],\n", + " [6.5, 3. , 5.8, 2.2],\n", + " [7.6, 3. , 6.6, 2.1],\n", + " [4.9, 2.5, 4.5, 1.7],\n", + " [7.3, 2.9, 6.3, 1.8],\n", + " [6.7, 2.5, 5.8, 1.8],\n", + " [7.2, 3.6, 6.1, 2.5],\n", + " [6.5, 3.2, 5.1, 2. ],\n", + " [6.4, 2.7, 5.3, 1.9],\n", + " [6.8, 3. , 5.5, 2.1],\n", + " [5.7, 2.5, 5. , 2. ],\n", + " [5.8, 2.8, 5.1, 2.4],\n", + " [6.4, 3.2, 5.3, 2.3],\n", + " [6.5, 3. , 5.5, 1.8],\n", + " [7.7, 3.8, 6.7, 2.2],\n", + " [7.7, 2.6, 6.9, 2.3],\n", + " [6. , 2.2, 5. , 1.5],\n", + " [6.9, 3.2, 5.7, 2.3],\n", + " [5.6, 2.8, 4.9, 2. ],\n", + " [7.7, 2.8, 6.7, 2. ],\n", + " [6.3, 2.7, 4.9, 1.8],\n", + " [6.7, 3.3, 5.7, 2.1],\n", + " [7.2, 3.2, 6. , 1.8],\n", + " [6.2, 2.8, 4.8, 1.8],\n", + " [6.1, 3. , 4.9, 1.8],\n", + " [6.4, 2.8, 5.6, 2.1],\n", + " [7.2, 3. , 5.8, 1.6],\n", + " [7.4, 2.8, 6.1, 1.9],\n", + " [7.9, 3.8, 6.4, 2. ],\n", + " [6.4, 2.8, 5.6, 2.2],\n", + " [6.3, 2.8, 5.1, 1.5],\n", + " [6.1, 2.6, 5.6, 1.4],\n", + " [7.7, 3. , 6.1, 2.3],\n", + " [6.3, 3.4, 5.6, 2.4],\n", + " [6.4, 3.1, 5.5, 1.8],\n", + " [6. , 3. , 4.8, 1.8],\n", + " [6.9, 3.1, 5.4, 2.1],\n", + " [6.7, 3.1, 5.6, 2.4],\n", + " [6.9, 3.1, 5.1, 2.3],\n", + " [5.8, 2.7, 5.1, 1.9],\n", + " [6.8, 3.2, 5.9, 2.3],\n", + " [6.7, 3.3, 5.7, 2.5],\n", + " [6.7, 3. , 5.2, 2.3],\n", + " [6.3, 2.5, 5. , 1.9],\n", + " [6.5, 3. , 5.2, 2. ],\n", + " [6.2, 3.4, 5.4, 2.3],\n", + " [5.9, 3. , 5.1, 1.8]])" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "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": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## by using .fit() we build a model" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "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": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import tree\n", + "tree_model = tree.DecisionTreeClassifier()\n", + "tree_model.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1])" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_model.predict([[6.2, 2.9, 4.3, 1.3]])" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "|--- petal width (cm) <= 0.80\n", + "| |--- class: 0\n", + "|--- petal width (cm) > 0.80\n", + "| |--- petal width (cm) <= 1.75\n", + "| | |--- petal length (cm) <= 4.95\n", + "| | | |--- petal width (cm) <= 1.65\n", + "| | | | |--- class: 1\n", + "| | | |--- petal width (cm) > 1.65\n", + "| | | | |--- class: 2\n", + "| | |--- petal length (cm) > 4.95\n", + "| | | |--- petal width (cm) <= 1.55\n", + "| | | | |--- class: 2\n", + "| | | |--- petal width (cm) > 1.55\n", + "| | | | |--- petal length (cm) <= 5.45\n", + "| | | | | |--- class: 1\n", + "| | | | |--- petal length (cm) > 5.45\n", + "| | | | | |--- class: 2\n", + "| |--- petal width (cm) > 1.75\n", + "| | |--- petal length (cm) <= 4.85\n", + "| | | |--- sepal width (cm) <= 3.10\n", + "| | | | |--- class: 2\n", + "| | | |--- sepal width (cm) > 3.10\n", + "| | | | |--- class: 1\n", + "| | |--- petal length (cm) > 4.85\n", + "| | | |--- class: 2\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.tree.export import export_text\n", + "iris = load_iris()\n", + "decision_tree = DecisionTreeClassifier(random_state=0, max_depth=5)\n", + "decision_tree = decision_tree.fit(iris.data, iris.target)\n", + "r = export_text(decision_tree, feature_names=iris['feature_names'])\n", + "print(r)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using PyDot plus for plotting the tree" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load libraries\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn import datasets\n", + "from IPython.display import Image \n", + "from sklearn import tree\n", + "import pydotplus\n", + "\n", + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "# Create decision tree classifer object\n", + "clf = DecisionTreeClassifier(random_state=0)\n", + "\n", + "# Train model\n", + "model = clf.fit(X, y)\n", + "\n", + "# Create DOT data\n", + "dot_data = tree.export_graphviz(clf, out_file=None, \n", + " feature_names=iris.feature_names, \n", + " class_names=iris.target_names)\n", + "\n", + "# Draw graph\n", + "graph = pydotplus.graph_from_dot_data(dot_data) \n", + "\n", + "# Show graph\n", + "Image(graph.create_png())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional : Lets review a Kaggle Challenge \n", + "\n", + "https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Lesson Review\n", + "---\n", + "\n", + "- We covered several different types of summary statistics, what are they?\n", + "- We covered three different types of visualizations, which ones?\n", + "- Describe bias and variance and why they are important.\n", + "- What are some important characteristics of distributions?\n", + "\n", + "**Any further questions?**" + ] + }, + { + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Intro to Stats/Intro_to_Stats.ipynb b/Intro to Stats/Intro_to_Stats.ipynb index 1253a96..335c766 100644 --- a/Intro to Stats/Intro_to_Stats.ipynb +++ b/Intro to Stats/Intro_to_Stats.ipynb @@ -1213,9 +1213,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3444,9 +3442,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.8.3" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/Machine Learning Fundamentals.docx b/Machine Learning Fundamentals.docx new file mode 100644 index 0000000..5d0b1f1 Binary files /dev/null and b/Machine Learning Fundamentals.docx differ diff --git a/My Notebook1.ipynb b/My Notebook1.ipynb new file mode 100644 index 0000000..19955b4 --- /dev/null +++ b/My Notebook1.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test\n" + ] + } + ], + "source": [ + "a = 'test'\n", + "\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = '1'\n", + "int(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " 'acos',\n", + " 'acosh',\n", + " 'asin',\n", + " 'asinh',\n", + " 'atan',\n", + " 'atan2',\n", + " 'atanh',\n", + " 'ceil',\n", + " 'copysign',\n", + " 'cos',\n", + " 'cosh',\n", + " 'degrees',\n", + " 'e',\n", + " 'erf',\n", + " 'erfc',\n", + " 'exp',\n", + " 'expm1',\n", + " 'fabs',\n", + " 'factorial',\n", + " 'floor',\n", + " 'fmod',\n", + " 'frexp',\n", + " 'fsum',\n", + " 'gamma',\n", + " 'gcd',\n", + " 'hypot',\n", + " 'inf',\n", + " 'isclose',\n", + " 'isfinite',\n", + " 'isinf',\n", + " 'isnan',\n", + " 'ldexp',\n", + " 'lgamma',\n", + " 'log',\n", + " 'log10',\n", + " 'log1p',\n", + " 'log2',\n", + " 'modf',\n", + " 'nan',\n", + " 'pi',\n", + " 'pow',\n", + " 'radians',\n", + " 'remainder',\n", + " 'sin',\n", + " 'sinh',\n", + " 'sqrt',\n", + " 'tan',\n", + " 'tanh',\n", + " 'tau',\n", + " 'trunc']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(math)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'pi' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'pi' is not defined" + ] + } + ], + "source": [ + "pi" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.141592653589793" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "math.pi" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.141592653589793" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import math as m\n", + "m.pi" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'm' 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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined" + ] + } + ], + "source": [ + "m.pi" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.141592653589793" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "math.pi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.4 64-bit", + "language": "python", + "name": "python37464bit9d5bfe3b0c324d7da800e1dbfa603398" + }, + "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": 4 +}