This homework assignment is required.

• Matplotlib:
  • Guide to getting started with matplotlib
  • Matplotlib chapter of the Data Science Handbook
• Altair:
  • Introduction to Statistical Visualization
  • Exploring Seattle Weather: A Case Study
• Data viz design: Introductory slides of London's design guidelines

• matplotlib
• seaborn
• altair

In this part, you'll use matplotlib, seaborn, and altair to explore a dataset of your choosing and generate some charts in a Jupyter notebook.

For this assignment, you can choose your own dataset to explore. I recommend selecting a dataset from OpenDataPhilly. You are welcome to to use a dataset from elsewhere, but please email me and let me know what you want to analyze.

Datasets with timestamped entries will be particularly good for analysis, but there are many interesting datasets to consider. They include:

• 311 Requests
• Voter turnout
• Complaints against police
• Parking violations
• Shooting victims
• L+I Violations

and many more...

For OpenDataPhilly datasets, data files can be downloaded in the form of CSV files.

From within a Jupyter notebook, you should explore the datasets and generate charts visualizing different aspects of the data.

Requirements:

• 1 Matplotlib chart (of any type)
  • You should consider what aspect of the dataset might be best plotted using matplotlib.
  • Include your reasoning for using matplotlib to visualize this specific aspect of the data.
  • You will be graded on the aesthetics of the plot, namely, color choices and clarity.
• 1 seaborn chart (of any type)
  • Please include the motivation behind your choice for the type of seaborn plot used and why.
• 3 altair plots
  • The following techniques should be used at least once:
    • A transformation (mean, count, binning, etc)
    • Brush selection
    • Extra credit: 2-chart dashboard, where filtering of one chart cross-filters the other chart (via transform_filter())
• A short discussion (a few sentences) of the main conclusion of each chart (in a markdown cell below each chart). It does not need to be interesting or insightful, but it is good practice to always note the main conclusions so the notebook make sense after time passes.

See the grading rubric for more details.

Important: Your notebooks should be a polished finished product. For example:

• Please remove any extra or unneccesary code.
• Please try to use markdown cells with section headers to mark different sections of the analysis.

We'll be using GitHub for assignment submission again. You can set up your own private repository on GitHub for this assignment using the following link:

https://classroom.github.com/a/ohRVUvKM

The assignment should be added to this GitHub repository before the deadline. You can add files to the repository through the browser (github.com) interface or using the command line locally on your machine. For more help, see:

• Adding a file to a repository via Github
• Adding a file to a repository using the command line

Submission Steps:

1. Add your .ipynb notebook file to your own private repository before the submission deadline.
2. Either add the dataset used in the analysis to the repository, or document the original source of the data so it can be downloaded during grading.
3. Please be sure that your name is listed at the top of the notebook file you submit.

Note: File sizes are limited to 25 MB when adding files to a repository via GitHub in a browser. If your dataset is larger than this, you can try compressing it into a ".zip" to make the file smaller, and then add it to the repository on GitHub. If you run into problems, please email me ([email protected]).