Exercise 7: APIs and Data Visualization

Part 1: Pull Data from a Web API 

Create an IPython notebook that connects to a web API and download a data set that you think would be interesting to examine in more detail and to generate some charts with. Clean and process the data in this notebook, if needed. 

Use the API techniques we learned in class last week. Consider using the Twitter API, the SF open data portal https://data.sfgov.org/. the Berkeley open data portal  https://data.cityofberkeley.info/, or any other city with open data that you can access programmatically with Python. Ideally, use an API to pull data you want to use in your final project for the class.

Part 2: Data Visualization

Using the data visualization techniques we learned in class and read about in McKinney's Python for Data Analysis chapter 8, visualize interesting aspects of your data set. Create at least 4 (in total) visualizations using at least 3 different visualization types (such as bar charts, line charts, scatter plots, or anything else). You can use Matplotlib or Seaborn to make your charts - but make them visually clear, well labeled, and informative. Each visualization should be followed by a markdown cell that explains what you are visualizing and why it is interesting.

Make sure your notebook runs without any errors and that all the visuals can be seen inline (without me having to re-run your notebook). Then submit your IPython notebook via bcourses.