This page has been updated a lot in the past 3 years. Older revisions you might like more than this one:
August 2021: This serves as the first refresh of this guide. My hope is that this guide is a constant work in progress, receiving updates as I receive requests and discover new things myself. This update features more data visualization examples and a more detailed filtering section. I've also posted all relevant code in the guide to github in a Jupyter Notebook, found here.
This guide serves as an update to my original nflscrapR Python Guide. As of 2020, nflscrapR is defunct and nflfastR has taken its place. As the name implies, the library has made the process of scraping new play by play data much faster.
Using Jupyter Notebooks or Jupyter Lab, which come pre-installed with Anaconda is typically the best way to work with