In this lesson, we will explore how to use Seaborn to graph multiple statistical distributions, including box plots and violin plots.
Seaborn is optimized to work with large datasets — from its ability to natively interact with Pandas DataFrames, to automatically calculating and plotting aggregates. One of the most powerful aspects of Seaborn is its ability to visualize and compare distributions. Distributions provide us with more information about our data — how spread out it is, its range, etc.
Calculating and graphing distributions is integral to analyzing massive amounts of data. We’ll look at how Seaborn allows us to move beyond the traditional distribution graphs to plots that enable us to communicate important statistical information.
To your right, you’ll find a Jupyter notebook with some example Seaborn charts. We won’t be using Jupyter notebooks in this lesson, but they’re a great way of combining text, code, and visualization. You can find more about them on the Jupyter website.
Take a moment to look at the graphs to the right. What do you notice about each of these charts? What kind of statistical information could they be trying to convey?
When you’re ready, continue to the first exercise.