Seaborn by Michael Waskom is a statistical visualization library that is built on top of Matplotlib. It comes with handy functions for visualizing categorical variables, univariate distributions, and bivariate distributions. For more complex plots, various statistical methods such as linear regression models and clustering algorithms are available. Like Matplotlib, Seaborn also supports Pandas dataframes as input, plus automatically performing the necessary slicing, grouping, aggregation, and statistical model fitting to produce informative figures.

These Seaborn functions aim to bring publication-quality figures through an API with a minimal set of arguments, while maintaining the full customization capabilities of Matplotlib. In fact, many functions in Seaborn return a Matplotlib axis or grid object when invoked. Therefore, Seaborn is a great companion of Matplotlib. To install Seaborn through PyPI, you can issue the following command in the terminal:

pip install pandas

Seaborn will be imported as sns throughout this book. This section will not be a documentation of Seaborn. Rather our goal is to give a high-level overview of Seaborn's capabilities from the perspective of Matplotlib users. Readers can refer to the official Seaborn site (http://seaborn.pydata.org/index.html) for more information.