David Landup
Dan Nelson

Seaborn is a statistical infographics library, which builds on the capabilities of Matplotlib. Seaborn was designed to augment Matplotlib’s functions and tools, addressing some of the common issues users have with Matplotlib, with the goal of making the creation of useful, aesthetically pleasing visualizations quicker and easier.

Matplotlib’s high-level API is low-level compared to Seaborn, and as a result the user often needs to write a fair amount of boilerplate code. Seaborn attempts to reduce much of the redundancy in Matplotlib and make the more difficult Matplotlib visualization tasks easier.

In this lesson, we’ll go over the features of Seaborn, discuss the process of creating and styling plots with Seaborn, and then look at some sample visualizations produced with it. We'll top it off with a hands-on project, exploring the Confused Students EEG Dataset.

Features of Seaborn

There are some notable features of Seaborn that make many people's preferred plotting choice, over pure Matplotlib. Seaborn allows the user to create statistical graphics easily thanks to features like: a high-level interface, aesthetically pleasing themes, easy comparison between multiple variables, multi-plot grids, univariate and bivariate visualization, automatic estimation for regressions, and easy plotting of time series data.

Compared to Matplotlib, Seaborn’s API is a much higher-level API, meaning that it takes fewer lines of code to produce visualizations with Seaborn.

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