Visualization with Seaborn

Code and slides to accompany the PyGotham webinars: https://2020.pygotham.tv/talks/visualization-with-seaborn/ by Data For Science.

Run the code in Binder:

Seaborn is a visualization package that builds on top of matplotlib and pandas to provides a simple, functional, interface that is capable of generating sophisticated and beautiful visualizations. In this lecture we will provide a systematic overview of the way in which seaborn is structured, how it can be used for data exploration and to produce publication ready figures and visualizations.

Outline

1. Basic function structure - seaborn uses a simple and systematic structure for its function calls, making it easy to quickly experiment with different plot types.
2. Axes level vs Figure level functions - axes level functions generate a single plot, while figure level functions have the ability to automatically produce several subplots within the same figure. We will cover how to take full advantage of both.
3. Relationship plots - relationship plot are useful to plot relationships between continuous variables
4. Categorical plots - categorical plots have at least one categorical feature that can be used to slice the data in various ways
5. Using FaceGrid - FaceGrid is a seaborn feature that allows us to define customized plotting functions to generate each subplot of a figure while taking advantage of seaborns functionality to slice and aggregate the data
6. Customizing seaborn with matplotlib - All figures generated by seaborn use matplot objects. We will learn how to access these objects and take advantage of matplotlib to further customize them.

Slides: https://data4sci.com/landing/seaborn/