This chapter continues the discussion on data visualization by teaching you how to use the seaborn library for visualizing your long form data and giving you the tools you need to customize your visualizations, making them presentation-ready.
There are three notebooks that we will work through, each numbered according to when they will be used:
1-introduction_to_seaborn.ipynb: introduces you to plotting withseaborn2-formatting_plots.ipynb: covers formatting and labeling plots3-customizing_visualizations.ipynb: provides some exposure to plot customizations including reference lines, annotations, and custom colormaps
There is also a bonus notebook that walks through an example of plotting data on a map using COVID-19 cases worldwide: covid19_cases_map.ipynb. It can be used to get started with maps in Python and also builds upon some of the formatting discussed in the chapter.
In addition, we have two Python modules that contain functions that we will use in the aforementioned notebooks:
color_utils.py: includes various functions for working with colors in Pythonviz.py: contains one function for generating regression and residuals plots for each pair of variables in the dataset usingseabornand another function for generating a KDE with reference lines for 1, 2, and 3 standard deviations from the mean
All the datasets necessary for the aforementioned notebooks, along with information on them, can be found in the data/ directory. The end-of-chapter exercises will use these datasets as well; solutions to the exercises can be found in the repository's solutions/ch_06/ directory.