Using Visualizations for the Identification of Unexpected Values

We dipped our toes in the water on visualizations in several recipes in the previous chapter. We used histograms and QQ plots to examine the distribution of a single variable, and scatter plots to view how two variables are related. But we were just scratching the surface of the rich visualization tools available in the Matplotlib and Seaborn libraries. Getting comfortable with these tools, and their seemingly inexhaustible capabilities, can help us uncover patterns and oddities that are not obvious when we run the standard battery of descriptives.

Boxplots, for example, are a great tool for visualizing values outside of a certain range. These can be extended with grouped boxplots or violin plots that allow us to compare distributions across subsets of data. We can also do much more with scatter plots than we did in the last chapter, including getting some sense of multivariate relationships. Histograms, too, can...