Summary

In this chapter, we demonstrated how Seaborn helps to create visually appealing figures. We discussed various options for controlling Figure aesthetics, such as Figure style, controlling spines, and setting the context of visualizations. We talked about color palettes in detail. Further visualizations were introduced for univariate and bivariate distributions. Moreover, we discussed FacetGrids for creating multi-plots, and regression plots as a way to analyze the relationships between two variables. Finally, we discussed the Squarify library, which is used to create tree maps.

In the next chapter, we will work with a different category of data, called geospatial data. The prominent attribute of such a dataset is the presence of geo-coordinates that can be used to plot elements on a given position on a map. We will visualize poaching points, the density of cities around the world, and create a more interactive visualization that only displays data points of the currently...