In previous chapters, we have learnt the versatile usage of basic Matplotlib APIs to create and customize various plot types. In order to create more suitable visuals for our data, there are more advanced techniques to make more refined figures. In fact, we can leverage not only the native Matplotlib functionalities but also a number of third-party packages built on top of Matplotlib. They provide easy ways to create more advanced plots that are also aesthetically styled by default. We can then make use of Matplotlib techniques to refine our data plots.

In this chapter, we would further explore the advanced usage of Matplotlib. We would learn how to group multiple relevant plots into subplots in one figure, using non-linear scale axis scales, plotting images, and creating advanced plots with the help of some popular third-party packages. Here are the detailed list of topics we would cover:

• Drawing subplots
• Using non-linear axis scales
• Plotting images
• Using Pandas-Matplotlib plotting integration
  • Hexbin plots on bivariate datasets
• Using Seaborn to construct:
  • Kernel density estimation plots for bivariate data
  • Heatmaps with and without hierarchical clustering
  • mpl_finance to plot finance data
• 3D plotting with Axes3D
• Using Basemap and GeoPandas to visualize geographical data