By transitioning to the three-dimensional space, you may enjoy greater creative freedom when creating visualizations. The extra dimension can also accommodate more information in a single plot. However, some may argue that 3D is nothing more than a visual gimmick when projected to a 2D surface (such as paper) as it would obfuscate the interpretation of data points.

In Matplotlib version 2, despite significant developments in the 3D API, annoying bugs or glitches still exist. We will discuss some workarounds toward the end of this chapter. More powerful Python 3D visualization packages do exist (such as MayaVi2, Plotly, and VisPy), but it's good to use Matplotlib's 3D plotting functions if you want to use the same package for both 2D and 3D plots, or you would like to maintain the aesthetics of its 2D plots.

For the most part, 3D plots in Matplotlib have similar structures to 2D plots. As such, we will not go through every 3D plot type in this section. We will put our focus on 3D scatter plots and bar charts.