#### Overview of this book

In this book, you’ll get hands-on with customizing your data plots with the help of Matplotlib. You’ll start with customizing plots, making a handful of special-purpose plots, and building 3D plots. You’ll explore non-trivial layouts, Pylab customization, and more about tile configuration. You’ll be able to add text, put lines in plots, and also handle polygons, shapes, and annotations. Non-Cartesian and vector plots are exciting to construct, and you’ll explore them further in this book. You’ll delve into niche plots and visualize ordinal and tabular data. In this book, you’ll be exploring 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will also be explored. Finally, you’ll learn how to create interactive plots with the help of Jupyter. Learn expert techniques for effective data visualization using Matplotlib 3 and Python with our latest offering -- Matplotlib 3.0 Cookbook
Preface
Free Chapter
Heavy Customization
Drawing on Plots
Special Purpose Plots
3D and Geospatial Plots
Interactive Plotting
Other Books You May Enjoy

# Heavy Customization

This book will teach us about advanced Matplotlib plots. It will enable you to go from data to plot to insight, in order to take raw numbers, raw information, and turn them into a visualization, which will allow us to build within our mind the actual bit of insight on how the data behaves. This will also focus on the advanced tools to derive more subtle insights from your data.

In this chapter, we will be focusing on the advanced tools of plotting so that you can really derive more subtle insights from your data. The prerequisites for this course give us a basic understanding of Python and the ability to use NumPy to work with array data.

We will learn about the following topics:

• Using style sheets to customize our plot's appearance
• Working with Matplotlib colors
• Building multi-panel plots with complex layouts
• How to configure Matplotlib to use our preferences whenever we start up a new Matplotlib session