#### 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
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# Looking at various 3D plot types

This section describes how to add line and scatter plots and how to add 3D contour types.

# How to rotate the camera in 3D plots

We have a parametric spiral curve that ascends along the different axes which is described in the following points:

1. We have spiral curve along the z axis, as shown in the following code:
`# Line plotfig = plt.figure()ax = fig.add_subplot(111, projection='3d')theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)z = np.linspace(-2, 2, 100)r = z**2 + 1x = r * np.sin(theta)y = r * np.cos(theta)ax.plot(x,y,z)`

Hence, here we see our spiral in the 3D plot:

1. If we take a look at the view_init method, we have two keyword arguments: elevation, which is given by elev,...