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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly Index

Creating 3D bars


Although matplotlib is mainly focused on plotting and mainly in two dimensions, there are different extensions that enable us to plot over geographical maps, to integrate more with Excel, and plot in 3D. These extensions are called toolkits in the matplotlib world. A toolkit is a collection of specific functions focused on one topic, such as plotting in 3D.

Popular toolkits are Basemap, GTK Tools, Excel Tools, Natgrid, AxesGrid, and mplot3d.

We will explore more about mplot3d in this recipe. Toolkit mpl_toolkits.mplot3d provides some basic 3D plotting. Plots supported are scatter, surf, line, and mesh plots. Although this is not the best 3D plotting library, it comes with matplotlib, and we are already familiar with the interface.

Getting ready

Basically, we still need to create a figure and add the desired axes to it. The difference is that we are now specifying a 3D projection for the figure and the axes we are adding are Axes3D.

Now, we can use almost the same functions for...

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