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Python Data Visualization Cookbook

You're reading from   Python Data Visualization Cookbook As a developer with knowledge of Python you are already in a great position to start using data visualization. This superb cookbook shows you how in plain language and practical recipes, culminating with 3D animations.

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Product type Paperback
Published in Nov 2013
Publisher Packt
ISBN-13 9781782163367
Length 280 pages
Edition 1st Edition
Languages
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Author (1):
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Igor Milovanovic Igor Milovanovic
Author Profile Icon Igor Milovanovic
Igor Milovanovic
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Table of Contents (15) Chapters Close

Python Data Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
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 Right Plots to Understand Data 8. More on matplotlib Gems Index

Using subplots


If you are reading this book from the start, you are probably familiar with the subplot class, a descendant of axes that lives on the regular grid of subplot instances. We are going to explain and demonstrate how to use subplots in advanced ways.

In this recipe we will be learning how to create custom subplot configurations on our plots.

Getting ready

The base class for subplots is matplotlib.axes.SubplotBase. These subplots are matplotlib.axes.Axes instances but provides helper methods for generating and manipulating a set of Axes within a figure.

There is a class matplotlib.figure.SubplotParams, which holds all the parameters for subplot. The dimensions are normalized to the width or height of the figure. As we already know, if we don't specify any custom values, they will be read from the rc parameters.

The scripting layer (matplotlib.pyplot) holds a few helper methods to manipulate subplots.

matplotlib.pyplot.subplots is used for the easy creation of common layouts of subplots...

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