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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

Setting the transparency and size of axis labels

The Axes label describes what the data in the figure represents and is quite important for the viewer's understanding of the figure itself. By providing labels to the axes background, we help the viewer comprehend the information in an appropriate way.

Getting ready

Before we dive into the code, it is important to understand how matplotlib organizes our figures.

At the top level, there is a Figure instance containing all that we see and some more (that we don't see). The figure contains, among other things, instances of the Axes class as a Figure.axes field. The Axes instances contain almost everything we care about: all the lines, points, ticks, and labels. So, when we call plot(), we are adding a line (matplotlib.lines.Line2D) to the Axes.lines list. If we plot a histogram (hist()), we are adding rectangles to the list of Axes.patches ("patches" is the term inherited from MATLABĀ®, and it represents the "patch...

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