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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

Adding a shadow to the chart line


To be able to distinguish one particular plot line in the figure or just to fit in the overall style of the output our figure is in, we sometimes need to add a shadow effect to the chart line (or histogram, for that matter). In this recipe, you will be learning how to add a shadow effect to the plot's chart lines.

Getting ready

To add shadows to the lines or rectangles in our charts, we need to use the transformation framework built in matplotlib and located in matplotlib.transforms.

To understand how it all works, we need to explain what transformations are available in matplotlib and how they work.

Transformations know how to convert the given coordinates from their coordinate system into display. They also know how to convert them from display coordinates into their own coordinate system.

The following table summarizes the existing coordinate systems and what they represent:

Coordinate system

Transformation object

Description

Data

Axes.transData

Represents...

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