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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

Adding a legend and annotations


Legends and annotations explain data plots clearly and in context. By assigning each plot a short description about what data it represents, we are enabling an easier mental model in the reader's (viewer's) head. This recipe will show how to annotate specific points on our figures and how to create and position data legends.

Getting ready

How many times have you looked at a chart and wondered what the data represents? More often than not, newspapers and other daily and weekly publications create plots that don't contain appropriate legends, thus leaving the reader free to interpret the representation. This creates ambiguity for the readers and increases the possibility of error.

How to do it...

Let us demonstrate how to add legends and annotations with the following example:

from matplotlib.pyplot import *

# generate different normal distributions
x1 = np.random.normal(30, 3, 100)
x2 = np.random.normal(20, 2, 100)
x3 = np.random.normal(10, 3, 100)

# plot them...
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