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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 legends 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's 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
plot...
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