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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

Creating contour plots


A contour plot displays the isolines of matrix. Isolines are curves where a function of two variables has the same value.

In this recipe we will learn how to create contour plots.

Getting ready

Contours are represented as a contour plot of matrix Z, where Z is interpreted as height with respect to the X-Y plane. Z is of minimum size 2 and must contain at least two different values.

The problem with contour plots is that if they are coded without labeling the isolines, they render pretty useless as we cannot decode the high points from low points or find local minimas.

Here we need to label the contour also. The labeling of isolines can be done either by using labels (clabel()) or colormaps. If your output medium permits the usage of color, colormaps are preferred because viewers will be able to decode data more easily.

The other risk with contour plots is choosing the number of isolines to plot. If we choose too many, the plot becomes too dense to decode, and if we go with...

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