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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

Using colormaps


Color coding the data can have great impact on how your visualizations are perceived by the viewer, as they come with assumptions about color and what that color represents.

Being explicit if the color is used to add additional information to the data is always good. To know when and how to use color in your visualizations is even better.

Getting ready

If your data is not naturally color coded (such as earth/terrain altitudes or object temperature), it's better not to make any artificial mappings to natural coloring. We want to understand the data appropriately and make a choice of color to help the reader decode data easily. We don't want readers constantly trying to suppress learned mapping of color for temperatures, if we are representing financial data that has no connection with Kelvins or Celsius.

If possible, avoid the usual red/green associations, if there are no strong correlations in the data to associate them with those colors.

To help you pick the right color mapping...

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