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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

Understanding logarithmic plots


More often than not, reading daily newspapers and similar articles, one can find charts that are used by media organizations to misrepresent the facts. One common example is using linear scales to create so called panic charts, where a constantly growing value is followed for a long period of time (years) and starting values are smaller than the latest one by several magnitudes. These values when visualized correctly would (and usually should) produce linear or almost linear charts, taking some panic out of the articles they illustrate.

Getting ready

With the logarithmic scale, the ratio of consecutive values is constant. This is important when we are trying to read log plots. With linear (arithmetic) scales, the constant is the distance between consecutive values. In other words, logarithmic plots have a constant distance in orders of magnitude. We will see this illustrated in the following plots. The code used to produce this figure is explained next.

As a...

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