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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

Understanding logarithmic plots


More often than not, while reading daily newspapers and similar articles, one can find charts that are used by media organizations to misrepresent the facts. One usual example is using linear scales to create, so called, panic charts where constantly growing value is followed for long period of time (years) and starting values are smaller from latest one by several magnitudes. These values when visualized correctly, would (and usually should), produce linear or almost linear charts. This takes 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 constant distance in orders of magnitude. We will see this illustrated on the following plots. The code used to produce this figure is explained here.

As...

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