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Matplotlib 3.0 Cookbook

You're reading from   Matplotlib 3.0 Cookbook Over 150 recipes to create highly detailed interactive visualizations using Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789135718
Length 676 pages
Edition 1st Edition
Languages
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Authors (2):
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 Poladi Poladi
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Poladi
 Borkar Borkar
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Borkar
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Table of Contents (17) Chapters Close

Preface 1. Anatomy of Matplotlib FREE CHAPTER 2. Getting Started with Basic Plots 3. Plotting Multiple Charts, Subplots, and Figures 4. Developing Visualizations for Publishing Quality 5. Plotting with Object-Oriented API 6. Plotting with Advanced Features 7. Embedding Text and Expressions 8. Saving the Figure in Different Formats 9. Developing Interactive Plots 10. Embedding Plots in a Graphical User Interface 11. Plotting 3D Graphs Using the mplot3d Toolkit 12. Using the axisartist Toolkit 13. Using the axes_grid1 Toolkit 14. Plotting Geographical Maps Using Cartopy Toolkit 15. Exploratory Data Analysis Using the Seaborn Toolkit 16. Other Books You May Enjoy

Twin axes

Sometimes, we may want to plot two charts on the same axes, but have a different scale of data. If we use a standard plot with the same scale on the left and right spines, charts may not look right due to a large difference in their scale of data. In such cases, we can use the twin axes feature provided by Matplotlib. We will learn how to use it in this recipe.

We will use product defects data for a month in a manufacturing plant for this example. We will draw a bar plot of the number of defects by reason code (for example 0 to 5, representing various reasons for producing defective products) and cumulative percentage line graph (sum total of defective products aggregated over reason codes).

There are three options for twinning the axes:

  • twinx: Shares the x axis for both the graphs, while the left and right axes denote two different scales
  • twiny: Shares the y axis for...
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