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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

Making a box and a whisker plot


Do you want to visualize a series of data measurement (or observations) to show several properties of the data series (such as the median value, the spread of the data, and the distribution of the data) in one plot? And would you want to do that in a way where you can visually compare several similar data series? How would you visualize them? Welcome to the box-and-whisker plot! Probably the best plot type for comparing distributions, if you are talking to people used to information density.

The box-and-whisker plot usage examples range from comparing test scores between schools to comparing process parameters before and after changes (optimization).

Getting ready

What are the elements of box and whisker plots? As we see in the following diagram, we have several important elements that carry information in the box-and-whisker plot. The first component is the box that carries information about the interquartile range going from lower to upper quartile values....

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