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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 bar charts with error bars


In this recipe, we will show how to create bar charts and how to draw error bars.

Getting ready

To visualize uncertainty of measurement in our dataset or to indicate the error, we can use error bars. Error bars can easily give an idea of how error free the dataset is. They can show one standard deviation, one standard error, or 95 percent confidence interval. There is no standard here, so always explicitly state what values (errors) error bars display. Most papers in the experimental sciences should contain error bars to present accuracy of the data.

How to do it...

Even though just two parameters are mandatory—left and height— we often want to use more than that. Here are some parameters we can use:

  • width: This gives the width of the bars. The default value is 0.8.

  • bottom: If bottom is specified, the value is added to the height. The default is None.

  • edgecolor: This gives the color of the bar edges.

  • ecolor: This specifies the color of any error bar.

  • linewidth...

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