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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

Making error bars


Error bars are useful to display the dispersion of data on a plot. They are relatively simple as a form of visualization; however, they are also a bit problematic because what is shown as an error varies across different sciences and publications. This does not lessen the usefulness of error bars, it just imposes the need to always be careful and explicitly state the nature of the error visualized as an error bar.

Getting ready

To be able to plot an error bar in the raw observed data, we need to compute the mean and the error we want to display.

The error we compute represents the 95% confidence interval that the mean we get from our observation is stable, which means our observations are good estimates of the whole population.

Matplotlib supports these type of plots via matplotlib.pyplot.errorbar function.

It offers several kinds of error bars. They can be vertical (yerr) or horizontal (xerr) and symmetrical or asymmetrical.

How to do it...

In the following code we will:

  1. Use some...

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