Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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.

Arrow left icon
Product type Paperback
Published in Nov 2013
Publisher Packt
ISBN-13 9781782163367
Length 280 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Igor Milovanovic Igor Milovanovic
Author Profile Icon Igor Milovanovic
Igor Milovanovic
Arrow right icon
View More author details
Toc

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 errorbars


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 percent 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 capabilities around error bars. They can be vertical (yerr) or horizontal (xerr), and symmetrical or asymmetrical.

How to do it...

In the following...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime