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Mastering matplotlib

You're reading from  Mastering matplotlib

Product type Book
Published in Jun 2015
Publisher
ISBN-13 9781783987542
Pages 292 pages
Edition 1st Edition
Languages
Authors (2):
Duncan M. McGreggor Duncan M. McGreggor
Profile icon Duncan M. McGreggor
Duncan M McGreggor Duncan M McGreggor
Profile icon Duncan M McGreggor
View More author details

Table of Contents (16) Chapters

Mastering matplotlib
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Up to Speed 2. The matplotlib Architecture 3. matplotlib APIs and Integrations 4. Event Handling and Interactive Plots 5. High-level Plotting and Data Analysis 6. Customization and Configuration 7. Deploying matplotlib in Cloud Environments 8. matplotlib and Big Data 9. Clustering for matplotlib Index

Visualizing large data


The majority of this notebook has been dedicated to processing large datasets and plotting histograms. This was done intentionally because by using such an approach, the number of artists on the matplotlib canvas is limited to something in the order of hundreds, which is better than attempting to plot millions of artists. In this section, we will address the problem of displaying the actual elements of large datasets. We will then return to the last HDF5 table in the remainder of the chapter.

As a refresher on the volume that we're looking at, the number of data points in our dataset can be calculated in the following way:

In [45]: data_len = len(tab)
         data_len
Out[45]: 288000000

Again, our dataset has nearly one third of a billion points. That is almost certainly more than matplotlib can handle. In fact, one often sees comments online that warn users not to attempt plotting more than ten thousand or one hundred thousand points.

However, is this a good advice...

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