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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

Big data


The term "big data" is semantically ambiguous due to the varying contexts to which it is applied and the motivations of the users applying it. The first question that may have occurred to you on seeing this chapter's title is "how is this applicable to matplotlib or even plotting in general?" Before we answer this question though, let's establish a working definition of big data.

The Wikipedia article on big data opens with the following informal definition:

"Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate."

This description is honest, as it admits that the definition is imprecise. It also implies that the definition may change given the differing contexts. The words large and complex are relative, and the term traditional data processing is not going to mean the same thing in different segments of the industry. In fact, different departments in a single organization may have widely varying data processing "traditions...

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