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Mastering Python Data Visualization

You're reading from  Mastering Python Data Visualization

Product type Book
Published in Oct 2015
Publisher
ISBN-13 9781783988327
Pages 372 pages
Edition 1st Edition
Languages

Table of Contents (16) Chapters

Mastering Python Data Visualization
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
A Conceptual Framework for Data Visualization Data Analysis and Visualization Getting Started with the Python IDE Numerical Computing and Interactive Plotting Financial and Statistical Models Statistical and Machine Learning Bioinformatics, Genetics, and Network Models Advanced Visualization Go Forth and Explore Visualization Index

Interactive visualization packages


A few years ago, there were not many interactive tools besides IPython. In order to understand how you can make any visualization interactive, it makes sense to compare it with an existing tool (such as D3.js). One of the reasons why D3.js is very powerful is that a JavaScript-based plotting framework can make the plots to be presented on the Web. Moreover, it comes with all the event-driven functions that can be configured easily.

There are two visualization libraries called Bokeh and VisPy that are popular among a few that are available today. There is another tool called Wakari. This is mainly used for data analytics with a resemblance to IPython in terms of how you can create a browser-based visualization. The Ashiba project was another tool that was developed by Clayton Davis at Continuum, but since the focus of Continuum shifted to Bokeh and Wakari, there is very little work that has been done on Ashiba in the past few years.

Bokeh

Bokeh is an interactive...

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