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Mastering Python Data Visualization

You're reading from   Mastering Python Data Visualization Generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783988327
Length 372 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. A Conceptual Framework for Data Visualization 2. Data Analysis and Visualization FREE CHAPTER 3. Getting Started with the Python IDE 4. Numerical Computing and Interactive Plotting 5. Financial and Statistical Models 6. Statistical and Machine Learning 7. Bioinformatics, Genetics, and Network Models 8. Advanced Visualization A. 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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