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Hands-On Data Visualization with Bokeh

You're reading from   Hands-On Data Visualization with Bokeh Interactive web plotting for Python using Bokeh

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781789135404
Length 174 pages
Edition 1st Edition
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Author (1):
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Kevin Jolly Kevin Jolly
Author Profile Icon Kevin Jolly
Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Bokeh Installation and Key Concepts FREE CHAPTER 2. Plotting using Glyphs 3. Plotting with different Data Structures 4. Using Layouts for Effective Presentation 5. Using Annotations, Widgets, and Visual Attributes for Visual Enhancement 6. Building and Hosting Applications Using the Bokeh Server 7. Advanced Plotting with Networks, Geo Data, WebGL, and Exporting Plots 8. The Bokeh Workflow – A Case Study 9. Other Books You May Enjoy

Using WebGL to improve performance

WebGL is a JavaScript API that is available in almost every popular browser in the market. Fundamentally, the Bokeh plots that are displayed on your browser do so with the help of plugins. This could slow down the rendering of plots, especially if the dataset that is used to render these plots is huge! WebGL renders plots using a GPU, without the need for plugins.

In order to enable WebGL while rendering plots, you simply need to use the code shown here while constructing the figure of the plot:

plot = figure(output_backend = 'webgl')

The only downside to rendering the plot in WebGL is that it does not support every component that Bokeh has to offer. At the moment, only two types of glyphs are supported with WebGL rendering. They are:

  • Circle glyphs
  • Line gyphs

The markers that are supported with WebGL are as follows:

  • asterisk
  • square...
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