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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

Exporting plots as PNG images

The plot generated above looks both impressive and informative at the same time. We might want to publish this plot on a website or in a magazine/journal as a .PNG image with a higher level of quality. Luckily for us, Bokeh offers this flexibility.

Bokeh can generate such images by using the export function. This function uses a browser called Webkit to save the plot in its memory and capture a screenshot. The dimensions of the generated image will be the same as that of the plot you created.

The first step is to install a dependency that this Bokeh functionality depends upon. This is called Phantomjs. You can install this package using Anaconda with the command shown here:

conda install selenium phantomjs pillow

The next step is to install Selenium using pip. We can do this by using the command shown here:

pip3 install selenium

The final step is...

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