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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
Languages
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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

Introduction to deploying the Bokeh application

In the previous sections, we deployed our Bokeh applications using our local machine, which could then be shared with our colleagues in our internal team.

In order to deploy a Bokeh application, we first wrote a script in Python that included the the plot, the callback function, and the layout. We then gave the script an appropriate name. In the previous example, we gave our scripts the name bokeh.py.

Using the Terminal for Mac/Linux or the shell for Windows, we deployed the application from the directory in which the Python script was located with the command shown here:

bokeh serve --show bokeh.py

This launches the application in the default browser of your choice as:

http://localhost:5006/bokeh

In this case, we were making use of the Bokeh Server in order to run and deploy our application.

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