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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

Presenting your results

The right visualization is not just limited to picking the right type of plot, such as scatter plots or bar charts. It extends to picking the right colors, shapes, markers, and features.

Some of the questions that you will want to ask yourself when choosing the right visualization are as follows:

  • Do I want to transmit a positive message to my readers? If yes, the colors green and blue are a great choice
  • Do I want to transmit an alarming/negative message, indicating some form of danger/decline to my readers? If yes, the color red works best
  • Do I want to show how two different segments/categories differ from each other? If yes, using contrasting colors such as red and blue works well

The tone of the insight and message that you want to convey is critical when it comes to creating the ideal visualization.

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