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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

Creating plots with ColumnDataSource

The ColumnDataSource provides us with a way to use the same data across multiple plots and widgets. By feeding data into the ColumnDataSource, you build a foundation of data that can be called upon whenever you please, instead of loading the data into your Jupyter Notebook multiple times.

Fundamentally, the ColumnDataSource creates a dictionary in which the value is the data contained in the column and the key is a string name that you specify for that particular column.

Creating a time series plot using the ColumnDataSource

In order to construct a time series plot using the ColumnDataSource, we use this code:

#Import the required packages

from bokeh.io import output_file, show
from bokeh...
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