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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 Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781789135404
Pages 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

Linking multiple plots together

At times, we might want our plots to have the same range of values along the x-and/or y-axes in order to facilitate meaningful comparison of the same range of points across different plots.

We will be working with plot1, plot2, and plot3 as illustrated in the sections before this.

In order to create multiple plots with the same range along the y-axis, we use the code shown here:

#Import the required packages

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

#Creating equal y axis ranges

plot3.y_range = plot1.y_range

#Create the row layout

row_layout = row(plot3, plot1)

#Output the plot

output_file('grid.html')

show(row_layout)

This results in a layout of plots as illustrated here:

Plots 1 and 3 linked together by the same y axis range as plot 3

In the previous code, we gave plot1 the same y-range as plot3. In the resulting...

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