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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 multiple plots in a row and column

We might see a situation in which we would like to compare the two scatter plots horizontally, but would like the time series plot to be stacked here with the scatter plots, but all within the embrace of the same layout.

Such a combination of horizontal and vertical layouts is called a nested layout.

We can construct a nested layout by using the code shown here:

#Import the required packages

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

#Construct the nested layout

nested_layout = column(row(plot1,plot2), plot3)

#Output the plot

output_file('nested.html')

show(nested_layout)

This results in a plot as illustrated here:

Plots 1, 2, and 3 in a nested layout

In the previous code, we used the row function to combine Plot 1 and Plot 2 in a horizontal row and then used the column function on the horizontal...

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