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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

Creating a robust grid layout

A grid layout combines the row, column, and nested layouts, and allows you to create plots horizontally, vertically, or both horizontally and vertically. Using the grid layout is much more robust because of the versatility of combinations that the layout offers in terms of stacking multiple plots together in a single screen.

In order to construct a grid layout, we will use the same three plots that we have been working on in the previous sections.

We can create a nested grid layout using the code shown here:

#Import required packages

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

#Create the grid layout

grid_layout = gridplot([plot1, plot2], [plot3, None])

#Output the plot

output_file('grid.html')

show(grid_layout)

This results in a grid layout as illustrated here:

Creating a nested layout using the grid layout

In...

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