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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 using a tabbed layout

Sometimes it may be more effective to view a single plot at a time but have multiple plots in the same space. This can be done by making use of the tabbed layout that Bokeh offers. Using a tabbed layout, each plot is stored in a single tab and can be accessed by simply clicking on that tab.

We are going to use the same three plots as in the earlier sections to construct the tabbed layout.

In order to create plots in a tabbed layout, we can use the code shown here:

#Import the required packages

from bokeh.models.widgets import Tabs, Panel
from bokeh.io import output_file, show
from bokeh.layouts import column, row

#Create the two panels

tab1 = Panel(child = plot1, title = 'Tab One')

tab2 = Panel(child = column(plot2,plot3), title = 'Tab Two')

#Feed the tabs into a Tabs object

tabs_object = Tabs(tabs = [tab1, tab2])

#Output...
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