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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

Visualizing geographic data with Bokeh

In this section, we are going to create a Bokeh visualization for the states in the USA that have the lowest crime rates. In order to do this, we will render a map of the USA and pinpoint the states that have the lowest crime rates.

The first step is to import the required packages:

from bokeh.sampledata import us_states
from bokeh.plotting import figure, show, output_file
from bokeh.models import HoverTool

us_states is a dictionary containing all the information that we need to construct a map of the USA in Bokeh. Additionally, if you wanted to create a map of any other country in Bokeh, you can find the necessary geo data on the web and import the file into your Jupyter Notebook.

The next step is to copy the data into Bokeh so that we can modify the data and delete states that are not significant (if required). Here, we will be deleting...

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