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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Authors (3):
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Igor Milovanovic Igor Milovanovic
Author Profile Icon Igor Milovanovic
Igor Milovanovic
 Foures Foures
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Foures
Giuseppe Vettigli Giuseppe Vettigli
Author Profile Icon Giuseppe Vettigli
Giuseppe Vettigli
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Table of Contents (16) Chapters Close

Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly Index

Plotting data on a map using the Google Map API


In this recipe, we will diverge from the desktop environment and show how we can output for the Web. Although the main language for the web frontend is not Python but HTML, CSS, and JavaScript, we can still use Python for heavy lifting: fetch data, process it, perform intensive computations, and render data in a format(s) suitable for web output, that is, create HTML pages with the required JavaScript version to render our visualization(s).

Getting ready

We will use Google Data Visualization Library for Python to help us prepare data for the frontend interface, where we will use another Google Visualization API to render data in the desired visualization, that is, a map and a table.

Before we start, we need to install the google-visualization-python module. Download the latest stable version from Github and install the module. The following actions demonstrate how to do this:

$ git clone https://github.com/google/google-visualization-python.git...
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