Book Image

Python Data Visualization Cookbook (Second Edition)

Book Image

Python Data Visualization Cookbook (Second Edition)

Overview of this book

Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
Table of Contents (16 chapters)
Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
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...