Book Image

Matplotlib 2.x By Example

By : Allen Yu, Claire Chung, Aldrin Yim
Book Image

Matplotlib 2.x By Example

By: Allen Yu, Claire Chung, Aldrin Yim

Overview of this book

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Table of Contents (15 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Visualizing population health information


The following section will be dedicated to combining both geographical and population health information of the US. Since this is a tutorial on Python, we focus more on ways to visualize the data, rather than to draw solid conclusions from it. However, many of the findings below actually concur with the population health research and news reports that one may find online.

To begin, let us first download the following information:

  • Top 10 leading causes of death in the United States from 1999 to 2013 from Healthdata.gov
  • 2016 TIGER GeoDatabase from US Census Bureau
  • Survival data of various type of cancers from The Cancer Genome Atlas (TCGA) project (https://cancergenome.nih.gov/)

Since some of the information does not allow direct download through links, we have included the raw data in our code repository:

  • Top 10 leading causes of death in the United States from 1999-2013: https://www.healthdata.gov/dataset/nchs-age-adjusted-death-rates-top-10-leading-causes...