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Mastering Python Data Visualization

You're reading from   Mastering Python Data Visualization Generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783988327
Length 372 pages
Edition 1st Edition
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Toc

Table of Contents (11) Chapters Close

Preface 1. A Conceptual Framework for Data Visualization 2. Data Analysis and Visualization FREE CHAPTER 3. Getting Started with the Python IDE 4. Numerical Computing and Interactive Plotting 5. Financial and Statistical Models 6. Statistical and Machine Learning 7. Bioinformatics, Genetics, and Network Models 8. Advanced Visualization A. Go Forth and Explore Visualization Index

The Ebola example

To illustrate the steps mentioned in the previous section and how they lead to an understandable visualization, let us consider the question we had earlier, that is, How many Ebola deaths were reported in the year 2014? This particular question leads to very specific data, which is usually maintained by the World Health Organization (http://www.who.int/en/) or Humanitarian Data Exchange (https://hdx.rwlabs.org). The original source of this data is the World Health Organization (WHO), but the Humanitarian Data Exchange (HDX) is the contributor. Please note, however, that we shall have all the data, along with the Python source code examples for this book, available at a single place.

The data contains information about the spread of the Ebola disease in Guinea, Liberia, Mali, Nigeria, Senegal, Sierra Leone, Spain, United Kingdom, and the United States of America.

The contributor URL for this information is https://data.hdx.rwlabs.org/dataset/ebola-cases-2014/.

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Mastering Python Data Visualization
Published in: Oct 2015
Publisher:
ISBN-13: 9781783988327
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