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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
Languages
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Author (1):
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Kirthi Raman Kirthi Raman
Author Profile Icon Kirthi Raman
Kirthi Raman
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Table of Contents (16) Chapters Close

Mastering Python Data Visualization
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. A Conceptual Framework for Data Visualization FREE CHAPTER 2. Data Analysis and Visualization 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 Go Forth and Explore Visualization Index

Some best practices for visualization


The first important step one can take to make a great visualization is to know what is the goal behind the effort. How does one know if the visualization has a purpose? It is also very important to know who the audience is and how this will help them.

Once the answers to these questions are known, and the purpose of visualization is well understood, the next challenge is to choose the right method to present it. The most commonly-used types of visualization could further be categorized according to the following:

  • Comparison and ranking

  • Correlation

  • Distribution

  • Location-specific or geodata

  • Part-to-whole relationships

  • Trends over time

Comparison and ranking

Comparing and ranking can be done in more than one way, but the traditional way is by using bar charts. A bar chart is believed to encode quantitative values as length on the same baseline. However, it is not always the best way to display comparison and rankings. For instance, to display the top 12 countries...

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