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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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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

Summary

The examples shown so far are just to give you an idea of how one should think and plan before making a presentation. The most important stage is the data familiarization and preparation process for visualization. Whether one can get the data first or shape the desired story is mainly influenced by exactly what outcome is attempted. It is like the "chicken and the egg" situation—does data come first or the focus? Initially, it may not be clear what data one may need, but in most cases, after a few iterations, things will be clear as long as there are no errors in the data.

Transform the quality of data by doing some cleanup or reducing the dimensions (if required), and fill gaps if any. Unless the data is good, the efforts that one may put into presenting it visually will be wasted. After a reasonable understanding of the data is achieved, it makes sense to determine what kind of visualization may be appropriate. In some cases, it would be better to display it in several different ways to see the story clearly.

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