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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

The IDE tools in Python

Analyzing and visualizing data requires several software tools: a text editor to write code (preferably the syntax highlight), additional tools and libraries to run and test the code, and perhaps another set of tools to present the results. There are many advantages of an IDE. Some notable ones are as follows:

  • The syntax highlight (showing errors or warnings right away)
  • Stepping through code in the debug mode
  • The interactive console
  • Integration with the interactive graphic notebook (such as IPython)

Python 3.x versus Python 2.7

Python 3.x is not backward compatible with the 2.x version. This is why Python 2.7 is still being used. In this book, we will use Python 2.7 and try not to focus on Python 3.x. This issue is beyond the scope of this book, and we recommend that you search for information about how to write code that works with different versions. Some IDE tools have specific instructions to use both these versions. In some cases, the code may have to be written...

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