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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

Product type Paperback

Published in Oct 2015

Publisher

ISBN-13 9781783988327

Length 372 pages

Edition 1st Edition

Languages

Python

Tools

Anaconda

Concepts

Data Visualization

Author (1):

Kirthi Raman

View More author details

Table of Contents (16) Chapters

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

Visualization tools in Python

Analyzing and visualizing data requires several software tools: a text editor to write the code (preferably syntax highlighted), Python and additional libraries to run and test the code, and perhaps a tool to present the results. There are two categories of software tools: general-purpose software tools and specific software components.

Development tools

The general-purpose software tool is an integrated development environment (IDE), which is an application that has all the productivity tools within one package. These IDEs are usually very convenient from the standpoint of handling the Python libraries. More details about these IDE tools will be discussed in the following chapter. In this chapter, we'll limit our discussion to a brief introduction to Canopy from Enthought and Anaconda from Continuum Analytics.

The specific software component are Python plotting libraries such as Bokeh, IPython, matplotlib, NetworkX, SciPy and NumPy, Scikit-learn, and Seaborn....

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Authors (1)

Kirthi Raman

Kirthi Raman

Kirthi Raman is currently working as a lead data engineer with Neustar Inc, based in Mclean, Virginia USA. Kirthi has worked on data visualization, with a focus on JavaScript, Python, R, and Java, and is a distinguished engineer. Previously, he worked as a principle architect, data analyst, and information retrieval specialist at Quotient, Inc. Kirthi has also worked as a technical lead and manager for a start-up. He has taught discrete mathematics and computer science for several years. Kirthi has a graduate degree in mathematics and computer science from IIT Delhi and an MS in computer science from the University of Maryland. He has written several white papers on data analysis and big data.

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