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Python Data Visualization Cookbook (Second Edition)

You're reading from Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

Product type Paperback

Published in Nov 2015

Publisher

ISBN-13 9781784396695

Length 302 pages

Edition 1st Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Visualization

Authors (3):

Igor Milovanovic

Foures

Giuseppe Vettigli

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Table of Contents (11) Chapters

Preface

1. Preparing Your Working Environment FREE CHAPTER

2. Knowing Your Data

3. Drawing Your First Plots and Customizing Them

4. More Plots and Customizations

5. Making 3D Visualizations

6. Plotting Charts with Images and Maps

7. Using the Right Plots to Understand Data

8. More on matplotlib Gems

9. Visualizations on the Clouds with Plot.ly

Index

Using subplots

If you are reading this book from the beginning, you are probably familiar with the subplot class, a descendant of axes that lives on the regular grid of subplot instances. We are going to explain and demonstrate how to use subplots in advanced ways.

In this recipe, you will be learning how to create custom subplot configurations on our plots.

Getting ready

The base class for subplots is matplotlib.axes.SubplotBase. These subplots are matplotlib.axes.Axes instances, but provide helper methods for generating and manipulating a set of Axes within a figure.

There is a class matplotlib.figure.SubplotParams, which holds all the parameters for subplot. The dimensions are normalized to the width or height of the figure. As we already know, if we don't specify any custom values, they will be read from the rc parameters.

The scripting layer (matplotlib.pyplot) holds a few helper methods to manipulate subplots.

matplotlib.pyplot.subplots is used for the easy creation of common layouts of...

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Authors (3)

Igor Milovanovic

Igor Milovanovic

Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems. An evangelist for high-quality systems design, he has a strong interest in software architecture and development methodologies. Igor is always committed to advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration. He also possesses solid knowledge of product development. With field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spend hours on work instead with her and being an avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is also thankful to his parents for letting him develop in various ways to become a person he is today.

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Foures

Foures

Dimitry is a data scientist with a background in applied mathematics and theoretical physics. After completing his physics undergraduate studies in ENS Lyon (France), he studied fluid mechanics at cole Polytechnique in Paris where he obtained first Class class Masters degree. He holds a PhD in applied mathematics from the University of Cambridge. He currently works as a data-scientist for a smart-energy start-up in Cambridge, in close collaboration with the university.

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Giuseppe Vettigli

Giuseppe Vettigli

Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years. His work is focused on the development of machine learning models and applications to use information from structured and unstructured data. He also writes about scientific computing and data visualisation in Python on his blog at http://glowingpython.blogspot.com.

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