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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

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Authors (3):
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Igor Milovanovic Igor Milovanovic
Author Profile Icon Igor Milovanovic
Igor Milovanovic
 Foures Foures
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Foures
Giuseppe Vettigli Giuseppe Vettigli
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Giuseppe Vettigli
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Table of Contents (16) Chapters Close

Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
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

Creating 3D histograms


Similarly to 3D bars, we might want to create 3D histograms. These are useful for easily spotting correlation between three independent variables. They can be used to extract information from images in which the third dimension could be the intensity of a channel in the x, y space of the image under analysis.

In this recipe, you will learn how to create 3D histograms.

Getting ready

To recall, a histogram represents the number of occurrences of some value in a particular column—usually called bin. A 3D histogram, then, represents the number of occurrences in a grid. This grid is rectangular, over two variables represented by the data in the two columns.

How to do it...

For this computation we will:

  1. Use NumPy's help, as it has a function for computing the histogram of two variables.

  2. Generate x and y from normal distributions, but with different parameters, to be able to distinguish the correlation in the resulting histogram.

  3. Plot the scatter plot of the same dataset, to demonstrate...

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