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Python Data Visualization Cookbook (Second Edition)

You're reading from  Python Data Visualization Cookbook (Second Edition)

Product type Book
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Pages 302 pages
Edition 1st Edition
Languages

Table of Contents (16) Chapters

Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
1. Preparing Your Working Environment 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 scatter plots and histograms


Scatter plots are very often encountered around, as they are the most common plot to visualize the relation between two variables. If we want to take a quick look at the data and see if there is any relation between those (that is, correlation), we would draw a quick scatter plot. For a scatter plot to exist, we must have one variable that can be systematically changed by, for example, experimenter, so we can inspect the possibilities of influencing another variable.

That's why, in this recipe, you will learn how to understand the scatter plots.

Getting ready

We want to see, for example, how two events are affected by each other or if they are affected at all. This visualization is especially useful on large sets of data, where we cannot make any conclusions by looking at the data in the native form—when it is just numbers.

Correlation between values, if there is any, can be positive and negative. Positive correlation is when, for increasing X values, the Y...

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