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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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Toc

Table of Contents (11) Chapters Close

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

Plotting the cross correlation between two variables


If we have two different datasets from two different observations, we want to know if those two event sets are correlated. We want to cross correlate them and see if they match in any way. We are looking for a pattern of a smaller data sample in a larger data sample. The pattern does not have to be an obvious or simple pattern.

Getting ready

We can use the matplotlib's function from pyplot lab—matplotlib.pyplot.xcorr. These functions can plot correlation between two datasets in such a way that we can see if there is any significant pattern between the plotted values. It is assumed that x and y are of the same length.

If we pass the normed argument as True, we can normalize by cross correlation at 0-th lag (that is, when there is no time delay or time lag).

Behind the scenes, correlation is done using NumPy's numpy.correlate function.

Using the usevlines argument (setting it to True), we can instruct matplotlib to use vlines() instead of plot...

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