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matplotlib Plotting Cookbook

You're reading from   matplotlib Plotting Cookbook Discover how easy it can be to create great scientific visualizations with Python. This cookbook includes over sixty matplotlib recipes together with clarifying explanations to ensure you can produce plots of high quality.

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Product type Paperback
Published in Mar 2014
Publisher Packt
ISBN-13 9781849513265
Length 222 pages
Edition Edition
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Author (1):
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Alexandre Devert Alexandre Devert
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Alexandre Devert
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Table of Contents (15) Chapters Close

matplotlib Plotting Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. First Steps FREE CHAPTER 2. Customizing the Color and Styles 3. Working with Annotations 4. Working with Figures 5. Working with a File Output 6. Working with Maps 7. Working with 3D Figures 8. User Interface Index

Plotting boxplots


Boxplot allows you to compare distributions of values by conveniently showing the median, quartiles, maximum, and minimum of a set of values.

How to do it...

The following script shows a boxplot for 100 random values drawn from a normal distribution:

import numpy as np
import matplotlib.pyplot as plt

data = np.random.randn(100)

plt.boxplot(data)
plt.show()

A boxplot will appear that represents the samples we drew from the random distribution. Since the code uses a randomly generated dataset, the resulting figure will change slightly every time the script is run.

The preceding script will display the following graph:

How it works...

The data = [random.gauss(0., 1.) for i in range(100)] variable generates 100 values drawn from a normal distribution. For demonstration purposes, such values are typically read from a file or computed from other data. The plot.boxplot() function takes a set of values and computes the mean, median, and other statistical quantities on its own. The following points describe the preceding boxplot:

  • The red bar is the median of the distribution.

  • The blue box includes 50 percent of the data from the lower quartile to the upper quartile. Thus, the box is centered on the median of the data.

  • The lower whisker extends to the lowest value within 1.5 IQR from the lower quartile.

  • The upper whisker extends to the highest value within 1.5 IQR from the upper quartile.

  • Values further from the whiskers are shown with a cross marker.

There's more...

To show more than one boxplot in a single graph, calling pyplot.boxplot() once for each boxplot is not going to work. It will simply draw the boxplots over each other, making a messy, unreadable graph. However, we can draw several boxplots with just one single call to pyplot.boxplot() as follows:

import numpy as np
import matplotlib.pyplot as plt

data = np.random.randn(100, 5)

plt.boxplot(data)
plt.show()

The preceding script displays the following graph:

The pyplot.boxplot() function accepts a list of lists as the input, rendering a boxplot for each sublist.

You have been reading a chapter from
matplotlib Plotting Cookbook
Published in: Mar 2014
Publisher: Packt
ISBN-13: 9781849513265
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