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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

Defining axis lengths and limits

This recipe will demonstrate a variety of useful axis properties around limits and lengths that we can configure in matplotlib.

Getting ready

For this recipe, we want to fire up IPython:

$ ipython

After this, we need to import the plotting functions right away:

from matplotlib.pylab import *

How to do it...

Start experimenting with various properties of axes. Just calling an empty axis() function will return the default values for the axis:

In [1]: axis()
Out[1]: (0.0, 1.0, 0.0, 1.0)

Note that if you are in interactive mode and are using a windowing backend, a figure with an empty axis will be displayed.

Here the values represent xmin, xmax, ymin, and ymax respectively. Similarly, we can set values for the x and y axes:

In [2]: l = [-1, 1, -10, 10]

In [3]: axis(l)
Out[3]: [-1, 1, -10, 10]

Again, if you are in an interactive mode, this will update the same figure. Furthermore, we can also update any value separately using keyword arguments (**kwargs), setting just...

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