back to the matplotlib-gallery at https://github.com/rasbt/matplotlib-gallery



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%load_ext watermark



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%watermark -u -v -d -p matplotlib,numpy









    



last updated: 2016-05-13 

CPython 3.5.1
IPython 4.0.3

matplotlib 1.5.1
numpy 1.11.0

[More info](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `%watermark` extension



In [3]:

    
%matplotlib inline

Matplotlib Formatting IV: Style Sheets

One of the coolest features added to matlotlib 1.5 is the support for "styles"! The "styles" functionality allows us to create beautiful plots rather painlessly -- a great feature for everyone who though that matplotlib's default layout looks a bit dated!

Sections

The styles that are currently included can be listed via print(plt.style.available):



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import matplotlib.pyplot as plt

print(plt.style.available)









    



['seaborn-colorblind', 'seaborn-dark', 'seaborn-ticks', 'seaborn-white', 'fivethirtyeight', 'seaborn-poster', 'seaborn-deep', 'dark_background', 'bmh', 'seaborn-muted', 'ggplot', 'seaborn-talk', 'seaborn-darkgrid', 'seaborn-whitegrid', 'grayscale', 'classic', 'seaborn-bright', 'seaborn-notebook', 'seaborn-paper', 'seaborn-dark-palette', 'seaborn-pastel']

Now, there are two ways to apply the styling to our plots. First, we can set the style for our coding environment globally via the plt.style.use function:



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import numpy as np

plt.style.use('ggplot')

x = np.arange(10)
for i in range(1, 4):
    plt.plot(x, i * x**2, label='Group %d' % i)
plt.legend(loc='best')
plt.show()

Another way to use styles is via the with context manager, which applies the styling to a specific code block only:



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with plt.style.context('fivethirtyeight'):
    for i in range(1, 4):
        plt.plot(x, i * x**2, label='Group %d' % i)
    plt.legend(loc='best')
    plt.show()

Finally, here's an overview of how the different styles look like:



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import math

n = len(plt.style.available)
num_rows = math.ceil(n/4)

fig = plt.figure(figsize=(15, 15))

for i, s in enumerate(plt.style.available):
    with plt.style.context(s):
        ax = fig.add_subplot(num_rows, 4, i+1)
        for i in range(1, 4):
            ax.plot(x, i * x**2, label='Group %d' % i)
            ax.set_xlabel(s, color='black')
            ax.legend(loc='best')
    
fig.tight_layout()
plt.show()



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