Getting the styles

To see what alternate styles are available for matplotlib charts, you would first run plt.style.available to get the list of available styles.

This is assuming you've already run the following:

import matplotlib.pyplot as plt

>>> [u'seaborn-darkgrid',
     u'Solarize_Light2',
     u'seaborn-notebook',
     u'classic',
     u'seaborn-ticks',
     u'grayscale',
     u'bmh',
     u'seaborn-talk',
     u'dark_background',
     u'ggplot',
     u'fivethirtyeight',
     u'_classic_test',
     u'seaborn-colorblind',
     u'seaborn-deep',
     u'seaborn-whitegrid',
     u'seaborn-bright',
     u'seaborn-poster',
     u'seaborn-muted',
     u'seaborn-paper',
     u'seaborn-white',
     u'fast',
     u'seaborn-pastel',
     u'seaborn-dark',
     u'seaborn',
     u'seaborn-dark-palette']

There's various reasons why you might want a different theme; maybe depending on the content of your report you want a darker or lighter theme. If you're planning on printing the graphs and only have access to a B&W printer, grayscale charts might be preferred. Being mindful of users who may be colorblind is also a thoughtful consideration.

Applying the style

The style must be applied before the charts are made, therefore it's a good idea to run the command right after importing the matplotlab module like so:

import matplotlib.pyplot as plt
plt.style.use('ggplot')

Note: If you change styles in the middle, you might get a mix of styles which is not consistent behavior and most likely not what you want. You should always restart your Jupyter kernal or Python instance when changing to a new style.

With this one line, you can easily customize your graphs and charts to your content making for a more polished reporting template.