Choosing matplotlib color maps

The matplotlib color maps are getting a lot of criticism lately because they can be misleading; however, most colormaps are just fine in my opinion. The defaults are getting a makeover in matplotlib 2.0 as announced at http://matplotlib.org/style_changes.html (retrieved July 2015). Of course, there are some good arguments that do not support using certain matplotlib colormaps, such as jet. In art, as in data analysis, almost nothing is absolutely true, so I leave it up to you to decide. In practical terms, I think it is important to consider how to deal with print publications and the various types of color blindness. In this recipe, I visualize relatively safe colormaps with colorbars. This is a tiny selection of the many colormaps in matplotlib.

How to do it...

1. The imports are as follows:

   import matplotlib.pyplot as plt
   import matplotlib as mpl
   from dautil import plotting

2. Plot the datasets with the following code:

   fig, axes = plt.subplots(4, 4)
   cmaps = ['autumn', 'spring', 'summer', 'winter',
            'Reds', 'Blues', 'Greens', 'Purples',
            'Oranges', 'pink', 'Greys', 'gray',
            'binary', 'bone', 'hot', 'cool']
   
   for ax, cm in zip(axes.ravel(), cmaps):
       cmap = plt.cm.get_cmap(cm)
       cb = mpl.colorbar.ColorbarBase(ax, cmap=cmap, 
                                      orientation='horizontal')
       cb.set_label(cm)
       ax.xaxis.set_ticklabels([])
   
   plt.tight_layout()
   plt.show()

Refer to the following plot for the end result:

The notebook is in the choosing_colormaps.ipynb file in this book's code bundle. The color maps are used in various visualizations in this book.

See also

• The related matplotlib documentation at http://matplotlib.org/users/colormaps.html (retrieved July 2015)