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Broken axis example, where the y-axis will have a portion cut out.
import numpy as np import matplotlib.pyplot as plt np.random.seed(19680801) pts = np.random.rand(30)*.2 # Now let's make two outlier points which are far away from everything. pts[[3, 14]] += .8 # If we were to simply plot pts, we'd lose most of the interesting # details due to the outliers. So let's 'break' or 'cut-out' the y-axis # into two portions - use the top (ax1) for the outliers, and the bottom # (ax2) for the details of the majority of our data fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) fig.subplots_adjust(hspace=0.05) # adjust space between axes # plot the same data on both axes ax1.plot(pts) ax2.plot(pts) # zoom-in / limit the view to different portions of the data ax1.set_ylim(.78, 1.) # outliers only ax2.set_ylim(0, .22) # most of the data # hide the spines between ax and ax2 ax1.spines.bottom.set_visible(False) ax2.spines.top.set_visible(False) ax1.xaxis.tick_top() ax1.tick_params(labeltop=False) # don't put tick labels at the top ax2.xaxis.tick_bottom() # Now, let's turn towards the cut-out slanted lines. # We create line objects in axes coordinates, in which (0,0), (0,1), # (1,0), and (1,1) are the four corners of the axes. # The slanted lines themselves are markers at those locations, such that the # lines keep their angle and position, independent of the axes size or scale # Finally, we need to disable clipping. d = .5 # proportion of vertical to horizontal extent of the slanted line kwargs = dict(marker=[(-1, -d), (1, d)], markersize=12, linestyle="none", color='k', mec='k', mew=1, clip_on=False) ax1.plot([0, 1], [0, 0], transform=ax1.transAxes, **kwargs) ax2.plot([0, 1], [1, 1], transform=ax2.transAxes, **kwargs) plt.show()