importmatplotlib.pyplotaspltimportnumpyasnpnp.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 datafig,(ax1,ax2)=plt.subplots(2,1,sharex=True)fig.subplots_adjust(hspace=0.05)# adjust space between Axes# plot the same data on both Axesax1.plot(pts)ax2.plot(pts)# zoom-in / limit the view to different portions of the dataax1.set_ylim(.78,1.)# outliers onlyax2.set_ylim(0,.22)# most of the data# hide the spines between ax and ax2ax1.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 topax2.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 linekwargs=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()

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