import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable # the random data x = np.random.randn(1000) y = np.random.randn(1000) fig, axScatter = plt.subplots(figsize=(5.5, 5.5)) # the scatter plot: axScatter.scatter(x, y) axScatter.set_aspect(1.) # create new axes on the right and on the top of the current axes # The first argument of the new_vertical(new_horizontal) method is # the height (width) of the axes to be created in inches. divider = make_axes_locatable(axScatter) axHistx = divider.append_axes("top", 1.2, pad=0.1, sharex=axScatter) axHisty = divider.append_axes("right", 1.2, pad=0.1, sharey=axScatter) # make some labels invisible plt.setp(axHistx.get_xticklabels() + axHisty.get_yticklabels(), visible=False) # now determine nice limits by hand: binwidth = 0.25 xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))]) lim = (int(xymax/binwidth) + 1)*binwidth bins = np.arange(-lim, lim + binwidth, binwidth) axHistx.hist(x, bins=bins) axHisty.hist(y, bins=bins, orientation='horizontal') # the xaxis of axHistx and yaxis of axHisty are shared with axScatter, # thus there is no need to manually adjust the xlim and ylim of these # axis. #axHistx.axis["bottom"].major_ticklabels.set_visible(False) for tl in axHistx.get_xticklabels(): tl.set_visible(False) axHistx.set_yticks([0, 50, 100]) #axHisty.axis["left"].major_ticklabels.set_visible(False) for tl in axHisty.get_yticklabels(): tl.set_visible(False) axHisty.set_xticks([0, 50, 100]) plt.draw() plt.show()