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# Scatter Hist

    import numpy as np
    import matplotlib.pyplot as plt
    from mpl_toolkits.axes_grid1 import make_axes_locatable

    # Fixing random state for reproducibility
    np.random.seed(19680801)

    # 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 = max(np.max(np.abs(x)), np.max(np.abs(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()

Total running time of the script: ( 0 minutes 0.232 seconds)

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