Note

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Scatter plot with histograms#

Add histograms to the x-axes and y-axes margins of a scatter plot.

This layout features a central scatter plot illustrating the relationship between x and y, a histogram at the top displaying the distribution of x, and a histogram on the right showing the distribution of y.

For a nice alignment of the main Axes with the marginals, two options are shown below:

While Axes.inset_axes may be a bit more complex, it allows correct handling of main Axes with a fixed aspect ratio.

Let us first define a function that takes x and y data as input, as well as three Axes, the main Axes for the scatter, and two marginal Axes. It will then create the scatter and histograms inside the provided Axes.

import matplotlib.pyplot as plt
import numpy as np

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

# some random data
x = np.random.randn(1000)
y = np.random.randn(1000)


def scatter_hist(x, y, ax, ax_histx, ax_histy):
    # no labels
    ax_histx.tick_params(axis="x", labelbottom=False)
    ax_histy.tick_params(axis="y", labelleft=False)

    # the scatter plot:
    ax.scatter(x, y)

    # 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)
    ax_histx.hist(x, bins=bins)
    ax_histy.hist(y, bins=bins, orientation='horizontal')

Defining the Axes positions using subplot_mosaic#

We use the subplot_mosaic function to define the positions and names of the three axes; the empty axes is specified by '.'. We manually specify the size of the figure, and can make the different axes have different sizes by specifying the width_ratios and height_ratios arguments. The layout argument is set to 'constrained' to optimize the spacing between the axes.

fig, axs = plt.subplot_mosaic([['histx', '.'],
                               ['scatter', 'histy']],
                              figsize=(6, 6),
                              width_ratios=(4, 1), height_ratios=(1, 4),
                              layout='constrained')
scatter_hist(x, y, axs['scatter'], axs['histx'], axs['histy'])
scatter hist

Defining the Axes positions using inset_axes#

inset_axes can be used to position marginals outside the main Axes. The advantage of doing so is that the aspect ratio of the main Axes can be fixed, and the marginals will always be drawn relative to the position of the Axes.

# Create a Figure, which doesn't have to be square.
fig = plt.figure(layout='constrained')
# Create the main Axes.
ax = fig.add_subplot()
# The main Axes' aspect can be fixed.
ax.set_aspect('equal')
# Create marginal Axes, which have 25% of the size of the main Axes.  Note that
# the inset Axes are positioned *outside* (on the right and the top) of the
# main Axes, by specifying axes coordinates greater than 1.  Axes coordinates
# less than 0 would likewise specify positions on the left and the bottom of
# the main Axes.
ax_histx = ax.inset_axes([0, 1.05, 1, 0.25], sharex=ax)
ax_histy = ax.inset_axes([1.05, 0, 0.25, 1], sharey=ax)
# Draw the scatter plot and marginals.
scatter_hist(x, y, ax, ax_histx, ax_histy)

plt.show()
scatter hist

While we recommend using one of the two methods described above, there are number of other ways to achieve a similar layout:

References

The use of the following functions, methods, classes and modules is shown in this example:

Tags: component: axes plot-type: scatter plot-type: histogram level: intermediate

Total running time of the script: (0 minutes 2.354 seconds)

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