Scatter plot with histograms

Show the marginal distributions of a scatter as histograms at the sides of the plot.

For a nice alignment of the main axes with the marginals, two options are shown below.

• the axes positions are defined in terms of rectangles in figure coordinates
• the axes positions are defined via a gridspec

An alternative method to produce a similar figure using the axes_grid1 toolkit is shown in the Scatter Histogram (Locatable Axes) example.

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 numpy as np
import matplotlib.pyplot as plt

# 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')

Axes in figure coordinates

To define the axes positions, Figure.add_axes is provided with a rectangle [left, bottom, width, height] in figure coordinates. The marginal axes share one dimension with the main axes.

# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
spacing = 0.005


rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom + height + spacing, width, 0.2]
rect_histy = [left + width + spacing, bottom, 0.2, height]

# start with a square Figure
fig = plt.figure(figsize=(8, 8))

ax = fig.add_axes(rect_scatter)
ax_histx = fig.add_axes(rect_histx, sharex=ax)
ax_histy = fig.add_axes(rect_histy, sharey=ax)

# use the previously defined function
scatter_hist(x, y, ax, ax_histx, ax_histy)

plt.show()

Using a gridspec

We may equally define a gridspec with unequal width- and height-ratios to achieve desired layout. Also see the Customizing Figure Layouts Using GridSpec and Other Functions tutorial.

# start with a square Figure
fig = plt.figure(figsize=(8, 8))

# Add a gridspec with two rows and two columns and a ratio of 2 to 7 between
# the size of the marginal axes and the main axes in both directions.
# Also adjust the subplot parameters for a square plot.
gs = fig.add_gridspec(2, 2,  width_ratios=(7, 2), height_ratios=(2, 7),
                      left=0.1, right=0.9, bottom=0.1, top=0.9,
                      wspace=0.05, hspace=0.05)

ax = fig.add_subplot(gs[1, 0])
ax_histx = fig.add_subplot(gs[0, 0], sharex=ax)
ax_histy = fig.add_subplot(gs[1, 1], sharey=ax)

# use the previously defined function
scatter_hist(x, y, ax, ax_histx, ax_histy)

plt.show()

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References

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

import matplotlib
matplotlib.figure.Figure.add_axes
matplotlib.figure.Figure.add_subplot
matplotlib.figure.Figure.add_gridspec
matplotlib.axes.Axes.scatter
matplotlib.axes.Axes.hist

Out:

<function Axes.hist at 0x7fba54b528b0>