crippledlambda - 1 year ago 217
Python Question

# Is there a function to make scatterplot matrices in matplotlib?

Example of scatterplot matrix

Is there such a function in matplotlib.pyplot?

Generally speaking, matplotlib doesn't usually contain plotting functions that operate on more than one axes object (subplot, in this case). The expectation is that you'd write a simple function to string things together however you'd like.

I'm not quite sure what your data looks like, but it's quite simple to just build a function to do this from scratch. If you're always going to be working with structured or rec arrays, then you can simplify this a touch. (i.e. There's always a name associated with each data series, so you can omit having to specify names.)

As an example:

``````import itertools
import numpy as np
import matplotlib.pyplot as plt

def main():
np.random.seed(1977)
numvars, numdata = 4, 10
data = 10 * np.random.random((numvars, numdata))
fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'],
linestyle='none', marker='o', color='black', mfc='none')
fig.suptitle('Simple Scatterplot Matrix')
plt.show()

def scatterplot_matrix(data, names, **kwargs):
"""Plots a scatterplot matrix of subplots.  Each row of "data" is plotted
against other rows, resulting in a nrows by nrows grid of subplots with the
diagonal subplots labeled with "names".  Additional keyword arguments are
passed on to matplotlib's "plot" command. Returns the matplotlib figure
object containg the subplot grid."""
numvars, numdata = data.shape
fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(8,8))

for ax in axes.flat:
# Hide all ticks and labels
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)

# Set up ticks only on one side for the "edge" subplots...
if ax.is_first_col():
ax.yaxis.set_ticks_position('left')
if ax.is_last_col():
ax.yaxis.set_ticks_position('right')
if ax.is_first_row():
ax.xaxis.set_ticks_position('top')
if ax.is_last_row():
ax.xaxis.set_ticks_position('bottom')

# Plot the data.
for i, j in zip(*np.triu_indices_from(axes, k=1)):
for x, y in [(i,j), (j,i)]:
axes[x,y].plot(data[x], data[y], **kwargs)

# Label the diagonal subplots...
for i, label in enumerate(names):
axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
ha='center', va='center')

# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)

return fig

main()
``````

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