np.random.choice unusable for array dimensions > 1

NumPy version 1.14.2

It’s not possible to grab a random row from a 2d array using np.random.choice. Consider this array:

points = np.random.random((10,2))

Trying to get a random row this way fails

np.random.choice(points)

ValueError: a must be 1-dimensional

which might be reasonable. (A more reasonable behavior is perhaps for np.random.choice to take an optional axis= argument so that it can return a random slice along the axis, defaulting to a random element in the entire array.)

However, there is no way to directly grab a random row:

np.random.choice(list(points))

ValueError: a must be 1-dimensional

np.random.choice(tuple(points))

ValueError: a must be 1-dimensional

np.random.choice([row for row in points])

ValueError: a must be 1-dimensional

Whereas this can be done with random.sample:

import random
random.sample(list(points), 1)

[array([0.77376144, 0.64678796])]

[njsmith]

You can sample a random set of row indices:

row_i = np.random.choice(points.shape[0], ...)
points[row_i, :]

This also allows for sampling a random set of columns.

[rgommers]

FWIW the issue currently has 10 +1, which would rank it 12th of 1,864 among open issues on that metric. Perhaps it’s time to consider reopening?

Those are from before adding Generator.choice, which solves the problem. We’d like to avoid changing the legacy functions at this point.

At least it would be nice to improve discovery for the new recommended method (which does solve the problem AFAICT).

That is a good point. I opened PR gh-16075 to do that.