Create a Numpy matrix storing shuffled versions of an input ndarray

I have a 2d ndarray called weights of shape (npts, nweights). For every column of weights, I wish to randomly shuffle the rows. I want to repeat this process num_shuffles times, and store the collection of shufflings into a 3d ndarray called weights_matrix. Importantly, for each shuffling iteration, the shuffling indices of each column of weights should be the same.

Below appears an explicit naive double-for-loop implementation of this algorithm. Is it possible to avoid the python loops and generate weights_matrix in pure Numpy?

import numpy as np 
npts, nweights = 5, 2
weights = np.random.rand(npts*nweights).reshape((npts, nweights))

num_shuffles = 3
weights_matrix = np.zeros((num_shuffles, npts, nweights))
for i in range(num_shuffles):
    indx = np.random.choice(np.arange(npts), npts, replace=False)
    for j in range(nweights):
        weights_matrix[i, :, j] = weights[indx, j]