Apply same permutation for every row in a 2D numpy array

Question

To permute a 1D array A I know that you can run the following code:

import numpy as np
A = np.random.permutation(A)

I have a 2D array and want to apply exactly the same permutation for every row of the array. Is there any way you can specify the numpy to do that for you?

Answer 1

Generate random permutations for the number of columns in A and index into the columns of A, like so -

A[:,np.random.permutation(A.shape[1])]

Sample run -

In [100]: A
Out[100]: 
array([[3, 5, 7, 4, 7],
       [2, 5, 2, 0, 3],
       [1, 4, 3, 8, 8]])

In [101]: A[:,np.random.permutation(A.shape[1])]
Out[101]: 
array([[7, 5, 7, 4, 3],
       [3, 5, 2, 0, 2],
       [8, 4, 3, 8, 1]])

Answer 2

Actually you do not need to do this, from the documentation:

If x is a multi-dimensional array, it is only shuffled along its first index.

So, taking Divakar's array:

a = np.array([
    [3, 5, 7, 4, 7],
    [2, 5, 2, 0, 3],
    [1, 4, 3, 8, 8]
])

you can just do: np.random.permutation(a) and get something like:

array([[2, 5, 2, 0, 3],
       [3, 5, 7, 4, 7],
       [1, 4, 3, 8, 8]])

P.S. if you need to perform column permutations - just do np.random.permutation(a.T).T. Similar things apply to multi-dim arrays.