Individually sort columns of 2D arrays within a 3D array based on their individual rows-CodePudding

I know that we can sort the columns of an 2D numpy array based on a row the following way:

a = np.array([[1,4,7],
              [3,1,5],
              [9,5,8]])

a = a[:, a[1, :].argsort()]

Out: [[4,1,7],
      [1,3,5],
      [5,9,8]]

Please note that this is indeed what I want. The second row (index=1) is now sorted and the values in rows 0 and 2 also shifted accordingly. That is, the column positions change based on the sorting order of row 1.

But now to my problem: I don't have a 2D array but a 3D array (i.e. an array of 2D arrays).

a = np.array([[[1,4,7],
               [3,1,5],
               [9,5,8]],
              [[2,8,7],
               [3,8,1],
               [9,2,8]]])

I still want to sort the columns of the 2D arrays, individually, based on the values of their respective rows 1. The desired result would be:

([[[4,1,7],
   [1,3,5],
   [5,9,8]],
  [[7,2,8],
   [1,3,8],
   [8,9,2]]])

I tried the following but the results are not as desired:

a = a[:, :, a[: , 1, :].argsort()]

CodePudding user response：

Try np.take_along_axis:

np.take_along_axis(a,a[:,1].argsort()[:,None], axis=2)

Out:

array([[[4, 1, 7],
        [1, 3, 5],
        [5, 9, 8]],

       [[7, 2, 8],
        [1, 3, 8],
        [8, 9, 2]]])

Honestly, don't ask me why it works :-)

CodePudding user response：

You can use a combination of numpy.argsort and numpy.take_along_axis:

idx = np.argsort(a, axis=2)
np.take_along_axis(a, idx[:,None,1], axis=2)

It works by getting the sorting order from argsort and then keeping only the relevant row (1 here), reshapes to broadcast the operation of take_along_axis on all the other rows.

output:

array([[[4, 1, 7],
        [1, 3, 5],
        [5, 9, 8]],

       [[7, 2, 8],
        [1, 3, 8],
        [8, 9, 2]]])