Sort invariant for numpy.argsort with multiple dimensions

Question

numpy.argsort docs state

Returns:
index_array : ndarray, int Array of indices that sort a along the specified axis. If a is one-dimensional, <

Answer 1

This argsort produces a (3,2) array

In [453]: idx=np.argsort(A,axis=-1)
In [454]: idx
Out[454]: 
array([[0, 1],
       [1, 0],
       [0, 1]], dtype=int32)

As you note applying this to A to get the equivalent of np.sort(A, axis=-1) isn't obvious. The iterative solution is sort each row (a 1d case) with:

In [459]: np.array([x[i] for i,x in zip(idx,A)])
Out[459]: 
array([[-1.0856306 ,  0.99734545],
       [-1.50629471,  0.2829785 ],
       [-0.57860025,  1.65143654]])

While probably not the fastest, it is probably the clearest solution, and a good starting point for conceptualizing a better solution.

The tuple(inds) from the take solution is:

(array([[0],
        [1],
        [2]]), 
 array([[0, 1],
        [1, 0],
        [0, 1]], dtype=int32))
In [470]: A[_]
Out[470]: 
array([[-1.0856306 ,  0.99734545],
       [-1.50629471,  0.2829785 ],
       [-0.57860025,  1.65143654]])

In other words:

In [472]: A[np.arange(3)[:,None], idx]
Out[472]: 
array([[-1.0856306 ,  0.99734545],
       [-1.50629471,  0.2829785 ],
       [-0.57860025,  1.65143654]])

The first part is what np.ix_ would construct, but it does not 'like' the 2d idx.

---

Looks like I explored this topic a couple of years ago

argsort for a multidimensional ndarray

a[np.arange(np.shape(a)[0])[:,np.newaxis], np.argsort(a)]

I tried to explain what is going on. The take function does the same sort of thing, but constructs the indexing tuple for a more general case (dimensions and axis). Generalizing to more dimensions, but still with axis=-1 should be easy.

For the first axis, A[np.argsort(A,axis=0),np.arange(2)] works.