Numpy: argmax over multiple axes without loop

You could do something like this –

# Reshape input array to a 2D array with rows being kept as with original array.
# Then, get idnices of max values along the columns.
max_idx = A.reshape(A.shape[0],-1).argmax(1)

# Get unravel indices corresponding to original shape of A
maxpos_vect = np.column_stack(np.unravel_index(max_idx, A[0,:,:].shape))

Sample run –

In [214]: # Input array
     ...: A = np.random.rand(5,4,3,7,8)

In [215]: # Setup output array and use original loopy code
     ...: maxpos=np.empty(shape=(5,4)) # 4 because ndims in A is 5
     ...: for n in range(0, 5):
     ...:     maxpos[n,:]=np.unravel_index(np.argmax(A[n,:,:,:,:]), A[n,:,:,:,:].shape)
     ...:     

In [216]: # Proposed approach
     ...: max_idx = A.reshape(A.shape[0],-1).argmax(1)
     ...: maxpos_vect = np.column_stack(np.unravel_index(max_idx, A[0,:,:].shape))
     ...: 

In [219]: # Verify results
     ...: np.array_equal(maxpos.astype(int),maxpos_vect)
Out[219]: True

Generalize to n-dim array

We could generalize to solve for n-dim array to get argmax for last N axes combined with something like this –

def argmax_lastNaxes(A, N):
    s = A.shape
    new_shp = s[:-N] + (np.prod(s[-N:]),)
    max_idx = A.reshape(new_shp).argmax(-1)
    return np.unravel_index(max_idx, s[-N:])

The result would a tuple of arrays of indices. If you need the final output as an array, we can use np.stack or np.concatenate.