N-D version of itertools.combinations in numpy

Question

I would like to implement itertools.combinations for numpy. Based on this discussion, I have a function that works for 1D input:

def combs(a, r):
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Answer 1

Not sure how it will work out performance-wise, but you can do the combinations on an index array, then extract the actual array slices with np.take:

def combs_nd(a, r, axis=0):
    a = np.asarray(a)
    if axis < 0:
        axis += a.ndim
    indices = np.arange(a.shape[axis])
    dt = np.dtype([('', np.intp)]*r)
    indices = np.fromiter(combinations(indices, r), dt)
    indices = indices.view(np.intp).reshape(-1, r)
    return np.take(a, indices, axis=axis)

>>> combs_nd([1,2,3], 2)
array([[1, 2],
       [1, 3],
       [2, 3]])
>>> combs_nd([[1,2,3],[4,5,6]], 2, axis=1)
array([[[1, 2],
        [1, 3],
        [2, 3]],

       [[4, 5],
        [4, 6],
        [5, 6]]])