Find indexes of matching rows in two 2-D arrays

numpy, python

Solution

This is an all `numpy` solution - not that is necessarily better than an iterative Python one. It still has to look at all combinations.

In [53]: np.array(np.all((x[:,None,:]==y[None,:,:]),axis=-1).nonzero()).T.tolist()
Out[53]: [[0, 4], [2, 1], [3, 2], [4, 3]]

The intermediate array is `(5,5,4)`. The `np.all` reduces it to:

array([[False, False, False, False,  True],
       [False, False, False, False, False],
       [False,  True, False, False, False],
       [False, False,  True, False, False],
       [False, False, False,  True, False]], dtype=bool)

The rest is just extracting the indices where this is `True`

In crude tests, this times at 47.8 us; the other answer with the `L1` dictionary at 38.3 us; and a third with a double loop at 496 us.

Problem

Suppose that I have two 2-D arrays as follows: ``` array([[3, 3, 1, 0], [2, 3, 1, 3], [0, 2, 3, 1], [1, 0, 2, 3], [3, 1, 0, 2]], dtype=int8) array([[0, 3, 3, 1], [0, 2, 3, 1], [1, 0, 2, 3], [3, 1, 0, 2], [3, 3, 1, 0]], dtype=int8) ``` Some rows in each array have a corresponding row that matches by value (but not necessarily by index) in the other array, and some don't. I would like to find an efficient way to return pairs of indexes in the two arrays that correspond to matching rows. If they were to be tuples I would expect to return ``` (0,4) (2,1) (3,2) (4,3) ```

Original source