Question

I have a bi-dimensional np.array like

x = np.array([[1,2], [4,5], [4,6], [5,4], [4,5]])

now I want the indices where x is equal to [4,5] (-> [1, 4]). The operator == works in a different way:

x == [4,5]
array([[False, False],
       [ True,  True],
       [ True, False],
       [False, False],
       [ True,  True]], dtype=bool)

but I want something like [False, True, False, False, True]. Is it ok to do an and?

Usually the array is very big and I have to do it a lot of times, so I need a very fast way.

Answer 1

this should be the numpy-way:

x = np.array([[1,2], [4,5], [4,6], [5,4], [4,5]])
(x == [4,5]).all(1)

#out: array([False,  True, False, False,  True], dtype=bool)