Mask an 2Darray row wise by another array

Question

Consider the following 2d array:

>>> A = np.arange(2*3).reshape(2,3)
array([[0, 1, 2],
       [3, 4, 5]])

>>> b = np.array([1, 2])

I would like to get the following mask from A as row wise condition from b as an upper index limit:

>>> mask
array([[True, False, False],
       [True, True, False]])

Can numpy do this in a vectorized manner?

mozway · Accepted Answer · 2022-12-02 09:02:14Z

3

You can use array broadcasting:

mask = np.arange(A.shape[1]) < b[:,None]

output:

array([[ True, False, False],
       [ True,  True, False]])

answered Dec 2, 2022 at 9:02

mozway

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Comments

PaulS · Accepted Answer · 2022-12-02 10:19:28Z

0

Another possible solution, based on the idea that the wanted mask corresponds to a boolean lower triangular matrix:

mask = np.tril(np.ones(A.shape, dtype=bool))

Output:

array([[ True, False, False],
       [ True,  True, False]])

answered Dec 2, 2022 at 9:55

PaulS

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