Numpy: Mask of elements in array1 that are also elements of array2

Question

I wonder if there is a numpy natural way of creating a binary mask over array1 for elements that are also in array2. Another way to say it, binary mask over array1 for intersection of array1 and 2.

This works:

def bin_mask(a, b):
    return sum(a==n for n in b)

a = np.array([1,2,3,4,5,6,7,8,9,20])
b = np.array([3,5,7])

In: bin_mask(a,b)
Out: array([0, 0, 1, 0, 1, 0, 1, 0, 0, 0])

But I wonder if there is some numpy prebuilt I am missing.

Edit: Correct answer from comments: np.isin(a, b). I also marked in1d as the correct answer. Both work.

np.isin(a, b).view('i1')

user3483203
– user3483203

2020-02-13 18:31:40 +00:00
Commented Feb 13, 2020 at 18:31 — user3483203
– user3483203, Commented Feb 13, 2020 at 18:31

mathfux · Accepted Answer · 2020-02-13 19:33:44Z

1

in1d method does the trick too:

>>> np.in1d(a,b)
array([False, False,  True, False,  True, False,  True, False, False, False])
>>> np.in1d(a,b).astype(int)    
array([0, 0, 1, 0, 1, 0, 1, 0, 0, 0])

answered Feb 13, 2020 at 19:33

mathfux

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