Find where a NumPy array is equal to any value in a list of values

Question

I have an array of integers and want to find where that array is equal to any value in a list of multiple values.

This can easily be done by treating each value individually, or by using multiple "or" statements in a loop, but I feel like there must be a better/faster way to do it. I'm actually dealing with arrays of size 4000 x 2000, but here is a simplified edition of the problem:

fake = arange(9).reshape((3,3))

array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

want = (fake==0) + (fake==2) + (fake==6) + (fake==8)

print want 

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

What I would like is a way to get want from a single command involving fake and the list of values [0, 2, 6, 8].

I'm assuming there is a package that has this included already that would be significantly faster than if I just wrote a function with a loop in Python.

Answer 1

The function numpy.in1d seems to do what you want. The only problems is that it only works on 1d arrays, so you should use it like this:

In [9]: np.in1d(fake, [0,2,6,8]).reshape(fake.shape)
Out[9]: 
array([[ True, False,  True],
       [False, False, False],
       [ True, False,  True]], dtype=bool)

I have no clue why this is limited to 1d arrays only. Looking at its source code, it first seems to flatten the two arrays, after which it does some clever sorting tricks. But nothing would stop it from unflattening the result at the end again, like I had to do by hand here.