Python numpy bool masks for absolute values

Suppose you have a numpy array(n,n) ie.

x = np.arange(25).reshape(5,5) and you fill x with random integers between -5 and 5. Is there a method to use a boolean mask so that all of my values which are 0 become 1 and all my numbers which are nonzero become zero?(i.e, if [index]>0 or [index]<0, [index]=0, and if [index]=0 then [index]=1)

I know you could use an iteration to change each element, but my goal is speed and as such I would like to eliminate as many loops as possible from the finalized script.

EDIT: Open to other ideas, as well, of course, as long as speed/efficiency is kept in mind

Firstly, you could instantiate your array directly using np.random.randint :

# Note: the lower limit is inclusive while the upper limit is exclusive x = np.random.randint(-5, 6, size=(5, 5))

To actually get the job done, perhaps type-cast to bool, type-cast back, and then negate?

res = 1 - x.astype(bool).astype(int)

Alternatively, you could be a bit more explicit:

x[x != 0] = 1 res = 1 - x

But the second method seems to take more than twice as much time:

>>> n = 1000 >>> a = np.random.randint(-5, 6, (n, n)) >>> %timeit a.astype(bool).astype(int) 1000 loops, best of 3: 1.58 ms per loop >>> %timeit a[a != 0] = 1 100 loops, best of 3: 4.61 ms per loop