Apply numpy nonzero row-wise?

I did not quite understand what you wanted (maybe an example would help), but two guesses:

If you want to see if there are any Trues on a row, then:

np.any(a, axis=1)

will give you an array with boolean value for each row.

Or if you want to get the indices for the Trues row-by-row, then

testarray = np.array([
    [True, False, True],
    [True, True, False],
    [False, False, False],
    [False, True, False]])

collists = [ np.nonzero(t)[0] for t in testarray ]

This gives:

>>> collists
[array([0, 2]), array([0, 1]), array([], dtype=int64), array([1])]

If you want to know the indices of columns with a True on row 3, then:

>>> collists[3]
array([1])  

There is no pure array-based way of accomplishing this because the number of items on each row varies. That is why we need the lists. On the other hand, the performance is decent, I tried it with a 10000 x 10000 random boolean array, and it took 774 ms to complete the task.