Numpy: change max in each row to 1, all other numbers to 0

Question

I\'m trying to implement a numpy function that replaces the max in each row of a 2D array with 1, and all other numbers with zero:

>>> a = np.array(         


        

Answer 1

a==np.max(a) will raise an error in the future, so here's a tweaked version that will continue to broadcast correctly.

I know this question is pretty ancient, but I think I have a decent solution that's a bit different from the other solutions.

# get max by row and convert from (n, ) -> (n, 1) which will broadcast
row_maxes = a.max(axis=1).reshape(-1, 1)
np.where(a == row_maxes, 1, 0)
np.where(a == row_maxes).astype(int)

if the update needs to be in place, you can do

a[:] = np.where(a == row_maxes, 1, 0)

Answer 2

Method #1, tweaking yours:

>>> a = np.array([[0, 1], [2, 3], [4, 5], [6, 7], [9, 8]])
>>> b = np.zeros_like(a)
>>> b[np.arange(len(a)), a.argmax(1)] = 1
>>> b
array([[0, 1],
       [0, 1],
       [0, 1],
       [0, 1],
       [1, 0]])

[Actually, range will work just fine; I wrote arange out of habit.]

Method #2, using max instead of argmax to handle the case where multiple elements reach the maximum value:

>>> a = np.array([[0, 1], [2, 2], [4, 3]])
>>> (a == a.max(axis=1)[:,None]).astype(int)
array([[0, 1],
       [1, 1],
       [1, 0]])

Answer 3

I prefer using numpy.where like so:

a[np.where(a==np.max(a))] = 1