Replace numpy array value on condition with random number

I need to replace some values in a numpy array based on a condition with a random number.

I have a function that adds a random value 50% of the time:

def add_noise(noise_factor=0.5):

    chance = random.randint(1,100)
    threshold_prob = noise_factor * 100.

    if chance <= threshold_prob:
        noise = float(np.random.randint(1,100))
    else:
        noise = 0.

    return(noise)

But when I call the numpy function, it replaces all matching values with the random number generated:

np.place(X, X==0., add_noise(0.5))

The problem with this, is that add_noise() only runs once, and it replaces all the 0. values with the noise value.

What I am trying to do is "iterate" through every element in the numpy array, check the condition (is it ==0.) and I want to generate the noise value through add_noise() every time.

I could do this with a for loop going through every row and column, but does anyone know of a more efficient manner of doing it?