Speed of np.empty vs np.zeros

Question

I am using numpy version 1.14.3 and python 2.7.12.

Referencing this question, I am finding dramatically different speeds between initializing arrays with np.zeros and np.empty. However, the output is the same.

import numpy as np
r = np.random.random((50, 100, 100))
z = np.zeros(r.shape)
e = np.empty(r.shape)
np.allclose(e, z)

This returns True. However, the timing functions %timeit gives very different results:

%timeit z = np.zeros(r.shape)

10000 loops, best of 3: 143 µs per loop

%timeit e = np.empty(r.shape)

1000000 loops, best of 3: 1.83 µs per loop

The previously accepted answer referenced above says that np.zeros was always the better choice, and that it is faster too.

Why not use np.empty when it is 80 times faster than np.zeros and returns the same answer?

Edit As user2285236 pointed out, flipping the order of initializing z and e will break the equality, because it overwrites on the same memory area.

Answer 1

np.empty and np.zeros do different things.

np.empty creates an array from available memory space, leaving whatever values happened to be hanging around in memory as the values. These values may or may not be zeros.

np.zeros creates an array from available memory space, and then fills it with zeros for your chosen dtype. Obviously np.zeros has to do more work so it should be slower, since it's also writing to the memory allocated.

A more fair comparison would be between np.empty and np.ndarray.