My timings in Ipython are (with a simplier timeit interface):

In [57]: timeit np.zeros_like(x)
1 loops, best of 3: 420 ms per loop

In [58]: timeit np.zeros((12488, 7588, 3), np.uint8)
100000 loops, best of 3: 15.1 µs per loop

When I look at the code with IPython (np.zeros_like??) I see:

res = empty_like(a, dtype=dtype, order=order, subok=subok)
multiarray.copyto(res, 0, casting='unsafe')

while np.zeros is a blackbox - pure compiled code.

Timings for empty are:

In [63]: timeit np.empty_like(x)
100000 loops, best of 3: 13.6 µs per loop

In [64]: timeit np.empty((12488, 7588, 3), np.uint8)
100000 loops, best of 3: 14.9 µs per loop

So the extra time in zeros_like is in that copy.

In my tests, the difference in assignment times (x[]=1) is negligible.

My guess is that zeros, ones, empty are all early compiled creations. empty_like was added as a convenience, just drawing shape and type info from its input. zeros_like was written with more of an eye toward easy programming maintenance (reusing empty_like) than for speed.

np.ones and np.full also use the np.empty ... copyto sequence, and show similar timings.

https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/array_assign_scalar.c appears to be file that copies a scalar (such as 0) to an array. I don't see a use of memset.

https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/alloc.c has calls to malloc and calloc.

https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/ctors.c - source for zeros and empty. Both call PyArray_NewFromDescr_int, but one ends up using npy_alloc_cache_zero and the other npy_alloc_cache.

npy_alloc_cache in alloc.c calls alloc. npy_alloc_cache_zero calls npy_alloc_cache followed by a memset. Code in alloc.c is further confused with a THREAD option.

More on the calloc v malloc+memset difference at: Why malloc+memset is slower than calloc?

But with caching and garbage collection, I wonder whether the calloc/memset distinction applies.

This simple test with the memory_profile package supports the claim that zeros and empty allocate memory 'on-the-fly', while zeros_like allocates everything up front:

N = (1000, 1000) 
M = (slice(None, 500, None), slice(500, None, None))

Line #    Mem usage    Increment   Line Contents
================================================
     2   17.699 MiB    0.000 MiB   @profile
     3                             def test1(N, M):
     4   17.699 MiB    0.000 MiB       print(N, M)
     5   17.699 MiB    0.000 MiB       x = np.zeros(N)   # no memory jump
     6   17.699 MiB    0.000 MiB       y = np.empty(N)
     7   25.230 MiB    7.531 MiB       z = np.zeros_like(x) # initial jump
     8   29.098 MiB    3.867 MiB       x[M] = 1     # jump on usage
     9   32.965 MiB    3.867 MiB       y[M] = 1
    10   32.965 MiB    0.000 MiB       z[M] = 1
    11   32.965 MiB    0.000 MiB       return x,y,z