np.full(size, 0) vs. np.zeros(size) vs. np.empty()

Question

If you were to choose one of the following three ways of initializing an array with zeros which one would you choose and why?

my_arr_1 = np.full(size, 0)  

or

my_arr_2 = np.zeros(size) 

or

my_arr_3 = np.empty(size) my_arr_3[:] = 0 

Answer 1

I'd use np.zeros, because of its name. I would never use the third idiom because

1. it takes two statements instead of a single expression and

2. it's harder for the NumPy folks to optimize. In fact, in NumPy 1.10, np.zeros is still the fastest option, despite all the optimizations to indexing:

>>> %timeit np.zeros(1e6) 1000 loops, best of 3: 804 µs per loop >>> %timeit np.full(1e6, 0) 1000 loops, best of 3: 816 µs per loop >>> %timeit a = np.empty(1e6); a[:] = 0 1000 loops, best of 3: 919 µs per loop 

Bigger array for comparison with @John Zwinck's results:

>>> %timeit np.zeros(1e8) 100000 loops, best of 3: 9.66 µs per loop >>> %timeit np.full(1e8, 0) 1 loops, best of 3: 614 ms per loop >>> %timeit a = np.empty(1e8); a[:] = 0 1 loops, best of 3: 229 ms per loop