Numpy element-wise addition with multiple arrays

Question

I'd like to know if there is a more efficient/pythonic way to add multiple numpy arrays (2D) rather than:

def sum_multiple_arrays(list_of_arrays):
   a = np.zeros(shape=list_of_arrays[0].shape) #initialize array of 0s
   for array in list_of_arrays:
      a += array
   return a 

Ps: I am aware of np.add() but it works only with 2 arrays.

Answer 1

np.sum(list_of_arrays, axis=0) 

should work. Or

np.add.reduce(list_of_arrays)

Answer 2

The simplest and most Pythonic solution is simply to use sum(), like so:

sum(list_of_arrays)

This may even be faster than other options, at least under some conditions (thanks to Loc Quan for pointing this out!). For example, on my laptop, with Python 3.10.2 and NumPy 2.1.3, I ran the following code:

n = 100000; m = 5; k = 5;
list_of_arrays = [np.random.random_sample((n,m)) for _ in range(k)]

%timeit np.sum(list_of_arrays, axis=0)
7.29 ms ± 459 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit np.add.reduce(list_of_arrays)
7.04 ms ± 134 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit reduce(np.add, list_of_arrays)
2.04 ms ± 86.2 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit sum(list_of_arrays)
1.59 ms ± 44.9 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

Showing a huge advantage to sum() over np.sum() and np.add.reduce(), and a more modest but still significant speedup vs. reduce(np.add, ...).