Numpy add with multiple arrays

Question

Is there a way to add (as opposed to sum) multiple arrays together in a single operation? Obviously, np.sum and np.add are different operations, however, the problem I'm struggling with right now is that np.add only takes two arrays at once. I could utilize either

output = 0
for arr in arr_list:
    output = output + array

or

output = 0
for arr in arr_list:
    output = np.add(output, array)

and, yes, this is workable. However, it would be nice if I could simply do some variant of

output = np.add_multiple(arr_list)

Does this exist?

EDIT: I failed to be clear initially. I specifically require a function that does not require an array of arrays to be constructed, as my arrays are not of equal dimensions and require broadcasting, for example:

a = np.arange(3).reshape(1,3)
b = np.arange(9).reshape(3,3)

a, b = a[:,:,None,None], b[None,None,:,:]

These work:

a + b        # Works
np.add(a, b) # Works

These do not, and fail with the same exception:

np.sum([a, b], axis = 0)
np.add.reduce([a, b])

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: could not broadcast input array from shape (3,1,1) into shape (1)

Answer 1

You can just use the Python's sum built-in:

output = sum(arr_list)

For many other numpy functions, np.<ufunc>.reduce can be used as shown by @hpaulj.

Answer 2

You can use the sum() to add multiple arrays.

arr = np.array([[6,2,3,5,4,3], 
          [7,7,2,4,6,7],
          [10,6,2,4,5,9]])
np.add(0, arr.sum(axis=0))