According to me
Use python's methods (sum()) on python datatypes and use NumPy's methods on NumPy arrays (np.sum()).

massive_array=np.random.random(100000)
massive_array.size

100000

massive_array

array([0.81947279, 0.24254041, 0.76437261, ..., 0.15969415, 0.34502387,
0.15858268])

%timeit sum(massive_array) #Python sum
%timeit np.sum(massive_array) #Numpy sum

16 ms ± 494 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
50.6 µs ± 4.83 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

That's a massive difference!!

What do you guys think???