Python's Sum or NumPy's np.sum() ???I found a big difference in time!!

import random
import array
import numpy

ar = [random.random() for i in range(100000)]
np_ar = numpy.random.random(100000)
tuple_ar = tuple(ar)
array_ar = array.array('f', ar)

In [26]: %timeit sum(ar)
738 µs ± 117 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [27]: %timeit sum(np_ar)
24.8 ms ± 1.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [28]: %timeit numpy.sum(np_ar)
57.2 µs ± 7.01 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In [32]: %timeit sum(tuple_ar)
667 µs ± 78 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [33]: %timeit numpy.sum(tuple_ar)
5.92 ms ± 1.13 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [34]: %timeit numpy.sum(ar)
5.21 ms ± 334 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [38]: %timeit sum(array_ar)
2.1 ms ± 433 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [39]: %timeit numpy.sum(array_ar)
70.7 µs ± 13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In [40]: %timeit sum(np_ar)
25.2 ms ± 1.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)