Python's sum vs. NumPy's numpy.sum

Question

What are the differences in performance and behavior between using Python\'s native sum function and NumPy\'s numpy.sum? sum works on

Answer 1

I got curious and timed it. numpy.sum seems much faster for numpy arrays, but much slower on lists.

import numpy as np
import timeit

x = range(1000)
# or 
#x = np.random.standard_normal(1000)

def pure_sum():
    return sum(x)

def numpy_sum():
    return np.sum(x)

n = 10000

t1 = timeit.timeit(pure_sum, number = n)
print 'Pure Python Sum:', t1
t2 = timeit.timeit(numpy_sum, number = n)
print 'Numpy Sum:', t2

Result when x = range(1000):

Pure Python Sum: 0.445913167735
Numpy Sum: 8.54926219673

Result when x = np.random.standard_normal(1000):

Pure Python Sum: 12.1442425643
Numpy Sum: 0.303303771848

I am using Python 2.7.2 and Numpy 1.6.1