Time performance: `a.prod()` vs. `reduce(lambda x,y:x*y, a)`

gistfile1.py

In [1]: import numpy as np

In [2]: a = np.r_[.1:4:40j]

In [3]: a
Out[3]: 
array([ 0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,
        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,
        2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,
        3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ])

In [4]: timeit reduce(lambda x,y:x*y, a)
100000 loops, best of 3: 18.1 us per loop

In [5]: timeit a.prod()
1000000 loops, best of 3: 1.88 us per loop

In [6]: reduce(lambda x,y:x*y, a)
Out[6]: 81591528.324789688

In [7]: a.prod()
Out[7]: 81591528.324789688

In [8]: timeit np.prod(a)
100000 loops, best of 3: 2.4 us per loop