The answer for Ashwini is great, in pointing out that scipy.math.factorial, numpy.math.factorial, math.factorial are the same functions. However, I'd recommend use the one that Janne mentioned, that scipy.special.factorial is different. The one from scipy can take np.ndarray as an input, while the others can't.

In [12]: import scipy.special

In [13]: temp = np.arange(10) # temp is an np.ndarray

In [14]: math.factorial(temp) # This won't work
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-14-039ec0734458> in <module>()
----> 1 math.factorial(temp)

TypeError: only length-1 arrays can be converted to Python scalars

In [15]: scipy.special.factorial(temp) # This works!
Out[15]: 
array([  1.00000000e+00,   1.00000000e+00,   2.00000000e+00,
         6.00000000e+00,   2.40000000e+01,   1.20000000e+02,
         7.20000000e+02,   5.04000000e+03,   4.03200000e+04,
         3.62880000e+05])

So, if you are doing factorial to a np.ndarray, the one from scipy will be easier to code and faster than doing the for-loops.

Maybe you could look into increasing the efficiency using table lookups if space efficiency isn't a major concern. It would greatly reduce the number of repeated calculations. The following isn't terribly efficient, but it's the basic idea.

cache = {1:1}
def cached_factorial(n):
    if (n in cache):
        return cache[n]
    else:
        result = n * cached_factorial(n-1)
        cache[n] = result
        return result