Factorial in numpy and scipy

You can import them like this:

In [7]: import scipy, numpy, math                                                          

In [8]: scipy.math.factorial, numpy.math.factorial, math.factorial
Out[8]: 
(<function math.factorial>,                                                                
 <function math.factorial>,                                                                
 <function math.factorial>)

scipy.math.factorial and numpy.math.factorial seem to simply be aliases/references for/to math.factorial, that is scipy.math.factorial is math.factorial and numpy.math.factorial is math.factorial should both give True.

SciPy has the function scipy.special.factorial (formerly scipy.misc.factorial)

>>> import math
>>> import scipy.special
>>> math.factorial(6)
720
>>> scipy.special.factorial(6)
array(720.0)

    from numpy import prod

    def factorial(n):
        print prod(range(1,n+1))

or with mul from operator:

    from operator import mul

    def factorial(n):
        print reduce(mul,range(1,n+1))

or completely without help:

    def factorial(n):
        print reduce((lambda x,y: x*y),range(1,n+1))

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.