You can save some homemade factorial functions on a separate module, utils.py, and then import them and compare the performance with the predefinite one, in scipy, numpy and math using timeit. In this case I used as external method the last proposed by Stefan Gruenwald:
import numpy as np
def factorial(n):
return reduce((lambda x,y: x*y),range(1,n+1))
Main code (I used a framework proposed by JoshAdel in another post, look for how-can-i-get-an-array-of-alternating-values-in-python):
from timeit import Timer
from utils import factorial
import scipy
n = 100
# test the time for the factorial function obtained in different ways:
if __name__ == '__main__':
setupstr="""
import scipy, numpy, math
from utils import factorial
n = 100
"""
method1="""
factorial(n)
"""
method2="""
scipy.math.factorial(n) # same algo as numpy.math.factorial, math.factorial
"""
nl = 1000
t1 = Timer(method1, setupstr).timeit(nl)
t2 = Timer(method2, setupstr).timeit(nl)
print 'method1', t1
print 'method2', t2
print factorial(n)
print scipy.math.factorial(n)
Which provides:
method1 0.0195569992065
method2 0.00638914108276
93326215443944152681699238856266700490715968264381621468592963895217599993229915608941463976156518286253697920827223758251185210916864000000000000000000000000
93326215443944152681699238856266700490715968264381621468592963895217599993229915608941463976156518286253697920827223758251185210916864000000000000000000000000
Process finished with exit code 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))
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)
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.
Source: Stackoverflow.com