Factorial in numpy and scipy

Question

How can I import factorial function from numpy and scipy separately in order to see which one is faster    I already imported factorial from python itself by import math  But  it does not work for numpy and scipy

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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   lt ipython-input-14-039ec0734458 gt  in  lt module gt    ---- gt  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

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after running different aforementioned functions for factorial  by different people  turns out that math factorial is the fastest to calculate the factorial   find running times for different functions in the attached image

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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

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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

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You can import them like this   In  7   import scipy  numpy  math                                                            In  8   scipy math factorial  numpy math factorial  math factorial Out 8      lt function math factorial gt                                                                     lt function math factorial gt                                                                     lt function math factorial gt     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

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SciPy has the function scipy special factorial  formerly scipy misc factorial    gt  gt  gt  import math  gt  gt  gt  import scipy special  gt  gt  gt  math factorial 6  720  gt  gt  gt  scipy special factorial 6  array 720 0

[python] Factorial in numpy and scipy

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