python pandas apply a function with arguments to a series

Question

I want to apply a function with arguments to a series in python pandas   x   my series apply my function  more arguments 1  y   my series apply my function  more arguments 2        The documentation describes support for an apply method  but it doesn t accept any arguments   Is there a different method that accepts arguments   Alternatively  am I missing a simple workaround   Update  October 2017    Note that since this question was originally asked that pandas apply   has been updated to handle positional and keyword arguments and the documentation link above now reflects that and shows how to include either type of argument

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Steps    Create a dataframe Create a function Use the named arguments of the function in the apply statement    Example  x pd DataFrame  1 2 3 4      def add i1  i2         return i1 i2  x apply add i2 9    The outcome of this example is that each number in the dataframe will be added to the number 9         0 0  10 1  11 2  12 3  13   Explanation   The  add  function has two parameters  i1  i2   The first parameter is going to be the value in data frame and the second is whatever we pass to the  apply  function   In this case  we are passing  9  to the apply function using the keyword argument  i2

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You can pass any number of arguments to the function that apply is calling through either unnamed arguments  passed as a tuple to the args parameter  or through other keyword arguments internally captured as a dictionary by the kwds parameter   For instance  let s build a function that returns True for values between 3 and 6  and False otherwise   s   pd Series np random randint 0 10  10   s  0    5 1    3 2    1 3    1 4    6 5    0 6    3 7    4 8    9 9    6 dtype  int64  s apply lambda x  x  gt   3 and x  lt   6   0     True 1     True 2    False 3    False 4     True 5    False 6     True 7     True 8    False 9     True dtype  bool   This anonymous function isn t very flexible  Let s create a normal function with two arguments to control the min and max values we want in our Series   def between x  low  high       return x  gt   low and x   lt  high   We can replicate the output of the first function by passing unnamed arguments to args   s apply between  args  3 6     Or we can use the named arguments  s apply between  low 3  high 6    Or even a combination of both  s apply between  args  3    high 6

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Series apply func  convert dtype True  args       kwds   args   tuple  x   my series apply my function  args    arg1

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Newer versions of pandas do allow you to pass extra arguments  see the new documentation   So now you can do   my series apply your function  args  2 3 4   extra kw 1    The positional arguments are added after the element of the series     For older version of pandas   The documentation explains this clearly  The apply method accepts a python function which should have a single parameter  If you want to pass more parameters you should use functools partial as suggested by Joel Cornett in his comment   An example    gt  gt  gt  import functools  gt  gt  gt  import operator  gt  gt  gt  add 3   functools partial operator add 3   gt  gt  gt  add 3 2  5  gt  gt  gt  add 3 7  10   You can also pass keyword arguments using partial   Another way would be to create a lambda   my series apply  lambda x  your func a b c d     x      But I think using partial is better

[python] python pandas: apply a function with arguments to a series

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