Use Pandas groupby() + apply() with arguments

Question

I would like to use df.groupby() in combination with apply() to apply a function to each row per group.

I normally use the following code, which usually works (note, that this is without groupby()):

df.apply(myFunction, args=(arg1,)) 

With the groupby() I tried the following:

df.groupby('columnName').apply(myFunction, args=(arg1,)) 

However, I get the following error:

TypeError: myFunction() got an unexpected keyword argument 'args'

Hence, my question is: How can I use groupby() and apply() with a function that needs arguments?

Answer 1

pandas.core.groupby.GroupBy.apply does NOT have named parameter args, but pandas.DataFrame.apply does have it.

So try this:

df.groupby('columnName').apply(lambda x: myFunction(x, arg1)) 

or as suggested by @Zero:

df.groupby('columnName').apply(myFunction, ('arg1')) 

Demo:

In [82]: df = pd.DataFrame(np.random.randint(5,size=(5,3)), columns=list('abc'))  In [83]: df Out[83]:    a  b  c 0  0  3  1 1  0  3  4 2  3  0  4 3  4  2  3 4  3  4  1  In [84]: def f(ser, n):     ...:     return ser.max() * n     ...:  In [85]: df.apply(f, args=(10,)) Out[85]: a    40 b    40 c    40 dtype: int64 

when using GroupBy.apply you can pass either a named arguments:

In [86]: df.groupby('a').apply(f, n=10) Out[86]:     a   b   c a 0   0  30  40 3  30  40  40 4  40  20  30 

a tuple of arguments:

In [87]: df.groupby('a').apply(f, (10)) Out[87]:     a   b   c a 0   0  30  40 3  30  40  40 4  40  20  30