You can go with @greenAfrican example, if it's possible for you to rewrite your function. But if you don't want to rewrite your function, you can wrap it into anonymous function inside apply, like this:

>>> def fxy(x, y):
...     return x * y

>>> df['newcolumn'] = df.apply(lambda x: fxy(x['A'], x['B']), axis=1)
>>> df
    A   B  newcolumn
0  10  20        200
1  20  30        600
2  30  10        300

It's just the way you think it would be, apply accepts args and kwargs and passes them directly to some_func.

df.apply(some_func, var1='DOG', axis=1)

Or,

df.apply(some_func, args=('DOG', ), axis=1)

0    foo-x-DOG
1    bar-y-DOG
dtype: object

If for any reason that won't work for your use case, then you can always fallback to using a lambda:

df.apply(lambda row: some_func(row, 'DOG'), axis=1)

0    foo-x-DOG
1    bar-y-DOG
dtype: object

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:

>>> import functools
>>> import operator
>>> add_3 = functools.partial(operator.add,3)
>>> add_3(2)
5
>>> 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.