I don’t believe apply has access to the index; it treats each row as a numpy object, not a Series, as you can see:
In [27]: s.apply(lambda x: type(x))
Out[27]:
a b
1 2 <type 'numpy.float64'>
3 6 <type 'numpy.float64'>
4 4 <type 'numpy.float64'>
To get around this limitation, promote the indexes to columns, apply your function, and recreate a Series with the original index.
Series(s.reset_index().apply(f, axis=1).values, index=s.index)
Other approaches might use s.get_level_values, which often gets a little ugly in my opinion, or s.iterrows(), which is likely to be slower — perhaps depending on exactly what f does.