I think there should be a way to take a DataFrame and use the values in its columns as indices into a MultiIndexed Series. Here is an example:

>>> d = pandas.DataFrame({"Let": ["A", "B", "C"], "Num": [1, 2, 3]}) >>> d Let Num 0 A 1 1 B 2 2 C 3 >>> ser = pandas.Series( ... ['a', 'b', 'c', 'd', 'e', 'f'], ... index=pandas.MultiIndex.from_arrays([["A", "B", "C"]*2, [1, 2, 3, 4, 5, 6]]) ... ) >>> ser A 1 a B 2 b C 3 c A 4 d B 5 e C 6 f dtype: object 

With this data, you should be able to do d.map(ser) (or whatever method name instead of map) and get the same result as this:

>>> ser.ix[d.apply(tuple, axis=1)] A 1 a B 2 b C 3 c dtype: object 

You currently cannot do this without converting the rows to tuples (ser[d] gives ValueError: Cannot index with multidimensional key). Converting to tuple is an awkward way to do a very natural task, which is using a tabular array of data to look up values in a tabular index (i.e., a MultiIndex).

I'm creating an issue to resurrect this request from much earlier discussion here and here.