PERF: groupby slower than pure numpy (for many aggregation functions)
@ml31415 and I have just created/updated an [aggregation package](https://github.com/ml31415/accumarray) which has multiple equivalent implementations: pure python, numpy, pandas, and scipy.weave.  As shown on the readme, pandas is slower than a careful numpy implementation for most aggregation functions, and slower than scipy.weave by a fairly wide margin in all cases.  The worst functions appear to be `any` and `all`.  Note that the times shown include construction of the dataframe, although whatever overhead this incurs cannot be more than the minimum time shown (11ms for `groupby.first()`).

Surely pandas should be able to more or less match at least the numpy implementation, and maybe even the weave version (since it looks like pandas is also getting at low level C using cython)?

Note that although pandas may end up faster when performing multiple aggregations of the same grouping (e.g. first, last and max), the other implementations also have this potential (admittedly this optimization is yet to be implemented though).
