Compare with dlply. It solves a similar problem as pandas does but has a vastly simpler API. To be fair, Pandas does do more but dlply is also more flexible. I looked at implementing something like dlply in Python but you really need to have a lazy evaluation syntax. dlply makes extensive use of this feature of R. As the downside, it can be very confusing to new users as it is hard to debug this lazy evaluation code.
Rather than adopting Pandas to build our product, I built a very minimal version of it (on top of numpy) that only does what we need. That was some extra work but I'm happy I did it as we avoid this huge dependency. I understand quite well my little minimal version does, it is only about 1000 lines of Python code and some tiny C extensions.