TimeSeries integration optimization
Currently, using scipy.integrate to numerically integrate a pandas TimeSeries incurs quite a large overhead. I sketched up a quick benchmark in [this notebook](http://nbviewer.ipython.org/5512857). This overhead is causing me quite a lot of trouble in my personal work where this integration is the main bottleneck (sometimes lasting minutes on my data, whereas the rest of the analysis executes lightning-fast) and I feel that it shouldn't be that big of a deal since it more-or-less boils down to summing up values.

I already talked about the issue with the scipy folks, to see if it would maybe be feasible to dynamically detect if it's a pandas.TimeSeries and rely on datetime operations, instead of making the user convert the domain to 1 int = 1 second. This would complicate their code a bit, but there would still be only one integration codebase to maintain.

Another option would be to copy the portion of the code regarding sample-based numerical integration (which isn't too complicated - see e.g. [the cumtrapz function](https://github.com/scipy/scipy/blob/master/scipy/integrate/quadrature.py#L245)) into pandas and modify it to work faster.

What is your opinion on the issue I am having? Do you maybe have some idea of how and where to solve it? I would like to hack on the code myself, so if you could point me in the right direction, it would be great.
