PERF: performance regression in 0.13.0 release
One of the projects in my workplace that uses `pandas` a lot has hit a performance regression (~50%) when I was trying to upgrade from `0.12.0` to `0.13.0`. I've double-checked with

``` python
In [1]: pd.__version__
Out[1]: '0.13.0-470-gf0c4953'
```

and the regression is still there. I've skimmed through recent tickets but didn't find anything open about performance, so I'm filing a new one. `0.12.0` package was provided by `conda`. We don't do heavy math with `pandas`, just usual data aggregation/filtering, but I've included `bottleneck` & `numexpr` just to be sure. `0.13.0+` was built into a clone of `0.12.0` environment with `python setup.py develop`.

Here's a list of top pandas modules by total time spent as reported by `runsnake`:

```
filename                0.12    0.13    diff
tseries/index.py        11.7    13.4    14.53%
core/common.py          14.6    25      71.23%
core/frame.py           15.4    15.2    -1.30%
core/series.py          22.5    35.7    58.67%
core/index.py           28.3    44.9    58.66%
core/internals.py       29.6    53      79.05%
core/generic.py         5.5     24      336.36%
```

I've put profiles restricted to `pandas` modules to gdrive  [here (0.12.0)](https://drive.google.com/file/d/0B_ZtkLy4UXstRk1ZalNROTZsOU0/edit?usp=sharing) and [here (0.13.0+)](https://drive.google.com/file/d/0B_ZtkLy4UXstcG42akoxdHcybTQ/edit?usp=sharing).

While most of this is explicable by, for example, `Series` having changed to subclass `NDArray` instead of `np.ndarray`, a performance hit like this makes it virtually impossible to talk my colleagues into upgrading.

Cheers
