Pandas 0.13.1 slower than 0.12.0 for pandas.datetime and strange error; expected behavior?
Part of my workflow involves reading in files with dates that I convert into timestamps. The files are big but not huge, say a million lines with 10 columns.

The operation that takes a bit of time is the following:

```
self.SEEDFILE['tmp_refdate'] = self.SEEDFILE[['year','month','day','seconds']].apply(lambda x: pd.datetime(x['year'], x['month'], x['day'],0,0,x['seconds']), axis=1)  
```

As you can see this now takes longer on 0.13.1 as compared to 0.12.0...?
INFO: Numpy version: 1.8.0
INFO: Pandas version: 0.13.1
DEBUG: time for refdate: 61.992943

INFO: Numpy version: 1.8.1
INFO: Pandas version: 0.12.0
DEBUG: time for refdate: 14.616941

Is this expected behavior?

Also, the ability to add a datetime array to a np.timedelta64 objects seems to have died in 0.13.1, this used to work but now I get:

```
TypeError: Argument 'values' has incorrect type (expected numpy.ndarray, got Series)
```

The workaround has been to load the times into a pandas.timestamp using read_csv's time parser options.  Thus, I'm not really posting this looking for a solution. Any thoughts/comments appreciated. I had a look in the 0.13.1 docs and I gather there have been other changes to the datetime interface so perhaps my problems are unfortunate side effects..?
