Handling datetimes in columns
I couldn't see anything like this in the issues list. It's a change in behaviour between `0.13.1` and `master/HEAD` which might be intended, might not :)

Suppose you create a dataframe like this:

``` python
from datetime import datetime
B = pd.DataFrame(data = np.array([[1, datetime(2001, 1, 1)],
                                  [2,datetime(2001, 2, 1)]]), columns=['a', 'b'])
```

In `pandas 0.13.1` you can slice out the date column and you get datetime64 data back:

``` python
B['b'].values
Out[90]: 
array(['2001-01-01T00:00:00.000000000+0000',
       '2001-02-01T00:00:00.000000000+0000'], dtype='datetime64[ns]')
B['b'].values.dtype
Out[93]: dtype('<M8[ns]')
```

but in `master/HEAD` you get

``` python
B['b'].values
Out[97]: 
array([datetime.datetime(2001, 1, 1, 0, 0),
       datetime.datetime(2001, 2, 1, 0, 0)], dtype=object)
B['b'].values.dtype
Out[99]: dtype('O')
```

I noticed the change because some of our tests construct expected values using `datetime` and it no longer matches result (which is constructed indirectly and has Timestamp/datetime64 in it).

I'm not sure if this is a bug/regression or a desirable change in behaviour.. I guess it depends whether you think that all datetime data should be stored as `datetime64` or if you'd prefer to preserve the original type of the data where possible...
