Indexing and slicing datetime columns using strings
I deal with DateTime columns extensively. Using strings to slice and select items is extremely useful, but it requires a DatetimeIndex.

I think it would be great to include the same functionality for any column.

Consider this DF:

```
>>> a = pd.DataFrame([pd.to_datetime('2016-4-3 15:32:04'), pd.to_datetime('2016-4-9 15:32:04')], columns=['a_col'])
>>> print(a)

                a_col
0 2016-04-03 15:32:04
1 2016-04-09 15:32:04
```

This is what I usually do, but it's extremely cluttered, and fails when the index is already something useful.

```
>>>print(a.set_index('a_col')['2016-4-3'].reset_index())
                a_col
0 2016-04-03 15:32:04
```

I imagine this is the preferred way of doing the same thing (please correct me if I'm wrong). Requires a lot of typing, and not elegant at all (especially for chosing a single day, as in this example).

```
>>> print(a[(a.a_col >= pd.to_datetime('2016-4-3')) & (a.a_col < pd.to_datetime('2016-4-4'))])
                a_col
0 2016-04-03 15:32:04
```

I think something like the `ds` would be great:

```
>>> print(a.ds['a_col', '2016-4-3'])
```

The second element could be any argument that is accepted to DatetimeIndex elements.

What do you think? Am I missing something?
