TimeSeries indexing with not-specific-enough indices only works for strings
For indexing a sub-series of some TimeSeries, one can use indices that don't completely specify the moment. For example, in a Series

```
 s = Series(randn(1000), index=pd.date_range(start='2001-01-23 00:00', periods=1000, freq='H'))
```

I can say s['2001-01-23'] to get a Series for all the values for that day. This, however doesn't work with datetime-like objects. E.g. for a datetime object

```
day = pd.Timestamp('2001-03-05').date()
```

for indexing the Series with  `s[day]` you get a single element such as

```
-1.0028671116269237
```

but for a string representation of the object `s[str(day)]` you get a whole Series such as

```
2001-03-05 00:00:00   -1.002867
2001-03-05 01:00:00    0.416343
2001-03-05 02:00:00   -0.012255
...
2001-03-05 13:00:00   -1.140122
2001-03-05 14:00:00    0.742483
2001-03-05 15:00:00   -0.297421
Freq: H
```

This seems inconsistent and being able to work with objects more abstract than strings would be much nicer.
## Expected behaviour

Strings aren't always very convenient, e.g. for iterating over a TimeSeries by days. It would be nice to be able to use some sort of datetime-like objects to do not-specific-enough indexing and get sub-series - be it Datetimes,Timestamps or Timespans. This would allow us to treat indices as something we can do arithmetic with, adding offsets to move freely etc.
