Problems with snap (closest index) indexing
Say we have:

```
df = pd.Series(index=pd.DatetimeIndex(['2016-04-06 17:21:17', '2016-04-06 17:23:06',                                                                        
               '2016-04-06 17:25:50', '2016-04-06 17:26:10',     
               '2016-04-06 17:30:09', '2016-04-06 17:30:47',     
               '2016-04-06 17:35:51', '2016-04-06 17:40:48',     
               '2016-04-06 17:45:55', '2016-04-06 17:50:48'],    
              dtype='datetime64[ns]', freq=None), data=range(10))
```

which results in:

```
2016-04-06 17:21:17    0
2016-04-06 17:23:06    1
2016-04-06 17:25:50    2
2016-04-06 17:26:10    3
2016-04-06 17:30:09    4
2016-04-06 17:30:47    5
2016-04-06 17:35:51    6
2016-04-06 17:40:48    7
2016-04-06 17:45:55    8
2016-04-06 17:50:48    9
dtype: int64
```

Now, say I want to index the **closest** values between:

```
from_ix = pd.Timestamp('2016-04-06 17:40:00')
to_ix   = pd.Timestamp('2016-04-06 17:50:00')
```

If I do:

```
> df.loc[from_ix:to_ix]

2016-04-06 17:40:48    7
2016-04-06 17:45:55    8
```

I don't get the closest value to my indices. 

Instead I am looking for:

```
2016-04-06 17:40:48    7
2016-04-06 17:45:55    8
2016-04-06 17:50:48    9
```

How can I do that in Pandas?

I thought the following would work, but it fails

```
> df.index.get_loc(from_ix)
KeyError: Timestamp('2016-04-06 17:40:00')
```
