BUG: Series ops with a rhs of a Timestamp raising exception (#2898)
- Series ops with a rhs of a Timestamp was throwing an exception (#2898)
- fixed issue in _index.convert_scalar where the rhs was a non-scalar, lhs was dtype of M8[ns], and was trying
  to convert to a scalar (but didn't need conversion)
- added some utilities in tslib.pyx, inference.pyx to detect and convert timedelta/timedelta64
- added timedelta64 to checknull routines, representing timedelta64 as NaT
- aded setitem support to set NaT via np.nan (analagously to datetime64)
- added ability for Series to detect and set a `timedelta64[ns]` dtype (if all passed objects are timedelta like)
- added correct printing of timedelta64[ns](looks like py3.3 changed the default for str%28x%29, so rolled a new one)

```
In [149]: from datetime import datetime, timedelta

In [150]: s  = Series(date_range('2012-1-1', periods=3, freq='D'))

In [151]: td = Series([ timedelta(days=i) for i in range(3) ])

In [152]: df = DataFrame(dict(A = s, B = td))

In [153]: df
Out[153]: 
                    A                B
0 2012-01-01 00:00:00          0:00:00
1 2012-01-02 00:00:00   1 day, 0:00:00
2 2012-01-03 00:00:00  2 days, 0:00:00

In [154]: df['C'] = df['A'] + df['B']

In [155]: df
Out[155]: 
                    A                B                   C
0 2012-01-01 00:00:00          0:00:00 2012-01-01 00:00:00
1 2012-01-02 00:00:00   1 day, 0:00:00 2012-01-03 00:00:00
2 2012-01-03 00:00:00  2 days, 0:00:00 2012-01-05 00:00:00

In [156]: df.dtypes
Out[156]: 
A     datetime64[ns]
B    timedelta64[ns]
C     datetime64[ns]
Dtype: object

In [60]: s - s.max()
Out[60]: 
0    -2 days, 0:00:00
1     -1 day, 0:00:00
2             0:00:00
Dtype: timedelta64[ns]

In [61]: s - datetime(2011,1,1,3,5)
Out[61]: 
0    364 days, 20:55:00
1    365 days, 20:55:00
2    366 days, 20:55:00
Dtype: timedelta64[ns]

In [62]: s + timedelta(minutes=5)
Out[62]: 
0   2012-01-01 00:05:00
1   2012-01-02 00:05:00
2   2012-01-03 00:05:00
Dtype: datetime64[ns]

In [160]: y = s - s.shift()

In [161]: y
Out[161]: 
0              NaT
1   1 day, 0:00:00
2   1 day, 0:00:00
Dtype: timedelta64[ns]

The can be set to NaT using np.nan analagously to datetimes

In [162]: y[1] = np.nan

In [163]: y
Out[163]: 
0              NaT
1              NaT
2   1 day, 0:00:00
Dtype: timedelta64[ns]

# works on lhs too
In [64]: s.max() - s
Out[64]: 
0    2 days, 0:00:00
1     1 day, 0:00:00
2            0:00:00
Dtype: timedelta64[ns]

In [65]: datetime(2011,1,1,3,5) - s
Out[65]: 
0    -365 days, 3:05:00
1    -366 days, 3:05:00
2    -367 days, 3:05:00
Dtype: timedelta64[ns]

In [66]: timedelta(minutes=5) + s
Out[66]: 
0   2012-01-01 00:05:00
1   2012-01-02 00:05:00
2   2012-01-03 00:05:00
Dtype: datetime64[ns]
```

anybody have an issue with the API change vai `unique`, e.g. that it now returns a `Series` rather than an ndarray?
 I do. I would rather users use `drop_duplicates` than `unique` to achieve this
 prob is I was using this with a datetime64[ns] columns
since it is not boxed as a ts, I think it's a bit unexpected (though correct)
 all fixed up, restored unique behavior

fyi...py3 does funny things with np.timedelta64...., its auto converts them to int when you astype('O')
(which no other python version does!)
 @y-p when you have a chance, can you take a look at test_timedelta in tests/test_format.py. I had to put in my own formatter to handle NaT and py3.3 change in the way printing works for timedelta64. It works and returns a string. Not sure if I need to deal with unicode in any way? thanks
 you're returning a string tha always contains pure ascii characters,
that should be ok. 
python2 would try to convert that to unicode as needed using the system 
encoding, and that should succeed on all encodings I've seen.
 great thanks
 @wesm any problem with merging this in?
 Putting example from docs here, for readers of RELEASE.txt

```
  - Timedeltas are now fully operational (closes GH2898_)

    .. ipython:: python

        from datetime import datetime, timedelta
        s  = Series(date_range('2012-1-1', periods=3, freq='D'))
        td = Series([ timedelta(days=i) for i in range(3) ])
        df = DataFrame(dict(A = s, B = td))
        df
        s - s.max()
        df['C'] = df['A'] + df['B']
        df
        df.dtypes

        # timedelta are representas as ``NaT``
        y = s - s.shift()
    y

    # can be set via ``np.nan``
    y[1] = np.nan
    y

        # works on lhs too
    s.max() - s

        # some timedelta numeric operations are supported
        td - timedelta(minutes=5,seconds=5,microseconds=5)
```
