NaTType does not support .time()
I am currently writing a script to take data from an API and save it to an Excel file for non-technical users who need to access the data. One of the requirements is to have separate date and time column, rather than a single datetime column. 

I am using pandas 0.17.0. 

``` python
import pandas as pd

# creating datetime series
dates = pd.date_range('2015-01-01 00:00', '2015-01-01 03:00', freq='H')
dates = pd.Series(dates)
# setting one record to NaT
dates.iloc[0] = pd.NaT
# converting to date 
dates.apply(lambda x: x.date()) # ignores the NaT
# converting to time
dates.apply(lambda x: x.time()) # fails with error "NaTType does not support time"
```

I expected NaTType to behave in the same way for converting into a time as it did for a date i.e. returning NaT. 

The full error is: 

``` python

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-98-b34fccfc4c06> in <module>()
      9 dates.apply(lambda x: x.date()) # ignores the NaT
     10 # converting to time
---> 11 dates.apply(lambda x: x.time()) # fails with error "NaTType does not support time"

C:\Users\Nathan\Anaconda\lib\site-packages\pandas\core\series.pyc in apply(self, func, convert_dtype, args, **kwds)
   2158             values = lib.map_infer(values, lib.Timestamp)
   2159 
-> 2160         mapped = lib.map_infer(values, f, convert=convert_dtype)
   2161         if len(mapped) and isinstance(mapped[0], Series):
   2162             from pandas.core.frame import DataFrame

pandas\src\inference.pyx in pandas.lib.map_infer (pandas\lib.c:62187)()

```
