DataFrame.values not a 2D-array when constructed from timezone-aware datetimes
When a DataFrame column is constructed from timezone-aware datetime objects, its `values` attribute returns a `pandas.DatetimeIndex` instead of a 2D numpy array. This is problematic because the datetime index does not support all operations that a numpy array does.
#### Code Sample, a copy-pastable example if possible

``` python
import datetime
import dateutil
import pandas
import numpy as np
df = pandas.DataFrame()
df['Time'] = [datetime.datetime(2015,1,1,tzinfo=dateutil.tz.tzutc())]
df.dropna(axis=0) # raises ValueError: 'axis' entry is out of bounds
```

Also, `print df.values` returns `DatetimeIndex(['2015-01-01'], dtype='datetime64[ns, UTC]', freq=None)`.
#### Expected Output

The `df.dropna` call should be a no-op.

Compare this to the case when constructed using `df['Time'] = [datetime.datetime(2015,1,1)]`. In that case, `df.dropna` works as expected, and `df.values` is `array([['2014-12-31T16:00:00.000000000-0800']], dtype='datetime64[ns]')`.
#### output of `pd.show_versions()`

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.18.1
nose: None
pip: 8.0.2
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
