Groupby on date component results in corrupt DataFrame
#### Code Sample, a copy-pastable example if possible

```python
In [3]: df1 = pd.DataFrame({c : np.random.random(4) for c in ['a', 'b']})

In [4]: date = pd.DataFrame({'day' : 15, 'month' : df1.index + 1, 'year' : 2009})

In [5]: df1['date'] = pd.to_datetime(date)

In [6]: df1['month'] = df1['date'].dt.month

In [7]: df2 = df1.groupby('month').max()

In [8]: df2.transpose().mean()
Out[8]: 
Series([], dtype: float64)
```
#### Problem description

``df2`` had strictly positive dimensions, its mean cannot be an empty ``Series``.

#### Expected Output

The same as
``` python
In [9]: df2.mean(axis=1)
Out[9]: 
month
1     0.649630
2     0.392446
3     0.693638
12    0.381830
dtype: float64
```

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8

pandas: 0.19.0+603.g2cad4dd0b
pytest: 3.0.6
pip: 9.0.1
setuptools: 33.1.1
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
xarray: 0.9.1
IPython: 5.1.0.dev
sphinx: 1.4.9
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: 0.2.1

</details>
