Apply on selected columns of a groupby object - stopped working with 0.18.1
#### Code Sample

``` python
>>> import pandas as pd
>>> import numpy as np
>>> 
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
...                            'foo', 'bar', 'foo', 'foo'],
...                     'B' : ['one', 'one', 'two', 'three',
...                            'two', 'two', 'one', 'three'],
...                     'C' : np.random.randn(8),
...                     'D' : np.random.randn(8)})
>>> 
>>> gb = df.groupby('B')
>>> 
>>> gb[['C', 'D']].apply(lambda group: group.C.mean() - group.D.mean())

```

Works fine on 0.18.0. 
On 0.18.1 throws:

> TypeError: Series.name must be a hashable type
#### Expected Output

> B
> one      0.609650
> three   -1.027308
> two      0.515237
> dtype: float64

(some other random numbers)
### Comment

With 0.18.1 update, some piece of my code stopped working, in a quite surprising way. 

The code is easy to fix - just by removing [['C', 'D']] and applying on the whole groupby object. 

Nevertheless, its a regression in a point release and I think someone else may run into the same problem.

I am guessing that pandas is trying to generate a name for the result series based on the column list - unfortunately I cannot investigate further right now.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS

commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 24.0.2
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
