BUG: DataFrame.groupby() on tuple column works only when column name is "key"
This is the weirdest bug I have seen in Pandas. But I am guessing (hoping) the fix will not be too difficult.

#### Code Sample

Consider the following two code blocks:

**Block 1: key column is called "k"**
```
>>> df1 = pd.DataFrame({'x' : [1,2,3,4,5]*3,
                        'y' : [10,20,30,40,50]*3,
                        'z' : [100,200,300,400,500]*3})
>>> df1['k'] = [(0,0,1),(0,1,0),(1,0,0)]*5
```
**Block 2: key column is called "key"**
```
>>> df2 = pd.DataFrame({'x' : [1,2,3,4,5]*3,
                        'y' : [10,20,30,40,50]*3,
                        'z' : [100,200,300,400,500]*3})
>>> df2['key'] = [(0,0,1),(0,1,0),(1,0,0)]*5
```
Note that the same, static data is used, so that nothing else may be different, and hence culpable.

#### Problem description

Running a simple `.groupby().describe()` operation produces the following results:

```
>>> df1.groupby('k').describe()
# No Result

>>> df2.groupby('key').describe()
			x		y		z
key				
(0, 0, 1)	count	5.000000	5.000000	5.000000
         	mean	3.000000	30.000000	300.000000
         	std	1.581139	15.811388	158.113883
         	min	1.000000	10.000000	100.000000
         	25%	2.000000	20.000000	200.000000
         	50%	3.000000	30.000000	300.000000
         	75%	4.000000	40.000000	400.000000
         	max	5.000000	50.000000	500.000000
(0, 1, 0)	count	5.000000	5.000000	5.000000
         	mean	3.000000	30.000000	300.000000
         	std	1.581139	15.811388	158.113883
         	min	1.000000	10.000000	100.000000
         	25%	2.000000	20.000000	200.000000
         	50%	3.000000	30.000000	300.000000
         	75%	4.000000	40.000000	400.000000
         	max	5.000000	50.000000	500.000000
(1, 0, 0)	count	5.000000	5.000000	5.000000
         	mean	3.000000	30.000000	300.000000
         	std	1.581139	15.811388	158.113883
         	min	1.000000	10.000000	100.000000
         	25%	2.000000	20.000000	200.000000
         	50%	3.000000	30.000000	300.000000
         	75%	4.000000	40.000000	400.000000
         	max	5.000000	50.000000	500.000000
```

Note that `groupby().mean()`, `sum()`, and a few others work fine. `describe()` is the only one I think is causing the problem.

#### Expected Output

Obviously, the expected output for `df1.groupby('k').describe()` should be the same as `df2.groupby('key').describe()`.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.4
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None

</details>
