groupby categorical column fails with unstack
Replicating example

```
In [1]: df = pd.DataFrame([[1,2],[3,4]],columns=pd.CategoricalIndex(list('AB')))

In [2]: df.describe()
AttributeError: 'DataFrame' object has no attribute 'value_counts'
```

The behaviour in [this notebook](https://gist.github.com/williamsmj/c6cbabcb25f27f08407f) seems like a bug to me. This is pandas 0.17.0.

In it, `g` and `gcat` are the results of two `df.groupby(['medium', 'artist']).count().unstack()` operations. The only difference is that one of those operations is on `df` where one of the columns that the `groupby` operates over has been converted to Categorical.

`g` and `gcat` behave very differently. I've tried to pin this down to the exact operation in the split-apply-combine that causes the problem without much luck. 

Slicing a column out of `g` returns a Series as expected, while slicing a column out of `gcat` returns a DataFrame (see cells 4 and 5).

`g.describe()` works as expected, but `gcat.describe()` raises the exception

```
AttributeError: 'DataFrame' object has no attribute 'value_counts'
```

and `g['painting'] + g['sculpture']` works as expected but `g['painting'] + g['sculpture']` raises

```
Exception: Data must be 1-dimensional
```
