groupby on NaN-only column gives IndexError
Using pandas from master (0.16.2+590.g81b647f) in Python 3.4.2, the following code gives an `IndexError: index out of bounds`:

```
import pandas as pd, numpy as np
df = pd.DataFrame(dict(a=[np.nan]*3, b=[1,2,3]))
g = df.groupby(('a', 'b'))
len(g)  # IndexError
```

The same problem occurs when calling `list(g)` instead. Since NaN values are skipped according to the documentation, I guess the correct answer would be zero for `len(g)` and an empty list for `list(g)`.

Strangely, iteration works, so `for x in g: pass` (or `[x for x in g]`) does not give an error (and iterates zero times). Also, `g.count()`, `g.sum()` etc. work (and return an empty DataFrame).

To add to the confusion, `g.groups` gives the dictionary `{(nan, 1): [0], (nan, 2): [1], (nan, 3): [2]}`. Shouldn’t this be empty because group keys with NaNs are dropped?

Grouping only by column 'a' or 'b' works and results in a length of 0 or 3, respectively.
