bug in nan-handling in groupby
It looks to me like there's some subtle bug in the nan-handling for groupby. Unfortunately, I can't replicate it. Trying to replicate it, it drops the groups that have nan-keys, as the docs indicate [1].

1) Is it not possible to just compare NaNs in the same way as is handled elsewhere? If not, maybe it's desirable to raise a warning here. This is not what I expected, and I've been working with incorrect results without knowing it.

2) Here's the very odd bug that also led me to have incorrect results. Only evidence that I'm not crazy since I can't replicate. Maybe there will be some hint as to what's going on. Several of the static_variable columns here have NaNs.

```
dta_period = all_dta.groupby(("cityname", "period") +
                            tuple(static_variables))
```

Let's look at one key that definitely has a NaN in it and wasn't dropped.

```
keys = [i for i in dta_period.groups.keys() if i[0] == 'AACHEN']
```

The last category holds a NaN below. It's not dropped and they're not aggregated together into one group as I expected before knowing these were supposed to be dropped.

```
[20]: for i in keys:
....:     print i
....:     
```

(u'AACHEN', 56, 3.0, u'Aachen', 50.783000946, 6.08300018311, 50.7755, 6.0815, 50.4700012207, 6.0500001907300005, u'Small States of the Holy Empire of the German Nation', nan)
(u'AACHEN', 56, 3.0, u'Aachen', 50.783000946, 6.08300018311, 50.7755, 6.0815, 50.4700012207, 6.0500001907300005, u'Small States of the Holy Empire of the German Nation', nan)
(u'AACHEN', 56, 3.0, u'Aachen', 50.783000946, 6.08300018311, 50.7755, 6.0815, 50.4700012207, 6.0500001907300005, u'Small States of the Holy Empire of the German Nation', nan)

Now let's aggregate and see what happens to Aachen.

```
agg = dict(zip(variables + ['books'], [np.sum]*(len(variables)+1)))
df = dta_period.aggregate(agg)

[24]: df.index[0]
[24]: 
(u'AACHEN',
56,
3.0,
u'Aachen',
50.783000946000001,
6.0830001831100002,
50.775500000000001,
6.0815000000000001,
50.470001220699999,
6.0500001907300005,
u'Small States of the Holy Empire of the German Nation',
1065.0)
```

The NaN category in the group now somehow has data in it. Yikes.

[1] https://pandas-docs.github.io/pandas-docs-travis/groupby.html#na-group-handling
