Grouping by pd.cut() results creates a categorical index
After upgrading from 0.16.0 to 0.16.1, some of my plotting stopped functioning right, and after debugging I figured out that it was an issue with the groupby results---namely, that they're getting a `CategoricalIndex` even though they're being grouped by a number.

This is basically what I do in my code (simplified for this example):

```
df = pd.DataFrame()
df['x'] = 100 * np.random.random(100)
df['y'] = df.x**2

binedges = np.arange(0,110,10)
binlabels = np.arange(5,105,10)

z = df.groupby(pd.cut(df.x,bins=binedges ,labels=binlabels )).y.mean()
```

And here's what that produces:

```
x
5       21.809599
15     215.432515
25     556.803743
35    1282.261483
45    1941.166247
55    3104.923704
65    4256.942052
75    5862.686054
85    7304.835127
95    8810.194492
Name: y, dtype: float64
```

When I tried plotting this, the scale was all messed up---it was treating each of the 10 bins as an element of `[0,1,2,...,9]`, which wasn't apparent until I tried changing the `xticks` parameter and everything went screwy. Took a while to debug, but finally found the culprit, by looking at `z.index`:

```
CategoricalIndex([5, 15, 25, 35, 45, 55, 65, 75, 85, 95], categories=[5, 15, 25, 35, 45, 55, 65, 75, ...], ordered=True, name=u'x', dtype='category')
```

This isn't the desired behavior, right? I know the `CategoricalIndex` feature is new, but I don't think this was an intended feature. This same code previously produced an `Int64Index` that would plot just fine, and there's no use case I can see for changing that. Or if that was what was intended, is there any way to select an `Int64Index` instead (other than converting it after the fact)?

UPDATE: Also note that this screws up using pd.cut() to create bins in a dataframe, because the indices don't line up afterward. So doing something like

```
df['bin'] = pd.cut(df.x,bins=mybins,labels=mylabels)
```

will just result in an empty column. So I can't imagine that this is the correct behavior.
