Groupby added to original frame has different index based on how it is accessed
So when I use `groupby` and then set it as a column in my original array, everything looks good if I use bracket indexing:

```
In [11]:otms['LastOrd'] = otms.EVENTTIME.where(otms.EVENT=='Submitted')
         otms.LastOrd = otms.groupby(['OPTION','EXCHANGE','BUYSELL', 'TRIGGER',]).LastOrd.shift(1)
         otms.LastOrd = otms.groupby(['OPTION','EXCHANGE','BUYSELL', 'TRIGGER',]).LastOrd.ffill()

In [12]: df['LastOrd'].head(7)
Out[12]: 0               NaT
         1   09:44:17.918848
         2   09:44:17.918848
         3   09:44:17.918848
         4               NaT
         5               NaT
         6   09:44:18.049305
         Name: LastOrd, dtype: timedelta64[ns]
```

That is a `Int64Index`, like the parent DF. But when I try to use dot indexing, it gives me a `MultiIndex` series:

```
In [13]: df.LastOrd.head(7)
Out[13]: 0  1022               NaT
            1024   10:05:08.418368
            1026   10:05:08.418368
         1  7406               NaT
            7407   14:01:59.547820
            7408   14:01:59.547820
         2  1021               NaT
dtype: timedelta64[ns]
```

This is not expected behavior, correct?
