df.groupby(col).resample('x') returning both unstacked or multiindex result
``` python
import pandas as pd
from itertools import cycle, islice
N = 4
df = pd.DataFrame(index=pd.date_range('2000', periods=N))
df['col1'] = list(islice(cycle(['A', 'B']), N))
df['col2'] = list(islice(cycle(['a', 'b', 'c']), N))
print(df)
```

returns

```
           col1 col2
2000-01-01    A    a
2000-01-02    B    b
2000-01-03    A    c
2000-01-04    B    a
```

If I then do the following groupbys, lines 2 and 4 return the same result (a multiindex), but line 3 returns the result of running `.unstack` on line 1.

``` python
print(df.groupby(['col1', pd.TimeGrouper('W')]).size())  # 1. unstacked
print(df.groupby(['col2', pd.TimeGrouper('W')]).size())  # 2. unstacked
print(df.groupby('col1').resample('W').size())  # 3. multiindex
print(df.groupby('col2').resample('W').size())  # 4. unstacked
```

```
col1
A     2000-01-02    1
      2000-01-09    1
B     2000-01-02    1
      2000-01-09    1
dtype: int64
col2
a     2000-01-02    1
      2000-01-09    1
b     2000-01-02    1
c     2000-01-09    1
dtype: int64
      2000-01-02  2000-01-09
col1
A              1           1
B              1           1
col2
a     2000-01-02    1
      2000-01-09    1
b     2000-01-02    1
c     2000-01-09    1
dtype: int64
```

This seems like an inconsistency to me, in the sense that, if 1 and 3 do not return the same result, neither should 2 and 4. Is this a bug, or is `df.groupby(col).resample('x')` supposed to behave like this, and is it supposed to behave differently to `df.groubpy[(col, pd.TimeGrouper(x)])`?

Note that in `df` col1 has two groups of identical size, while col2 has unequal sized groups. I'm not sure if that's the problem, but this inconsistency goes away for some choices of `N` (e.g. all four lines above return multiindex for `N=3` and `N=24`?!)

``` python
print(df.groupby('col1').size())
print(df.groupby('col2').size())
```

```
col1
A    2
B    2
dtype: int64
col2
a    2
b    1
c    1
dtype: int64
```
