groupby() syntactic sugar not working correctly in DataFrame form
In Wes' "Python for Data Analysis" book it says on the bottom of page 256 that:

``` python
    df.groupby('key1')['data1']
    df.groupby('key1')[['data2']]
```

are syntactic sugar for:

``` python
    df['data1'].groupby(df['key1'])
    df[['data2']].groupby(df['key1'])
```

I find that the first form works but the second one doesn't, in particular the
restriction on the data columns doesn't occurr in the second form. Here is some
example code:

``` python
In [33]: import pandas as pd

In [34]: df = pd.DataFrame(dict(key1=[0]*3+[1]*3, data1=range(6), data2=range(3)*2))

In [35]: print df
   data1  data2  key1
0      0      0     0
1      1      1     0
2      2      2     0
3      3      0     1
4      4      1     1
5      5      2     1

In [36]: expected = df['data1'].groupby(df['key1']).count()

In [37]: print expected
key1
0       3
1       3

In [38]: actual = df.groupby('key1')['data1'].count()

In [39]: print actual
key1
0       3
1       3

In [40]: expected2 = df[['data2']].groupby(df['key1']).count()

In [41]: print expected2
      data2
key1       
0         3
1         3

In [42]: actual2 = df.groupby('key1')[['data2']].count()

In [43]: print actual2
      data1  data2  key1
key1                    
0         3      3     3
1         3      3     3

In [44]: print pd.__version__
0.10.1
```
