Bug? selecting a column for GroupBy
I am reading the book "Python for Data Analysis" and try some examples. But I found something strange.

On page 256, it says:

Quote:

Indexing a GroupBy object created from a DataFrame with a column name or array of column names has the effect of selecting those columns for aggregation. This means that:

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

are syntactic sugar for:

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

I think this makes sense, but when I tried a testing code, it didn't work as expected.

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

Here, it still returns the whole DataFrame. It does not perform the same as 

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

I used "for" loop to iterate the object, maybe this is the reason?

Testing code:

```
df = DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],
                'key2' : ['one', 'two', 'one', 'two', 'one'],
                'data1' : np.random.randn(5),
                'data2' : np.random.randn(5)})
df
```

```
Out[5]:
      data1     data2 key1 key2
0  0.894564  1.050070    a  one
1  0.688548 -0.740128    a  two
2  0.196103  0.041584    b  one
3  0.413136  2.180567    b  two
4 -0.489973 -0.274152    a  one
```

```
for key, value in df[['data2']].groupby(df['key1']):
    print key
    print value

output:
a
      data2
0  1.050070
1 -0.740128
4 -0.274152
b
      data2
2  0.041584
3  2.180567

```

```
for key, value in df.groupby('key1')[['data2']]:
    print key
    print value


output:
a
      data1     data2 key1 key2
0  0.894564  1.050070    a  one
1  0.688548 -0.740128    a  two
4 -0.489973 -0.274152    a  one
b
      data1     data2 key1 key2
2  0.196103  0.041584    b  one
3  0.413136  2.180567    b  two
```

Environment:

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 14.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.15.2
nose: 1.3.4
Cython: 0.19.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.0
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: 2.4.0
numexpr: 2.2.2
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.2.3
bs4: 4.3.2
html5lib: 0.95-dev
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.8.3
pymysql: None
psycopg2: None
```
