Docs: broken groupby.GroupBy.apply example
#### Sample
Copied directly from [pandas-docs-travis](https://pandas-docs.github.io/pandas-docs-travis/generated/pandas.core.groupby.GroupBy.apply.html):
```python
import pandas as pd
df = pd.DataFrame({'A': 'a a b'.split(), 'B': [1,2,3], 'C': [4,6, 5]})
g = df.groupby('A')

# ValueError
g.apply(lambda x: x / x.sum()) 

# ValueError
g.apply(lambda x: x.max() - x.min()) 

# Works fine
g.apply(lambda x: x.C.max() - x.B.min()) 
```
#### Problem description

The first two apply operations return `ValueError: can only convert an array of size 1 to a Python scalar`. The third apply operation works as expected.

#### Expected Output
As given by [pandas-docs-travis](https://pandas-docs.github.io/pandas-docs-travis/generated/pandas.core.groupby.GroupBy.apply.html):
```python
>>> g.apply(lambda x: x / x.sum())
          B    C
0  0.333333  0.4
1  0.666667  0.6
2  1.000000  1.0
```

```python
>>> g.apply(lambda x: x.max() - x.min())
   B  C
A
a  1  2
b  0  0
```

```python
>>> g.apply(lambda x: x.C.max() - x.B.min())
A
a    5
b    2
dtype: int64
```

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: None.None

pandas: 0.22.0
pytest: 3.0.5
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 5.4.1
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.4
feather: None
matplotlib: 2.1.1
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.9999999
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
