groupby.agg changes the default parameter of np.std
#### Code Sample, a copy-pastable example if possible

As opposed to pandas, the default value for `ddof` in standard deviation calculations is 0 in numpy. However, when I use `groupby.agg(np.std)` I get the result for `ddof=1`.

```
import numpy as np
import pandas as pd
prng = np.random.RandomState(0)
df = pd.DataFrame({"A": prng.choice(list("abcdef"), 15), 
                   "B": prng.randint(0, 10, 15)})
df.groupby('A')['B'].agg(np.std)

Out[54]: 
A
a    3.511885
b         NaN
c    2.828427
d    3.593976
e    1.527525
f    1.414214
Name: B, dtype: float64
```
#### Expected Output

There is a [note ](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.agg.html) in the docs that states the default axis is changed but for me ddof change is quite unexpected. So my expected output is this:

```
df.groupby('A')['B'].apply(np.std)

Out[61]: 
A
a    2.867442
b    0.000000
c    2.000000
d    3.112475
e    1.247219
f    1.000000
Name: B, dtype: float64
```
#### output of `pd.show_versions()`

INSTALLED VERSIONS

commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
