Different behavior of numpy functions when used as a Python lambda function
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import numpy as np

df = pd.DataFrame().from_dict({"a": [1, 1, 1, 1], "b": [1, 2, 3, 4]})
df
    a   b
0   1   1
1   1   2
2   1   3
3   1   4

gdf = df.groupby("a")

gdf['b'].agg([("std1", lambda x: np.std(x))])  # ddof = 0 
        std1
a          
1  1.118034

gdf['b'].agg([("std2", np.std)])  # ddof = 1
       std2
a          
1  1.290994
```
#### Problem description

To most Python/Pandas users, `np.std` and `lambda x: np.std(x)` should be interchangeable, as should any other method and its lambda equivalent. However, due to the implicit translation to `pd.std` that occurs in the latter example, these two calls use different default values of the `ddof` parameter, which can lead to real mismatches when refactoring code. 

Given the discussion on closed Issue #13344, I gather that this is intended behavior, but it is nevertheless confusing. We may want to consider allowing for comparable output, at least as a default; if users want to override that behavior, they can explicitly provide a value for `ddof`.

#### Expected Output
BOTH aggregates output the `ddof = 1` calculation: 
```
       std2
a          
1  1.290994
```
when no parameters are passed to the lambda.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.1.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.2.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.1
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 45.1.0.post20200119
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.1
IPython          : 7.12.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None
</details>
