Improve PandasSeriesGroupby operations performance
#### Code Sample
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0,10000,size=(10000, 4)), columns=list('ABCD'))
df2 = df.groupby('B')
df2.apply(lambda x: x.A.values.mean() ) # Operation 1
df2.apply(lambda x: x.A.mean()) # Operation 2
```


#### Problem description
First operation takes 344 ms, second takes 800 ms.
For smaller arrays (10 elements), first  operation takes 1.86 ms and second takes 2.26 ms.

The slowdown occurs also with the max function it's not exclusive of mean. 

I had my theory in previous editions in this post. But now I don't have a clue.
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.2.11
machine          : x86_64
processor        : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder        : little
LC_ALL           : None
LANG             : pt_BR.UTF-8
LOCALE           : pt_BR.UTF-8

pandas           : 0.25.1
numpy            : 1.14.5
pytz             : 2019.2
dateutil         : 2.7.2
pip              : 19.1
setuptools       : 41.2.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None



</details>
