BUG: GroupBy sum on an Int64 column with min_count=1 is wrong when all values are null

[ali-tny]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

import pandas as pd

df = pd.DataFrame(
    [
        {"key": 1, "value": None},
    ],
    dtype="Int64",
)

>>> df["value"].sum(min_count=1)
<NA>
>>> df.groupby("key")["value"].sum(min_count=1)
key
1    -9223372036854775808
Name: value, dtype: Int64

Problem description

When summing with min_count=1 (so that groups with all nulls sum to null), DataFrame.sum exhibits expected behaviour (ie, all nulls sum to null), but GroupBy.sum returns the minimum Int64 -9223372036854775808 rather than <NA>.

Expected Output

>>> df.groupby("key")["value"].sum(min_count=1)
key
1    <NA>
Name: value, dtype: Int64

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.14.0
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

[jreback]

pls try on master
a patch was recently merged for this

[jorisvandenbossche]

Yes, master gives

In [21]: import pandas as pd 
    ...:  
    ...: df = pd.DataFrame( 
    ...:     [ 
    ...:         {"key": 1, "value": None}, 
    ...:     ], 
    ...:     dtype="Int64", 
    ...: )                                                                                                                                                                                                         

In [22]: df.groupby("key")["value"].sum(min_count=1)                                                                                                                                                               
Out[22]: 
key
1    <NA>
Name: value, dtype: Int64

Duplicate of #32861

[ali-tny]

Thank you! Sorry I missed the closed issue