pandas groupby sum min_count misbehaves

Code Sample, a copy-pastable example if possible

d=pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
d['col2']=d['col2'].astype(str)
d['col1']=np.nan
d=d.groupby(lambda x:x, axis=1).sum(min_count=1)

Problem description

My hope is that, by using min_count=1, pandas will return NaN when all columns being summed up are NaN. However, now it is returning 0 instead of NaN. Any idea why?

Actual output:

 col1  col2
0   0.0   3.0
1   0.0   4.0

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Expected Output

   col1  col2
0   Nan   3.0
1   Nan   4.0

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]

INSTALLED VERSIONS

commit: None python: 2.7.15.final.0 python-bits: 64 OS: Darwin OS-release: 18.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: None LOCALE: None.None

pandas: 0.23.4 pytest: 3.8.2 pip: 10.0.1 setuptools: 39.1.0 Cython: None numpy: 1.15.4 scipy: 1.1.0 pyarrow: None xarray: None IPython: 5.8.0 sphinx: None patsy: 0.5.0 dateutil: 2.7.3 pytz: 2018.5 blosc: None bottleneck: 1.2.1 tables: None numexpr: None feather: None matplotlib: 2.2.3 openpyxl: None xlrd: 1.1.0 xlwt: None xlsxwriter: None lxml: None bs4: 4.6.3 html5lib: 1.0.1 sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.10 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None

[wbijster]

FYI, I have the same issue. I use groupby to sum data and I want to retain the NaNs if there is no data in a group but have a sum if the group does contain data, even if there are some NaNs. I use this workaround now:

def sumgroup(s):  
    s = s.sum(min_count=1)
    return(s)

dftest['new'] = dftest.groupby(level=['one', 'two'])['data'].apply(sumgroup) # pd.__version__ == 0.24.2

[sv1990]

The min_count parameter still doesn’t work when used as

d=pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
d['col2']=d['col2'].astype(str)
d['col1']=np.nan
d=d.groupby(lambda x:x, axis=1).agg({'col1': 'sum'}, min_count=1)