BUG: Error with std of nullable column obtained from groupby

[MathieuDutSik]

The error occurs with

df = pd.DataFrame({"A": [2, 1, 1, 1, 2, 2, 1], "B": pd.Series(np.full(7, np.nan), dtype="Int64")})
df.groupby("A").std()

while now we have df.groupby("A").var() working correctly.

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

• [x ] 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.

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Problem description

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Expected Output

Output of pd.show_versions()

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[arw2019]

This works on current master

[jreback]

this is more complicated as its currently wrong, we should be outputing and ignoring in the calculation.

In [21]: df = pd.DataFrame(
    ...:         {"A": [2, 1, 1, 1, 2, 2, 1], "B": pd.Series(np.full(7, np.nan), dtype="Int64"), 'C': pd.array([1,2,3, 4,pd.NA, pd.NA, pd.NA], dtype='Int64')}
    ...:
    ...:         )

In [22]: df.groupby('A').std()
Out[22]:
    B    C
A
1 NaN  1.0
2 NaN  NaN

In [23]: df.groupby('A').sum()
Out[23]:
   B  C
A
1  0  9
2  0  1

In [24]: df.groupby('A').mean()
Out[24]:
      B    C
A
1  <NA>  3.0
2  <NA>  1.0

[GabrielSimonetto]

@jreback What would be the desired behaviour? Is the problem only in the std function? Sorry if the question is a bit dumb, but I don't understand what your example is showing

[jreback]

we need to actually handle pd.NA correctly and return it as appropriate

here the results should all contain of pd.NA

this is pretty new and that's why it has not been built out yet

groupby supports EA for mean sum min max but not yet std / var for EAs generally

and that's the issue