TST(string dtype): Resolve xfail when grouping by nan column

[rhshadrach]

• closes #xxxx (Replace xxxx with the GitHub issue number)
• Tests added and passed if fixing a bug or adding a new feature
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At first glance the behavior perhaps seems odd, but I think have to go with it while we infer dtype based on the non-NA values and accept None as an NA value for the inferred dtype. Namely, in

 df = DataFrame({None: [1, 1, 2, 2], "b": [1, 1, 2, 3], "c": [4, 5, 6, 7]}) 

the columns are [None, "b", "c"] and so we infer this as strings with None being the corresponding NA-value, in this case np.nan. Thus we need to groupby np.nan.

[WillAyd]

At first glance the behavior perhaps seems odd, but I think have to go with it while we infer dtype based on the non-NA values and accept None as an NA value for the inferred dtype.

I think what is being tested here took advantage of the old implementation, giving None a special case as an object sentinel. In the log run, I don't see us supporting that natively for strings, so the coercion to a missing value indicator makes sense (and is consistent with constructors)

[mroeschke]

Thanks @rhshadrach

[lumberbot-app]

Owee, I'm MrMeeseeks, Look at me.

There seem to be a conflict, please backport manually. Here are approximate instructions:

1. Checkout backport branch and update it.

git checkout 2.3.x git pull 

2. Cherry pick the first parent branch of the this PR on top of the older branch:

git cherry-pick -x -m1 55a6d0a613897040fec1ae11adc15f5f04728032 

3. You will likely have some merge/cherry-pick conflict here, fix them and commit:

git commit -am 'Backport PR #60712: TST(string dtype): Resolve xfail when grouping by nan column' 

4. Push to a named branch:

git push YOURFORK 2.3.x:auto-backport-of-pr-60712-on-2.3.x 

5. Create a PR against branch 2.3.x, I would have named this PR:

"Backport PR #60712 on branch 2.3.x (TST(string dtype): Resolve xfail when grouping by nan column)"

And apply the correct labels and milestones.

Congratulations — you did some good work! Hopefully your backport PR will be tested by the continuous integration and merged soon!

Remember to remove the Still Needs Manual Backport label once the PR gets merged.

If these instructions are inaccurate, feel free to suggest an improvement.

[rhshadrach]

Backport PR: #60719

[jorisvandenbossche]

I do think that on the short term, we should maybe keep supporting df.groupby(by=[None]).sum() ? (and potentially deprecate it later?)

There are similar tests with pivot or stack/unstack where we also refer to a column/index label with None. For indexing itself, we kept label-lookup working (df[None] gives the column with label NaN).

[WillAyd]

One of the problems with doing that I think would be breaking usability between the str and string types; the latter has never supported using None as a special case

[rhshadrach]

I do think that on the short term, we should maybe keep supporting df.groupby(by=[None]).sum() ?

For all dtypes? Or just str? What happens with object dtype - is None also treated as an NA value?

I'm a bit resistant to changing groupby behavior here. This seems to me to be quite an edge case, and is long standing behavior for other dtypes. E.g.

df = pd.DataFrame([[1, 1, 2], [3, 4, 5]], columns=pd.Index([1, pd.NA, 2], dtype="Int64")) gb = df.groupby([None])

raises.