ueshin commented on a change in pull request #34160:
URL: https://github.com/apache/spark/pull/34160#discussion_r720522774
##########
File path: python/pyspark/pandas/groupby.py
##########
@@ -1295,6 +1303,8 @@ def wrapped_func(
pdf_or_ser =
pdf.groupby(groupkey_names)[name].apply(wrapped_func, *args, **kwargs)
else:
pdf_or_ser = pdf.groupby(groupkey_names).apply(wrapped_func,
*args, **kwargs)
+ if should_return_series and isinstance(pdf_or_ser,
pd.DataFrame):
Review comment:
No, I don't think it's related.
Pandas' `DataFrameGroupBy.apply` sometimes behaves weirdly when the udf
returns `Series` and whether there is only one group or more. E.g.,:
```py
>>> pdf = pd.DataFrame(
... {"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9,
16, 25, 36]},
... columns=["a", "b", "c"],
... )
>>> pdf.groupby('b').apply(lambda x: x['a'])
b
1 0 1
1 2
2 2 3
3 3 4
5 4 5
8 5 6
Name: a, dtype: int64
>>> pdf[pdf['b'] == 1].groupby('b').apply(lambda x: x['a'])
a 0 1
b
1 1 2
```
As you can see, if there is only one group, it returns a "wide" `DataFrame`
instead of `Series`.
In our non-shortcut path, there is always only one group because it will be
run in `groupby-applyInPandas`, so we will get `DataFrame`, then we should
convert it to `Series` ourselves.
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