EnricoMi commented on code in PR #37407:
URL: https://github.com/apache/spark/pull/37407#discussion_r970574953
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python/pyspark/sql/dataframe.py:
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@@ -3064,7 +3064,7 @@ def cube(self, *cols: "ColumnOrName") -> "GroupedData":
# type: ignore[misc]
def unpivot(
self,
- ids: Optional[Union["ColumnOrName", List["ColumnOrName"],
Tuple["ColumnOrName", ...]]],
+ ids: Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName",
...]],
Review Comment:
Just to differentiate the semantics of `ids` from `values`. When `values` is
`None`, it means "magically" all non-id columns. When `ids` is `None`, it does
not magically mean all non-value columns. Disallowing `None` for `ids` requires
the user to explicitly state `ids=[]`, and that is what you get.
Similar to the Scala / Java API where you have to provide `ids` as well,
even if an empty `Array`.
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