EnricoMi commented on code in PR #37407:
URL: https://github.com/apache/spark/pull/37407#discussion_r972871859
##########
python/pyspark/sql/dataframe.py:
##########
@@ -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:
However, `pyspark.pandas.frame.melt` allows for `None` for `ids`, having the
meaning of `[]`, while `values` being `None` means magically take all non-id
columns:
```python
def melt(
self,
id_vars: Optional[Union[Name, List[Name]]] = None,
value_vars: Optional[Union[Name, List[Name]]] = None,
var_name: Optional[Union[str, List[str]]] = None,
value_name: str = "value",
) -> "DataFrame":
"""
...
Parameters
----------
frame : DataFrame
id_vars : tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that
are not set as `id_vars`.
```
Should Python API be consistent with Scala API or PySpark Pandas API?
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