zero323 commented on a change in pull request #34354:
URL: https://github.com/apache/spark/pull/34354#discussion_r756336353
##########
File path: python/pyspark/sql/functions.py
##########
@@ -3514,7 +3538,19 @@ def map_from_arrays(col1: "ColumnOrName", col2:
"ColumnOrName") -> Column:
return Column(sc._jvm.functions.map_from_arrays(_to_java_column(col1),
_to_java_column(col2)))
+@overload
def array(*cols: "ColumnOrName") -> Column:
+ ...
+
+
+@overload
+def array(__cols: Union[List["ColumnOrName"], Tuple["ColumnOrName", ...]]) ->
Column:
+ ...
+
+
+def array(
+ *cols: Union["ColumnOrName", Union[List["ColumnOrName"],
Tuple["ColumnOrName", ...]]]
Review comment:
In general, nested `Unions` are flattened automatically:
```python
>>> Union["ColumnOrName", Union[List["ColumnOrName"], Tuple["ColumnOrName",
...]]]
typing.Union[ForwardRef('ColumnOrName'),
typing.List[ForwardRef('ColumnOrName')],
typing.Tuple[ForwardRef('ColumnOrName'), ...
```
Personally, I find structure that clearly groups similar categories useful
(for similar problem see
https://github.com/apache/spark/pull/34671#discussion_r754532969), but I am not
very attached to this idea.
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