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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/11/27 11:10:41 UTC
[GitHub] [spark] zero323 commented on a diff in pull request #38168: fix(type): add bool and List[bool] for join's on input
zero323 commented on code in PR #38168:
URL: https://github.com/apache/spark/pull/38168#discussion_r1032911052
##########
python/pyspark/sql/dataframe.py:
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@@ -2044,7 +2044,7 @@ def crossJoin(self, other: "DataFrame") -> "DataFrame":
def join(
self,
other: "DataFrame",
- on: Optional[Union[str, List[str], Column, List[Column]]] = None,
+ on: Optional[Union[str, List[str], bool, List[bool], Column, List[Column]]] = None,
Review Comment:
@HyukjinKwon As far as I can tell, there is no real difference here. The difference between Pandas and Spark check, is most likely related to the missing stubs for the former one. If I use environment without `pandas-stubs` things type check in PyCharm
If I choose one with `pandas-stubs` installed, I get
which is the same category of failure as for PySpark code.
Given mypy as a reference, this is an expected false positive (see https://github.com/python/mypy/issues/2783).
On a side note ‒ Pandas and PySpark joins shown on the screenshots are not even remotely equivalent.
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