HyukjinKwon commented on code in PR #37335:
URL: https://github.com/apache/spark/pull/37335#discussion_r932808436
##########
python/pyspark/sql/dataframe.py:
##########
@@ -3244,10 +3244,14 @@ def drop(self, *cols: "ColumnOrName") -> "DataFrame":
# type: ignore[misc]
else:
raise TypeError("col should be a string or a Column")
else:
- for col in cols:
- if not isinstance(col, str):
- raise TypeError("each col in the param list should be a
string")
- jdf = self._jdf.drop(self._jseq(cols))
+ if all(isinstance(col, str) for col in cols):
+ jdf = self._jdf.drop(self._jseq(cols))
+ elif all(isinstance(col, Column) for col in cols):
+ jdf = self._jdf
+ for col in cols:
+ jdf = jdf.drop(col._jc) # type: ignore[union-attr]
Review Comment:
Can we avoid looping here? This is super expensive in Spark SQL optmizer.
Ideally we should add the signature of `def drop(colNames: Column*` in Scala
side first, and PySpark side directlly invokes it.
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