harshmotw-db commented on code in PR #48770:
URL: https://github.com/apache/spark/pull/48770#discussion_r1831696110
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python/pyspark/sql/tests/pandas/test_pandas_udf_scalar.py:
##########
@@ -752,46 +752,87 @@ def check_vectorized_udf_return_scalar(self):
def test_udf_with_variant_input(self):
df = self.spark.range(0, 10).selectExpr("parse_json(cast(id as
string)) v")
- from pyspark.sql.functions import col
- scalar_f = pandas_udf(lambda u: str(u), StringType())
+ scalar_f = pandas_udf(lambda u: u.apply(str), StringType(),
PandasUDFType.SCALAR)
Review Comment:
Yes, for the most part. I recall that for pandas UDFs to work, I also had to
add changes in `arrow_to_pandas` and `_create_batch` too because they treat
struct types in a special way. Example:
https://github.com/apache/spark/pull/48770/files#r1831583273
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