HyukjinKwon commented on a change in pull request #27169: [SPARK-30485][SQL]
Remove SQL configs deprecated in Spark 2.1 and 2.3
URL: https://github.com/apache/spark/pull/27169#discussion_r365631834
##########
File path: python/pyspark/sql/tests/test_pandas_udf_scalar.py
##########
@@ -724,49 +724,6 @@ def iter_check_records_per_batch(it):
for (r,) in result:
self.assertTrue(r <= 3)
- def test_vectorized_udf_timestamps_respect_session_timezone(self):
- schema = StructType([
- StructField("idx", LongType(), True),
- StructField("timestamp", TimestampType(), True)])
- data = [(1, datetime(1969, 1, 1, 1, 1, 1)),
- (2, datetime(2012, 2, 2, 2, 2, 2)),
- (3, None),
- (4, datetime(2100, 3, 3, 3, 3, 3))]
- df = self.spark.createDataFrame(data, schema=schema)
-
- scalar_internal_value = pandas_udf(
- lambda ts: ts.apply(lambda ts: ts.value if ts is not pd.NaT else
None), LongType())
-
- @pandas_udf(LongType(), PandasUDFType.SCALAR_ITER)
- def iter_internal_value(it):
- for ts in it:
- yield ts.apply(lambda ts: ts.value if ts is not pd.NaT else
None)
-
- for internal_value, udf_type in [(scalar_internal_value,
PandasUDFType.SCALAR),
- (iter_internal_value,
PandasUDFType.SCALAR_ITER)]:
- f_timestamp_copy = pandas_udf(lambda ts: ts, TimestampType(),
udf_type)
- timezone = "America/New_York"
- with self.sql_conf({
Review comment:
This, too.
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