Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18664#discussion_r131723483
--- Diff: python/pyspark/sql/tests.py ---
@@ -3036,6 +3052,9 @@ def test_toPandas_arrow_toggle(self):
pdf = df.toPandas()
self.spark.conf.set("spark.sql.execution.arrow.enable", "true")
pdf_arrow = df.toPandas()
+ # need to remove timezone for comparison
+ pdf_arrow["7_timestamp_t"] = \
+ pdf_arrow["7_timestamp_t"].apply(lambda ts:
ts.tz_localize(None))
--- End diff --
Without Arrow, Spark will provide timestamps that are tz-naive. We would
need to construct the Pandas DataFrame, then call `tz_localize` with the
`SESSION_LOCAL_TIMEZONE` which would then change the internal values to be
normalized to UTC, as @wesm pointed out above. I don't think this is a very
good idea, because it assumes the timestamp was created with the
`SESSION_LOCAL_TIMEZONE`
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