Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18664#discussion_r147021068
--- Diff: python/pyspark/serializers.py ---
@@ -224,7 +225,13 @@ def _create_batch(series):
# If a nullable integer series has been promoted to floating point
with NaNs, need to cast
# NOTE: this is not necessary with Arrow >= 0.7
def cast_series(s, t):
- if t is None or s.dtype == t.to_pandas_dtype():
+ if type(t) == pa.TimestampType:
+ # NOTE: convert to 'us' with astype here, unit ignored in
`from_pandas` see ARROW-1680
+ return
_series_convert_timestamps_internal(s).values.astype('datetime64[us]')
--- End diff --
`apply()` will invoke the given function on each individual value of the
series. I think this iterates over the series, where `s.dt.tz_localize()`
would do a vectorized operation and should be faster.
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