Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18664#discussion_r146922937
--- 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 --
hmmm, that's strange `s.dt.tz_localize('tzlocal()` gets an `OverflowError:
Python int too large to convert to C long` error when printing but
`s.dt.tz_localize('tzlocal()').dt.tz_convert('UTC')` works but comes up with a
bogus time where the NaT was. I agree that `fillna(0)` is safer to avoid
overflow.
```
In [44]: s.dt.tz_localize('tzlocal()').dt.tz_convert('UTC')
Out[44]:
0 2017-10-24 17:44:51.483694+00:00
1 1677-09-21 08:12:43.145224192+00:00
dtype: datetime64[ns, UTC]
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