AlenkaF commented on issue #45644: URL: https://github.com/apache/arrow/issues/45644#issuecomment-2693674211
This behaviour is expected as NumPy datetimes are not timezone aware, see https://numpy.org/devdocs/reference/arrays.datetime.html#datetimes-and-timedeltas. You can convert pyarrow tz-aware timestamp array to - numpy datetime64 with loss of tz information, - pandas tz-aware datetime64 dtype - a Pyhon object ```python arr_tz = pa.array([1735689600, 1735689600, 1735689600], type=pa.timestamp("s", tz='UTC')) # numpy datetime64 dtype (losing tz information) >>> arr_tz.to_numpy() array(['2025-01-01T00:00:00', '2025-01-01T00:00:00', '2025-01-01T00:00:00'], dtype='datetime64[s]') ``` ```python # pandas tz-aware datetime64 dtype >>> arr_tz.to_pandas().array 0 2025-01-01 00:00:00+00:00 1 2025-01-01 00:00:00+00:00 2 2025-01-01 00:00:00+00:00 dtype: datetime64[s, UTC] ``` ```python # python object >>> arr_tz.to_pandas(timestamp_as_object=True).to_numpy() array([datetime.datetime(2025, 1, 1, 0, 0, tzinfo=<UTC>), datetime.datetime(2025, 1, 1, 0, 0, tzinfo=<UTC>), datetime.datetime(2025, 1, 1, 0, 0, tzinfo=<UTC>)], dtype=object) ``` I will keep this issue open as this needs to be documented in https://arrow.apache.org/docs/python/numpy.html. Also connected and need to go into the docs: https://github.com/apache/arrow/issues/41162. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: github-unsubscr...@arrow.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
