[GitHub] [arrow] jorisvandenbossche commented on pull request #36314: GH-36096: [Python] Call __from_arrow__ in Array.to_pandas

["jorisvandenbossche (via GitHub)" <gi...@apache.org>]

jorisvandenbossche commented on PR #36314:
URL: https://github.com/apache/arrow/pull/36314#issuecomment-1611070229

   Hmm, so the failing tests point out an issue with this approach: we have some keywords to control the conversion, notably `timestamp_as_object` in this case. 
   And if the user passes this, and we just call `pandas_dtype.__from_arrow__` nonetheless, this keyword gets ignored.
   
   But, we also already have this problem, as this already happens for the ChunkedArray conversion:
   
   ```
   In [8]: from datetime import datetime
      ...: import pyarrow as pa
      ...: 
      ...: arr = pa.array([datetime(2001, 1, 1)], pa.timestamp("s", tz="America/New_York"))
      ...: table = pa.table({'a': arr})
   
   In [9]: arr.to_pandas(timestamp_as_object=True)
   Out[9]: 
   0    2000-12-31 19:00:00-05:00
   dtype: object
   
   In [10]: table["a"].to_pandas(timestamp_as_object=True)
   Out[10]: 
   0   2000-12-31 19:00:00-05:00
   Name: a, dtype: datetime64[ns, America/New_York]
   ```


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