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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