[GitHub] [arrow] jorisvandenbossche commented on pull request #35656: GH-33321: [Python] Support converting to non-nano datetime64 for pandas >= 2.0

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

jorisvandenbossche commented on PR #35656:
URL: https://github.com/apache/arrow/pull/35656#issuecomment-1582537127

   >  For (2), I just need to add support, but it's going to grow this PR even larger unfortunately..
   
   If PR size is a concern, this is also something that could be done as a precursor. It's actually already an issue that shows in conversion to numpy as well:
   
   ```
   # no timezone -> this preserves the unit
   >>> pa.array([1, 2, 3], pa.timestamp('us')).to_numpy()
   array(['1970-01-01T00:00:00.000001', '1970-01-01T00:00:00.000002',
          '1970-01-01T00:00:00.000003'], dtype='datetime64[us]')
   
   # with timezone -> always converts to nanoseconds
   >>> pa.array([1, 2, 3], pa.timestamp('us', tz="Europe/Brussels")).to_numpy()
   ...
   ArrowInvalid: Needed to copy 1 chunks with 0 nulls, but zero_copy_only was True
   
   >>> pa.array([1, 2, 3], pa.timestamp('us', tz="Europe/Brussels")).to_numpy(zero_copy_only=False)
   array(['1970-01-01T00:00:00.000001000', '1970-01-01T00:00:00.000002000',
          '1970-01-01T00:00:00.000003000'], dtype='datetime64[ns]')
   ```
   
   While this could also be perfectly zero-copy to microseconds in the case with a timezone (we just return the underlying UTC values anyway)


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