[GitHub] [arrow] jorisvandenbossche commented on a diff in 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 code in PR #35656:
URL: https://github.com/apache/arrow/pull/35656#discussion_r1222675409


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python/pyarrow/types.pxi:
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@@ -40,10 +42,20 @@ cdef dict _pandas_type_map = {
     _Type_HALF_FLOAT: np.float16,
     _Type_FLOAT: np.float32,
     _Type_DOUBLE: np.float64,
-    _Type_DATE32: np.dtype('datetime64[ns]'),
-    _Type_DATE64: np.dtype('datetime64[ns]'),
-    _Type_TIMESTAMP: np.dtype('datetime64[ns]'),
-    _Type_DURATION: np.dtype('timedelta64[ns]'),
+    _Type_DATE32: np.dtype('datetime64[D]'),

Review Comment:
   > I'm somewhat inclined to convert date32 to [ms] by default so we don't have to add a conversion from [ms] -> [s] when doing a parquet roundtrip
   
   Yes, that sounds as a good idea (then it also gives the same for date32 vs date64)



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