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

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

danepitkin commented on code in PR #35656:
URL: https://github.com/apache/arrow/pull/35656#discussion_r1212301675


##########
python/pyarrow/types.pxi:
##########
@@ -1122,6 +1150,19 @@ cdef class DurationType(DataType):
         """
         return timeunit_to_string(self.duration_type.unit())
 
+    def to_pandas_dtype(self):
+        """
+        Return the equivalent NumPy / Pandas dtype.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> d = pa.duration('ms')
+        >>> d.to_pandas_dtype()
+        timedelta64[ms]
+        """
+        return _get_pandas_type(_Type_TIMESTAMP, self.unit)

Review Comment:
   Great catch! You're correct that this isn't tested yet.



##########
python/pyarrow/types.pxi:
##########
@@ -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:
   Thank you! Numpy supports [D]ay, but pandas does not.



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