When finding the common type, return the original type if there is only one common type. Return objects for date32 and date64 types when converting those types to numpy types.

Author: nli307

pandas/core/dtypes/common.py

@@ -1872,6 +1872,11 @@ def pandas_dtype(dtype) -> DtypeObj:
             result = result()
         return result
 
+    # try a pyarrow dtype
+    from pandas.core.dtypes.dtypes import ArrowDtype
+    if isinstance(dtype, ArrowDtype):
+        return ArrowDtype(dtype)
+
     # try a numpy dtype
     # raise a consistent TypeError if failed
     try:

pandas/core/dtypes/dtypes.py

@@ -2277,6 +2277,12 @@ def name(self) -> str:  # type: ignore[override]
     @cache_readonly
     def numpy_dtype(self) -> np.dtype:
         """Return an instance of the related numpy dtype"""
+        if pa.types.is_date32(self.pyarrow_dtype) or pa.types.is_date64(
+            self.pyarrow_dtype
+        ):
+            # date32 and date64 are pyarrow timestamps but do not have a
+            # corresponding numpy dtype.
+            return np.dtype(object)
         if pa.types.is_timestamp(self.pyarrow_dtype):
             # pa.timestamp(unit).to_pandas_dtype() returns ns units
             # regardless of the pyarrow timestamp units.
@@ -2453,6 +2459,18 @@ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
 
         null_dtype = type(self)(pa.null())
 
+        # Cover cases where numpy does not have a corresponding dtype, but
+        # only one non-null dtype is received, or all dtypes are null.
+        single_dtype = {
+            dtype
+            for dtype in dtypes
+            if dtype != null_dtype
+        }
+        if len(single_dtype) == 0:
+            return null_dtype
+        if len(single_dtype) == 1:
+            return single_dtype.pop()
+
         new_dtype = find_common_type(
             [
                 dtype.numpy_dtype if isinstance(dtype, ArrowDtype) else dtype

pandas/tests/dtypes/cast/test_find_common_type.py

@@ -173,3 +173,19 @@ def test_interval_dtype_with_categorical(dtype):
 
     result = find_common_type([dtype, cat.dtype])
     assert result == dtype
+
+
+@pytest.mark.parametrize(
+    "dtypes,expected",
+    [
+        (
+            ["date32[pyarrow]", "null[pyarrow]"],
+            "date32[day][pyarrow]",
+        ),
+    ],
+)
+def test_pyarrow_dtypes(dtypes, expected):
+    """Test finding common types with pyarrow dtypes not in numpy."""
+    source_dtypes = [pandas_dtype(dtype) for dtype in dtypes]
+    result = find_common_type(source_dtypes)
+    assert result == pandas_dtype(expected)