BUG: concat of bool and boolean giving object dtype (#43409)

Author: mzeitlin11

doc/source/whatsnew/v1.4.0.rst

@@ -419,6 +419,7 @@ Reshaping
 - :func:`concat` creating :class:`MultiIndex` with duplicate level entries when concatenating a :class:`DataFrame` with duplicates in :class:`Index` and multiple keys (:issue:`42651`)
 - Bug in :meth:`pandas.cut` on :class:`Series` with duplicate indices (:issue:`42185`) and non-exact :meth:`pandas.CategoricalIndex` (:issue:`42425`)
 - Bug in :meth:`DataFrame.append` failing to retain dtypes when appended columns do not match (:issue:`43392`)
+- Bug in :func:`concat` of ``bool`` and ``boolean`` dtypes resulting in ``object`` dtype instead of ``boolean`` dtype (:issue:`42800`)
 -
 
 Sparse

pandas/core/arrays/boolean.py

@@ -17,6 +17,7 @@
     ArrayLike,
     AstypeArg,
     Dtype,
+    DtypeObj,
     npt,
     type_t,
 )
@@ -153,6 +154,18 @@ def __from_arrow__(
         else:
             return BooleanArray._concat_same_type(results)
 
+    def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
+        # Handle only boolean + np.bool_ -> boolean, since other cases like
+        # Int64 + boolean -> Int64 will be handled by the other type
+        if all(
+            isinstance(t, BooleanDtype)
+            or (isinstance(t, np.dtype) and (np.issubdtype(t, np.bool_)))
+            for t in dtypes
+        ):
+            return BooleanDtype()
+        else:
+            return None
+
 
 def coerce_to_array(
     values, mask=None, copy: bool = False

pandas/tests/reshape/concat/test_concat.py

@@ -597,6 +597,24 @@ def test_concat_preserves_extension_int64_dtype():
     tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.parametrize(
+    "dtype1,dtype2,expected_dtype",
+    [
+        ("bool", "bool", "bool"),
+        ("boolean", "bool", "boolean"),
+        ("bool", "boolean", "boolean"),
+        ("boolean", "boolean", "boolean"),
+    ],
+)
+def test_concat_bool_types(dtype1, dtype2, expected_dtype):
+    # GH 42800
+    ser1 = Series([True, False], dtype=dtype1)
+    ser2 = Series([False, True], dtype=dtype2)
+    result = concat([ser1, ser2], ignore_index=True)
+    expected = Series([True, False, False, True], dtype=expected_dtype)
+    tm.assert_series_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     ("keys", "integrity"),
     [