API: Index(object_dtype_bool_ndarray) retain object dtype

doc/source/whatsnew/v2.0.0.rst

@@ -291,7 +291,8 @@ Other API changes
 - Passing a sequence containing ``datetime`` objects and ``date`` objects to :class:`Series` constructor will return with ``object`` dtype instead of ``datetime64[ns]`` dtype, consistent with :class:`Index` behavior (:issue:`49341`)
 - Passing strings that cannot be parsed as datetimes to :class:`Series` or :class:`DataFrame` with ``dtype="datetime64[ns]"`` will raise instead of silently ignoring the keyword and returning ``object`` dtype (:issue:`24435`)
 - Passing a sequence containing a type that cannot be converted to :class:`Timedelta` to :func:`to_timedelta` or to the :class:`Series` or :class:`DataFrame` constructor with ``dtype="timedelta64[ns]"`` or to :class:`TimedeltaIndex` now raises ``TypeError`` instead of ``ValueError`` (:issue:`49525`)
-- Changed behavior of :class:`Index` construct with sequence containing at least one ``NaT`` and everything else either ``None`` or ``NaN`` to infer ``datetime64[ns]`` dtype instead of ``object``, matching :class:`Series` behavior (:issue:`49340`)
+- Changed behavior of :class:`Index` constructor with sequence containing at least one ``NaT`` and everything else either ``None`` or ``NaN`` to infer ``datetime64[ns]`` dtype instead of ``object``, matching :class:`Series` behavior (:issue:`49340`)
+- Changed behavior of :class:`Index` constructor with an object-dtype ``numpy.ndarray`` containing all-``bool`` values or all-complex values, this will now retain object dtype, consistent with the :class:`Series` behavior (:issue:`49594`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/indexes/base.py

@@ -81,6 +81,7 @@
     find_common_type,
     infer_dtype_from,
     maybe_cast_pointwise_result,
+    maybe_infer_to_datetimelike,
     np_can_hold_element,
 )
 from pandas.core.dtypes.common import (
@@ -503,9 +504,8 @@ def __new__(
                 arr = com.asarray_tuplesafe(data, dtype=_dtype_obj)
 
                 if dtype is None:
-                    arr = _maybe_cast_data_without_dtype(
-                        arr, cast_numeric_deprecated=True
-                    )
+                    arr = maybe_infer_to_datetimelike(arr)
+                    arr = ensure_wrapped_if_datetimelike(arr)
                     dtype = arr.dtype
 
             klass = cls._dtype_to_subclass(arr.dtype)
@@ -534,9 +534,7 @@ def __new__(
             subarr = com.asarray_tuplesafe(data, dtype=_dtype_obj)
             if dtype is None:
                 # with e.g. a list [1, 2, 3] casting to numeric is _not_ deprecated
-                subarr = _maybe_cast_data_without_dtype(
-                    subarr, cast_numeric_deprecated=False
-                )
+                subarr = _maybe_cast_data_without_dtype(subarr)
                 dtype = subarr.dtype
             return Index(subarr, dtype=dtype, copy=copy, name=name)
 
@@ -7062,18 +7060,14 @@ def maybe_extract_name(name, obj, cls) -> Hashable:
     return name
 
 
-def _maybe_cast_data_without_dtype(
-    subarr: np.ndarray, cast_numeric_deprecated: bool = True
-) -> ArrayLike:
+def _maybe_cast_data_without_dtype(subarr: npt.NDArray[np.object_]) -> ArrayLike:
     """
     If we have an arraylike input but no passed dtype, try to infer
     a supported dtype.
 
     Parameters
     ----------
     subarr : np.ndarray[object]
-    cast_numeric_deprecated : bool, default True
-        Whether to issue a FutureWarning when inferring numeric dtypes.
 
     Returns
     -------
@@ -7088,12 +7082,6 @@ def _maybe_cast_data_without_dtype(
         convert_interval=True,
         dtype_if_all_nat=np.dtype("datetime64[ns]"),
     )
-    if result.dtype.kind in ["i", "u", "f"]:
-        if not cast_numeric_deprecated:
-            # i.e. we started with a list, not an ndarray[object]
-            return result
-        return subarr
-
     result = ensure_wrapped_if_datetimelike(result)
     return result
 

pandas/tests/indexes/test_index_new.py

@@ -39,6 +39,24 @@
 
 
 class TestIndexConstructorInference:
+    def test_object_all_bools(self):
+        # GH#49594 match Series behavior on ndarray[object] of all bools
+        arr = np.array([True, False], dtype=object)
+        res = Index(arr)
+        assert res.dtype == object
+
+        # since the point is matching Series behavior, let's double check
+        assert Series(arr).dtype == object
+
+    def test_object_all_complex(self):
+        # GH#49594 match Series behavior on ndarray[object] of all complex
+        arr = np.array([complex(1), complex(2)], dtype=object)
+        res = Index(arr)
+        assert res.dtype == object
+
+        # since the point is matching Series behavior, let's double check
+        assert Series(arr).dtype == object
+
     @pytest.mark.parametrize("val", [NaT, None, np.nan, float("nan")])
     def test_infer_nat(self, val):
         # GH#49340 all NaT/None/nan and at least 1 NaT -> datetime64[ns],