CLN: Index methods incorrectly assuming object dtype

pandas/core/indexes/base.py

@@ -391,7 +391,6 @@ def _outer_indexer(
     _comparables: list[str] = ["name"]
     _attributes: list[str] = ["name"]
     _is_numeric_dtype: bool = False
-    _can_hold_na: bool = True
     _can_hold_strings: bool = True
 
     # Whether this index is a NumericIndex, but not a Int64Index, Float64Index,
@@ -2206,6 +2205,20 @@ def _get_grouper_for_level(self, mapper, *, level=None):
     # --------------------------------------------------------------------
     # Introspection Methods
 
+    @cache_readonly
+    @final
+    def _can_hold_na(self) -> bool:
+        if isinstance(self.dtype, ExtensionDtype):
+            if isinstance(self.dtype, IntervalDtype):
+                # FIXME(GH#45720): this is inaccurate for integer-backed
+                #  IntervalArray, but without it other.categories.take raises
+                #  in IntervalArray._cmp_method
+                return True
+            return self.dtype._can_hold_na
+        if self.dtype.kind in ["i", "u", "b"]:
+            return False
+        return True
+
     @final
     @property
     def is_monotonic(self) -> bool:
@@ -2662,10 +2675,21 @@ def inferred_type(self) -> str_t:
         return lib.infer_dtype(self._values, skipna=False)
 
     @cache_readonly
+    @final
     def _is_all_dates(self) -> bool:
         """
         Whether or not the index values only consist of dates.
         """
+
+        if needs_i8_conversion(self.dtype):
+            return True
+        elif self.dtype != _dtype_obj:
+            # TODO(ExtensionIndex): 3rd party EA might override?
+            # Note: this includes IntervalIndex, even when the left/right
+            #  contain datetime-like objects.
+            return False
+        elif self._is_multi:
+            return False
         return is_datetime_array(ensure_object(self._values))
 
     @cache_readonly
@@ -6159,6 +6183,10 @@ def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
         """
         Can we compare values of the given dtype to our own?
         """
+        if self.dtype.kind == "b":
+            return dtype.kind == "b"
+        elif is_numeric_dtype(self.dtype):
+            return is_numeric_dtype(dtype)
         return True
 
     @final

pandas/core/indexes/datetimelike.py

@@ -100,10 +100,6 @@ class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex):
         ),
     )
 
-    @property
-    def _is_all_dates(self) -> bool:
-        return True
-
     # ------------------------------------------------------------------------
 
     def equals(self, other: Any) -> bool:
@@ -151,8 +147,6 @@ def __contains__(self, key: Any) -> bool:
             return False
         return True
 
-    _can_hold_na = True
-
     def _convert_tolerance(self, tolerance, target):
         tolerance = np.asarray(to_timedelta(tolerance).to_numpy())
         return super()._convert_tolerance(tolerance, target)

pandas/core/indexes/interval.py

@@ -897,14 +897,6 @@ def _intersection_non_unique(self, other: IntervalIndex) -> IntervalIndex:
 
     # --------------------------------------------------------------------
 
-    @property
-    def _is_all_dates(self) -> bool:
-        """
-        This is False even when left/right contain datetime-like objects,
-        as the check is done on the Interval itself
-        """
-        return False
-
     def _get_engine_target(self) -> np.ndarray:
         # Note: we _could_ use libjoin functions by either casting to object
         #  dtype or constructing tuples (faster than constructing Intervals)

pandas/core/indexes/multi.py

@@ -1841,10 +1841,6 @@ def to_flat_index(self) -> Index:
         """
         return Index(self._values, tupleize_cols=False)
 
-    @property
-    def _is_all_dates(self) -> bool:
-        return False
-
     def is_lexsorted(self) -> bool:
         warnings.warn(
             "MultiIndex.is_lexsorted is deprecated as a public function, "

pandas/core/indexes/numeric.py

@@ -14,7 +14,6 @@
 )
 from pandas._typing import (
     Dtype,
-    DtypeObj,
     npt,
 )
 from pandas.util._decorators import (
@@ -91,14 +90,6 @@ class NumericIndex(Index):
     _can_hold_strings = False
     _is_backward_compat_public_numeric_index: bool = True
 
-    # error: Signature of "_can_hold_na" incompatible with supertype "Index"
-    @cache_readonly
-    def _can_hold_na(self) -> bool:  # type: ignore[override]
-        if is_float_dtype(self.dtype):
-            return True
-        else:
-            return False
-
     _engine_types: dict[np.dtype, type[libindex.IndexEngine]] = {
         np.dtype(np.int8): libindex.Int8Engine,
         np.dtype(np.int16): libindex.Int16Engine,
@@ -268,10 +259,6 @@ def _convert_tolerance(self, tolerance, target):
                 )
         return tolerance
 
-    def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
-        # If we ever have BoolIndex or ComplexIndex, this may need to be tightened
-        return is_numeric_dtype(dtype)
-
     @classmethod
     def _assert_safe_casting(cls, data: np.ndarray, subarr: np.ndarray) -> None:
         """
@@ -284,13 +271,6 @@ def _assert_safe_casting(cls, data: np.ndarray, subarr: np.ndarray) -> None:
             if not np.array_equal(data, subarr):
                 raise TypeError("Unsafe NumPy casting, you must explicitly cast")
 
-    @property
-    def _is_all_dates(self) -> bool:
-        """
-        Checks that all the labels are datetime objects.
-        """
-        return False
-
     def _format_native_types(
         self, *, na_rep="", float_format=None, decimal=".", quoting=None, **kwargs
     ):