REF: share __array_wrap__

pandas/core/indexes/base.py

@@ -813,6 +813,7 @@ def __array_wrap__(self, result, context=None):
             return result
 
         attrs = self._get_attributes_dict()
+        attrs.pop("freq", None)  # For DatetimeIndex/TimedeltaIndex
         return Index(result, **attrs)
 
     @cache_readonly

pandas/core/indexes/datetimelike.py

@@ -35,7 +35,6 @@
 )
 
 from pandas.core.dtypes.common import (
-    is_bool_dtype,
     is_categorical_dtype,
     is_dtype_equal,
     is_integer,
@@ -113,15 +112,10 @@ def __array_wrap__(self, result, context=None):
         """
         Gets called after a ufunc and other functions.
         """
-        result = lib.item_from_zerodim(result)
-        if is_bool_dtype(result) or lib.is_scalar(result):
-            return result
-
-        attrs = self._get_attributes_dict()
-        if not is_period_dtype(self.dtype) and attrs["freq"]:
-            # no need to infer if freq is None
-            attrs["freq"] = "infer"
-        return type(self)(result, **attrs)
+        out = super().__array_wrap__(result, context=context)
+        if isinstance(out, DatetimeTimedeltaMixin) and self.freq is not None:
+            out = out._with_freq("infer")
+        return out
 
     # ------------------------------------------------------------------------
 

pandas/core/indexes/datetimes.py

@@ -257,8 +257,8 @@ class DatetimeIndex(DatetimeTimedeltaMixin):
     _engine_type = libindex.DatetimeEngine
     _supports_partial_string_indexing = True
 
-    _comparables = ["name", "freqstr", "tz"]
-    _attributes = ["name", "tz", "freq"]
+    _comparables = ["name", "freqstr"]
+    _attributes = ["name", "freq"]
 
     _is_numeric_dtype = False
 

pandas/core/indexes/interval.py

@@ -256,7 +256,7 @@ def func(self, other, sort=None):
 class IntervalIndex(ExtensionIndex):
     _typ = "intervalindex"
     _comparables = ["name"]
-    _attributes = ["name", "closed"]
+    _attributes = ["name"]
 
     # annotate properties pinned via inherit_names
     closed: str
@@ -422,12 +422,8 @@ def __contains__(self, key: Any) -> bool:
     def _multiindex(self) -> MultiIndex:
         return MultiIndex.from_arrays([self.left, self.right], names=["left", "right"])
 
-    def __array_wrap__(self, result, context=None):
-        # we don't want the superclass implementation
-        return result
-
     def __reduce__(self):
-        d = {"left": self.left, "right": self.right}
+        d = {"left": self.left, "right": self.right, "closed": self.closed}
         d.update(self._get_attributes_dict())
         return _new_IntervalIndex, (type(self), d), None
 

pandas/core/indexes/period.py

@@ -34,7 +34,6 @@
 from pandas.util._decorators import doc
 
 from pandas.core.dtypes.common import (
-    is_bool_dtype,
     is_datetime64_any_dtype,
     is_float,
     is_integer,
@@ -350,42 +349,6 @@ def __contains__(self, key: Any) -> bool:
     # ------------------------------------------------------------------------
     # Index Methods
 
-    def __array_wrap__(self, result, context=None):
-        """
-        Gets called after a ufunc and other functions.
-
-        Needs additional handling as PeriodIndex stores internal data as int
-        dtype
-
-        Replace this to __numpy_ufunc__ in future version and implement
-        __array_function__ for Indexes
-        """
-        if isinstance(context, tuple) and len(context) > 0:
-            func = context[0]
-            if func is np.add:
-                pass
-            elif func is np.subtract:
-                name = self.name
-                left = context[1][0]
-                right = context[1][1]
-                if isinstance(left, PeriodIndex) and isinstance(right, PeriodIndex):
-                    name = left.name if left.name == right.name else None
-                    return Index(result, name=name)
-                elif isinstance(left, Period) or isinstance(right, Period):
-                    return Index(result, name=name)
-            elif isinstance(func, np.ufunc):
-                if "M->M" not in func.types:
-                    msg = f"ufunc '{func.__name__}' not supported for the PeriodIndex"
-                    # This should be TypeError, but TypeError cannot be raised
-                    # from here because numpy catches.
-                    raise ValueError(msg)
-
-        if is_bool_dtype(result):
-            return result
-        # the result is object dtype array of Period
-        # cannot pass _simple_new as it is
-        return type(self)(result, freq=self.freq, name=self.name)
-
     def asof_locs(self, where: Index, mask: np.ndarray) -> np.ndarray:
         """
         where : array of timestamps