REF: share Datetimelike argmin/min etc with Index

pandas/core/indexes/base.py

@@ -6058,6 +6058,9 @@ def __inv__(self):
         # TODO: __inv__ vs __invert__?
         return self._unary_method(lambda x: -x)
 
+    # --------------------------------------------------------------------
+    # Reductions
+
     def any(self, *args, **kwargs):
         """
         Return whether any element is Truthy.
@@ -6178,6 +6181,84 @@ def _maybe_disable_logical_methods(self, opname: str_t):
             # This call will raise
             make_invalid_op(opname)(self)
 
+    @Appender(IndexOpsMixin.argmin.__doc__)
+    def argmin(self, axis=None, skipna=True, *args, **kwargs):
+        nv.validate_argmin(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                return -1
+        return super().argmin(skipna=skipna)
+
+    @Appender(IndexOpsMixin.argmax.__doc__)
+    def argmax(self, axis=None, skipna=True, *args, **kwargs):
+        nv.validate_argmax(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                return -1
+        return super().argmax(skipna=skipna)
+
+    @doc(IndexOpsMixin.min)
+    def min(self, axis=None, skipna=True, *args, **kwargs):
+        nv.validate_min(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not len(self):
+            return self._na_value
+
+        if len(self) and self.is_monotonic_increasing:
+            # quick check
+            first = self[0]
+            if not isna(first):
+                return first
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                return self._na_value
+
+        if not self._is_multi and not isinstance(self._values, np.ndarray):
+            # "ExtensionArray" has no attribute "min"
+            return self._values.min(skipna=skipna)  # type: ignore[attr-defined]
+
+        return super().min(skipna=skipna)
+
+    @doc(IndexOpsMixin.max)
+    def max(self, axis=None, skipna=True, *args, **kwargs):
+        nv.validate_max(args, kwargs)
+        nv.validate_minmax_axis(axis)
+
+        if not len(self):
+            return self._na_value
+
+        if len(self) and self.is_monotonic_increasing:
+            # quick check
+            last = self[-1]
+            if not isna(last):
+                return last
+
+        if not self._is_multi and self.hasnans:
+            # Take advantage of cache
+            mask = self._isnan
+            if not skipna or mask.all():
+                return self._na_value
+
+        if not self._is_multi and not isinstance(self._values, np.ndarray):
+            # "ExtensionArray" has no attribute "max"
+            return self._values.max(skipna=skipna)  # type: ignore[attr-defined]
+
+        return super().max(skipna=skipna)
+
+    # --------------------------------------------------------------------
+
     @final
     @property
     def shape(self) -> Shape:

pandas/core/indexes/datetimelike.py

@@ -18,7 +18,6 @@
 from pandas._libs import (
     NaT,
     Timedelta,
-    iNaT,
     lib,
 )
 from pandas._libs.tslibs import (
@@ -199,120 +198,6 @@ def tolist(self) -> list:
         """
         return list(self.astype(object))
 
-    def min(self, axis=None, skipna=True, *args, **kwargs):
-        """
-        Return the minimum value of the Index or minimum along
-        an axis.
-
-        See Also
-        --------
-        numpy.ndarray.min
-        Series.min : Return the minimum value in a Series.
-        """
-        nv.validate_min(args, kwargs)
-        nv.validate_minmax_axis(axis)
-
-        if not len(self):
-            return self._na_value
-
-        i8 = self.asi8
-
-        if len(i8) and self.is_monotonic_increasing:
-            # quick check
-            if i8[0] != iNaT:
-                return self._data._box_func(i8[0])
-
-        if self.hasnans:
-            if not skipna:
-                return self._na_value
-            i8 = i8[~self._isnan]
-
-        if not len(i8):
-            return self._na_value
-
-        min_stamp = i8.min()
-        return self._data._box_func(min_stamp)
-
-    def argmin(self, axis=None, skipna=True, *args, **kwargs):
-        """
-        Returns the indices of the minimum values along an axis.
-
-        See `numpy.ndarray.argmin` for more information on the
-        `axis` parameter.
-
-        See Also
-        --------
-        numpy.ndarray.argmin
-        """
-        nv.validate_argmin(args, kwargs)
-        nv.validate_minmax_axis(axis)
-
-        i8 = self.asi8
-        if self.hasnans:
-            mask = self._isnan
-            if mask.all() or not skipna:
-                return -1
-            i8 = i8.copy()
-            i8[mask] = np.iinfo("int64").max
-        return i8.argmin()
-
-    def max(self, axis=None, skipna=True, *args, **kwargs):
-        """
-        Return the maximum value of the Index or maximum along
-        an axis.
-
-        See Also
-        --------
-        numpy.ndarray.max
-        Series.max : Return the maximum value in a Series.
-        """
-        nv.validate_max(args, kwargs)
-        nv.validate_minmax_axis(axis)
-
-        if not len(self):
-            return self._na_value
-
-        i8 = self.asi8
-
-        if len(i8) and self.is_monotonic:
-            # quick check
-            if i8[-1] != iNaT:
-                return self._data._box_func(i8[-1])
-
-        if self.hasnans:
-            if not skipna:
-                return self._na_value
-            i8 = i8[~self._isnan]
-
-        if not len(i8):
-            return self._na_value
-
-        max_stamp = i8.max()
-        return self._data._box_func(max_stamp)
-
-    def argmax(self, axis=None, skipna=True, *args, **kwargs):
-        """
-        Returns the indices of the maximum values along an axis.
-
-        See `numpy.ndarray.argmax` for more information on the
-        `axis` parameter.
-
-        See Also
-        --------
-        numpy.ndarray.argmax
-        """
-        nv.validate_argmax(args, kwargs)
-        nv.validate_minmax_axis(axis)
-
-        i8 = self.asi8
-        if self.hasnans:
-            mask = self._isnan
-            if mask.all() or not skipna:
-                return -1
-            i8 = i8.copy()
-            i8[mask] = 0
-        return i8.argmax()
-
     # --------------------------------------------------------------------
     # Rendering Methods