diff --git a/pandas/core/base.py b/pandas/core/base.py
index e2b19a3baed33..f907089f77132 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -23,7 +23,6 @@
 
 from pandas._libs import lib
 from pandas._typing import (
-    Axis,
     AxisInt,
     DtypeObj,
     IndexLabel,
@@ -588,7 +587,8 @@ def to_numpy(
                 values = np.asarray(values, dtype=dtype)
             else:
                 values = values.copy()
-            values[np.asanyarray(self.isna())] = na_value
+
+            values[np.asanyarray(isna(self))] = na_value
 
         result = np.asarray(values, dtype=dtype)
 
@@ -608,50 +608,6 @@ def to_numpy(
     def empty(self) -> bool:
         return not self.size
 
-    def max(self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs):
-        """
-        Return the maximum value of the Index.
-
-        Parameters
-        ----------
-        axis : int, optional
-            For compatibility with NumPy. Only 0 or None are allowed.
-        skipna : bool, default True
-            Exclude NA/null values when showing the result.
-        *args, **kwargs
-            Additional arguments and keywords for compatibility with NumPy.
-
-        Returns
-        -------
-        scalar
-            Maximum value.
-
-        See Also
-        --------
-        Index.min : Return the minimum value in an Index.
-        Series.max : Return the maximum value in a Series.
-        DataFrame.max : Return the maximum values in a DataFrame.
-
-        Examples
-        --------
-        >>> idx = pd.Index([3, 2, 1])
-        >>> idx.max()
-        3
-
-        >>> idx = pd.Index(['c', 'b', 'a'])
-        >>> idx.max()
-        'c'
-
-        For a MultiIndex, the maximum is determined lexicographically.
-
-        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
-        >>> idx.max()
-        ('b', 2)
-        """
-        nv.validate_minmax_axis(axis)
-        nv.validate_max(args, kwargs)
-        return nanops.nanmax(self._values, skipna=skipna)
-
     @doc(op="max", oppose="min", value="largest")
     def argmax(
         self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
@@ -722,50 +678,6 @@ def argmax(
                 delegate, skipna=skipna
             )
 
-    def min(self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs):
-        """
-        Return the minimum value of the Index.
-
-        Parameters
-        ----------
-        axis : {None}
-            Dummy argument for consistency with Series.
-        skipna : bool, default True
-            Exclude NA/null values when showing the result.
-        *args, **kwargs
-            Additional arguments and keywords for compatibility with NumPy.
-
-        Returns
-        -------
-        scalar
-            Minimum value.
-
-        See Also
-        --------
-        Index.max : Return the maximum value of the object.
-        Series.min : Return the minimum value in a Series.
-        DataFrame.min : Return the minimum values in a DataFrame.
-
-        Examples
-        --------
-        >>> idx = pd.Index([3, 2, 1])
-        >>> idx.min()
-        1
-
-        >>> idx = pd.Index(['c', 'b', 'a'])
-        >>> idx.min()
-        'a'
-
-        For a MultiIndex, the minimum is determined lexicographically.
-
-        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
-        >>> idx.min()
-        ('a', 1)
-        """
-        nv.validate_minmax_axis(axis)
-        nv.validate_min(args, kwargs)
-        return nanops.nanmin(self._values, skipna=skipna)
-
     @doc(argmax, op="min", oppose="max", value="smallest")
     def argmin(
         self, axis: AxisInt | None = None, skipna: bool = True, *args, **kwargs
@@ -859,30 +771,6 @@ def hasnans(self) -> bool:
         # has no attribute "any"
         return bool(isna(self).any())  # type: ignore[union-attr]
 
-    def isna(self) -> npt.NDArray[np.bool_]:
-        return isna(self._values)
-
-    def _reduce(
-        self,
-        op,
-        name: str,
-        *,
-        axis: Axis = 0,
-        skipna: bool = True,
-        numeric_only=None,
-        filter_type=None,
-        **kwds,
-    ):
-        """
-        Perform the reduction type operation if we can.
-        """
-        func = getattr(self, name, None)
-        if func is None:
-            raise TypeError(
-                f"{type(self).__name__} cannot perform the operation {name}"
-            )
-        return func(skipna=skipna, **kwds)
-
     @final
     def _map_values(self, mapper, na_action=None, convert: bool = True):
         """
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index b57a8f2afee32..8b191897e9409 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -135,6 +135,7 @@
 
 from pandas.core import (
     arraylike,
+    nanops,
     ops,
 )
 from pandas.core.accessor import CachedAccessor
@@ -6984,8 +6985,46 @@ def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
                 return -1
         return super().argmax(skipna=skipna)
 
-    @doc(IndexOpsMixin.min)
     def min(self, axis=None, skipna: bool = True, *args, **kwargs):
+        """
+        Return the minimum value of the Index.
+
+        Parameters
+        ----------
+        axis : {None}
+            Dummy argument for consistency with Series.
+        skipna : bool, default True
+            Exclude NA/null values when showing the result.
+        *args, **kwargs
+            Additional arguments and keywords for compatibility with NumPy.
+
+        Returns
+        -------
+        scalar
+            Minimum value.
+
+        See Also
+        --------
+        Index.max : Return the maximum value of the object.
+        Series.min : Return the minimum value in a Series.
+        DataFrame.min : Return the minimum values in a DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index([3, 2, 1])
+        >>> idx.min()
+        1
+
+        >>> idx = pd.Index(['c', 'b', 'a'])
+        >>> idx.min()
+        'a'
+
+        For a MultiIndex, the minimum is determined lexicographically.
+
+        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
+        >>> idx.min()
+        ('a', 1)
+        """
         nv.validate_min(args, kwargs)
         nv.validate_minmax_axis(axis)
 
@@ -7007,10 +7046,49 @@ def min(self, axis=None, skipna: bool = True, *args, **kwargs):
         if not self._is_multi and not isinstance(self._values, np.ndarray):
             return self._values._reduce(name="min", skipna=skipna)
 
-        return super().min(skipna=skipna)
+        return nanops.nanmin(self._values, skipna=skipna)
 
-    @doc(IndexOpsMixin.max)
     def max(self, axis=None, skipna: bool = True, *args, **kwargs):
+        """
+        Return the maximum value of the Index.
+
+        Parameters
+        ----------
+        axis : int, optional
+            For compatibility with NumPy. Only 0 or None are allowed.
+        skipna : bool, default True
+            Exclude NA/null values when showing the result.
+        *args, **kwargs
+            Additional arguments and keywords for compatibility with NumPy.
+
+        Returns
+        -------
+        scalar
+            Maximum value.
+
+        See Also
+        --------
+        Index.min : Return the minimum value in an Index.
+        Series.max : Return the maximum value in a Series.
+        DataFrame.max : Return the maximum values in a DataFrame.
+
+        Examples
+        --------
+        >>> idx = pd.Index([3, 2, 1])
+        >>> idx.max()
+        3
+
+        >>> idx = pd.Index(['c', 'b', 'a'])
+        >>> idx.max()
+        'c'
+
+        For a MultiIndex, the maximum is determined lexicographically.
+
+        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
+        >>> idx.max()
+        ('b', 2)
+        """
+
         nv.validate_max(args, kwargs)
         nv.validate_minmax_axis(axis)
 
@@ -7032,7 +7110,7 @@ def max(self, axis=None, skipna: bool = True, *args, **kwargs):
         if not self._is_multi and not isinstance(self._values, np.ndarray):
             return self._values._reduce(name="max", skipna=skipna)
 
-        return super().max(skipna=skipna)
+        return nanops.nanmax(self._values, skipna=skipna)
 
     # --------------------------------------------------------------------
 
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 9c91badc57ce3..a5e95e10eadfe 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -240,9 +240,10 @@ def wrapper(self):
 # Series class
 
 
-# error: Definition of "max" in base class "IndexOpsMixin" is incompatible with
-# definition in base class "NDFrame"
-# error: Definition of "min" in base class "IndexOpsMixin" is incompatible with
+# error: Cannot override final attribute "ndim" (previously declared in base
+# class "NDFrame")
+# error: Cannot override final attribute "size" (previously declared in base
+# class "NDFrame")
 # definition in base class "NDFrame"
 class Series(base.IndexOpsMixin, NDFrame):  # type: ignore[misc]
     """
@@ -5395,10 +5396,8 @@ def _convert_dtypes(
         return result
 
     # error: Cannot determine type of 'isna'
-    # error: Return type "Series" of "isna" incompatible with return type "ndarray
-    # [Any, dtype[bool_]]" in supertype "IndexOpsMixin"
     @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
-    def isna(self) -> Series:  # type: ignore[override]
+    def isna(self) -> Series:
         return NDFrame.isna(self)
 
     # error: Cannot determine type of 'isna'
@@ -6092,8 +6091,7 @@ def all(
         )
 
     @doc(make_doc("min", ndim=1))
-    # error: Signature of "min" incompatible with supertype "IndexOpsMixin"
-    def min(  # type: ignore[override]
+    def min(
         self,
         axis: Axis | None = 0,
         skipna: bool = True,
@@ -6103,8 +6101,7 @@ def min(  # type: ignore[override]
         return NDFrame.min(self, axis, skipna, numeric_only, **kwargs)
 
     @doc(make_doc("max", ndim=1))
-    # error: Signature of "max" incompatible with supertype "IndexOpsMixin"
-    def max(  # type: ignore[override]
+    def max(
         self,
         axis: Axis | None = 0,
         skipna: bool = True,