diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index f47acf579d79c..fe88cb86693e8 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -12745,10 +12745,80 @@ def nunique(self, axis: Axis = 0, dropna: bool = True) -> Series:
         """
         return self.apply(Series.nunique, axis=axis, dropna=dropna)
 
-    @doc(_shared_docs["idxmin"], numeric_only_default="False")
     def idxmin(
         self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False
     ) -> Series:
+        """
+        Return index of first occurrence of minimum over requested axis.
+
+        NA/null values are excluded.
+
+        Parameters
+        ----------
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+        skipna : bool, default True
+            Exclude NA/null values. If the entire DataFrame is NA,
+            or if ``skipna=False`` and there is an NA value, this method
+            will raise a ``ValueError``.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        Series
+            Indexes of minima along the specified axis.
+
+        Raises
+        ------
+        ValueError
+            * If the row/column is empty
+
+        See Also
+        --------
+        Series.idxmin : Return index of the minimum element.
+
+        Notes
+        -----
+        This method is the DataFrame version of ``ndarray.argmin``.
+
+        Examples
+        --------
+        Consider a dataset containing food consumption in Argentina.
+
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         {
+        ...             "consumption": [10.51, 103.11, 55.48],
+        ...             "co2_emissions": [37.2, 19.66, 1712],
+        ...         }
+        ...     },
+        ...     index=["Pork", "Wheat Products", "Beef"],
+        ... )
+
+        >>> df
+                        consumption  co2_emissions
+        Pork                  10.51         37.20
+        Wheat Products       103.11         19.66
+        Beef                  55.48       1712.00
+
+        By default, it returns the index for the minimum value in each column.
+
+        >>> df.idxmin()
+        consumption                Pork
+        co2_emissions    Wheat Products
+        dtype: object
+
+        To return the index for the minimum value in each row, use ``axis="columns"``.
+
+        >>> df.idxmin(axis="columns")
+        Pork                consumption
+        Wheat Products    co2_emissions
+        Beef                consumption
+        dtype: object
+        """
         axis = self._get_axis_number(axis)
 
         if self.empty and len(self.axes[axis]):
@@ -12782,10 +12852,80 @@ def idxmin(
         final_result = data._constructor_sliced(result, index=data._get_agg_axis(axis))
         return final_result.__finalize__(self, method="idxmin")
 
-    @doc(_shared_docs["idxmax"], numeric_only_default="False")
     def idxmax(
         self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False
     ) -> Series:
+        """
+        Return index of first occurrence of maximum over requested axis.
+
+        NA/null values are excluded.
+
+        Parameters
+        ----------
+        axis : {{0 or 'index', 1 or 'columns'}}, default 0
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+        skipna : bool, default True
+            Exclude NA/null values. If the entire DataFrame is NA,
+            or if ``skipna=False`` and there is an NA value, this method
+            will raise a ``ValueError``.
+        numeric_only : bool, default False
+            Include only `float`, `int` or `boolean` data.
+
+            .. versionadded:: 1.5.0
+
+        Returns
+        -------
+        Series
+            Indexes of maxima along the specified axis.
+
+        Raises
+        ------
+        ValueError
+            * If the row/column is empty
+
+        See Also
+        --------
+        Series.idxmax : Return index of the maximum element.
+
+        Notes
+        -----
+        This method is the DataFrame version of ``ndarray.argmax``.
+
+        Examples
+        --------
+        Consider a dataset containing food consumption in Argentina.
+
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         {
+        ...             "consumption": [10.51, 103.11, 55.48],
+        ...             "co2_emissions": [37.2, 19.66, 1712],
+        ...         }
+        ...     },
+        ...     index=["Pork", "Wheat Products", "Beef"],
+        ... )
+
+        >>> df
+                        consumption  co2_emissions
+        Pork                  10.51         37.20
+        Wheat Products       103.11         19.66
+        Beef                  55.48       1712.00
+
+        By default, it returns the index for the maximum value in each column.
+
+        >>> df.idxmax()
+        consumption     Wheat Products
+        co2_emissions             Beef
+        dtype: object
+
+        To return the index for the maximum value in each row, use ``axis="columns"``.
+
+        >>> df.idxmax(axis="columns")
+        Pork              co2_emissions
+        Wheat Products     consumption
+        Beef              co2_emissions
+        dtype: object
+        """
         axis = self._get_axis_number(axis)
 
         if self.empty and len(self.axes[axis]):
diff --git a/pandas/core/shared_docs.py b/pandas/core/shared_docs.py
index 5725b96f66cd4..cb0c3d241534c 100644
--- a/pandas/core/shared_docs.py
+++ b/pandas/core/shared_docs.py
@@ -649,133 +649,3 @@
     3  3  d  e
     4  4  e  e
 """
-
-_shared_docs["idxmin"] = """
-    Return index of first occurrence of minimum over requested axis.
-
-    NA/null values are excluded.
-
-    Parameters
-    ----------
-    axis : {{0 or 'index', 1 or 'columns'}}, default 0
-        The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
-    skipna : bool, default True
-        Exclude NA/null values. If the entire Series is NA, or if ``skipna=False``
-        and there is an NA value, this method will raise a ``ValueError``.
-    numeric_only : bool, default {numeric_only_default}
-        Include only `float`, `int` or `boolean` data.
-
-        .. versionadded:: 1.5.0
-
-    Returns
-    -------
-    Series
-        Indexes of minima along the specified axis.
-
-    Raises
-    ------
-    ValueError
-        * If the row/column is empty
-
-    See Also
-    --------
-    Series.idxmin : Return index of the minimum element.
-
-    Notes
-    -----
-    This method is the DataFrame version of ``ndarray.argmin``.
-
-    Examples
-    --------
-    Consider a dataset containing food consumption in Argentina.
-
-    >>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
-    ...                   'co2_emissions': [37.2, 19.66, 1712]}},
-    ...                   index=['Pork', 'Wheat Products', 'Beef'])
-
-    >>> df
-                    consumption  co2_emissions
-    Pork                  10.51         37.20
-    Wheat Products       103.11         19.66
-    Beef                  55.48       1712.00
-
-    By default, it returns the index for the minimum value in each column.
-
-    >>> df.idxmin()
-    consumption                Pork
-    co2_emissions    Wheat Products
-    dtype: object
-
-    To return the index for the minimum value in each row, use ``axis="columns"``.
-
-    >>> df.idxmin(axis="columns")
-    Pork                consumption
-    Wheat Products    co2_emissions
-    Beef                consumption
-    dtype: object
-"""
-
-_shared_docs["idxmax"] = """
-    Return index of first occurrence of maximum over requested axis.
-
-    NA/null values are excluded.
-
-    Parameters
-    ----------
-    axis : {{0 or 'index', 1 or 'columns'}}, default 0
-        The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
-    skipna : bool, default True
-        Exclude NA/null values. If the entire Series is NA, or if ``skipna=False``
-        and there is an NA value, this method will raise a ``ValueError``.
-    numeric_only : bool, default {numeric_only_default}
-        Include only `float`, `int` or `boolean` data.
-
-        .. versionadded:: 1.5.0
-
-    Returns
-    -------
-    Series
-        Indexes of maxima along the specified axis.
-
-    Raises
-    ------
-    ValueError
-        * If the row/column is empty
-
-    See Also
-    --------
-    Series.idxmax : Return index of the maximum element.
-
-    Notes
-    -----
-    This method is the DataFrame version of ``ndarray.argmax``.
-
-    Examples
-    --------
-    Consider a dataset containing food consumption in Argentina.
-
-    >>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
-    ...                   'co2_emissions': [37.2, 19.66, 1712]}},
-    ...                   index=['Pork', 'Wheat Products', 'Beef'])
-
-    >>> df
-                    consumption  co2_emissions
-    Pork                  10.51         37.20
-    Wheat Products       103.11         19.66
-    Beef                  55.48       1712.00
-
-    By default, it returns the index for the maximum value in each column.
-
-    >>> df.idxmax()
-    consumption     Wheat Products
-    co2_emissions             Beef
-    dtype: object
-
-    To return the index for the maximum value in each row, use ``axis="columns"``.
-
-    >>> df.idxmax(axis="columns")
-    Pork              co2_emissions
-    Wheat Products     consumption
-    Beef              co2_emissions
-    dtype: object
-"""