DOC: Move idxmin/idxmax docstrings to shared docs

pandas/core/frame.py

@@ -10536,67 +10536,8 @@ def nunique(self, axis: Axis = 0, dropna: bool = True) -> Series:
         """
         return self.apply(Series.nunique, axis=axis, dropna=dropna)
 
+    @doc(_shared_docs["idxmin"])
     def idxmin(self, axis: Axis = 0, skipna: bool = True) -> 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 an entire row/column is NA, the result
-            will be NA.
-
-        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)
 
         res = self._reduce(
@@ -10613,67 +10554,8 @@ def idxmin(self, axis: Axis = 0, skipna: bool = True) -> Series:
         result = [index[i] if i >= 0 else np.nan for i in indices]
         return self._constructor_sliced(result, index=self._get_agg_axis(axis))
 
+    @doc(_shared_docs["idxmax"])
     def idxmax(self, axis: Axis = 0, skipna: bool = True) -> 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 an entire row/column is NA, the result
-            will be NA.
-
-        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)
 
         res = self._reduce(

pandas/core/groupby/generic.py

@@ -84,6 +84,7 @@
     all_indexes_same,
 )
 from pandas.core.series import Series
+from pandas.core.shared_docs import _shared_docs
 from pandas.core.util.numba_ import maybe_use_numba
 
 from pandas.plotting import boxplot_frame_groupby
@@ -1552,7 +1553,7 @@ def nunique(self, dropna: bool = True) -> DataFrame:
 
         return results
 
-    @Appender(DataFrame.idxmax.__doc__)
+    @doc(_shared_docs["idxmax"])
     def idxmax(self, axis=0, skipna: bool = True):
         axis = DataFrame._get_axis_number(axis)
         numeric_only = None if axis == 0 else False
@@ -1574,7 +1575,7 @@ def func(df):
         func.__name__ = "idxmax"
         return self._python_apply_general(func, self._obj_with_exclusions)
 
-    @Appender(DataFrame.idxmin.__doc__)
+    @doc(_shared_docs["idxmin"])
     def idxmin(self, axis=0, skipna: bool = True):
         axis = DataFrame._get_axis_number(axis)
         numeric_only = None if axis == 0 else False

pandas/core/shared_docs.py

@@ -734,3 +734,129 @@
         .. versionchanged:: 1.4.0
             Previously the explicit ``None`` was silently ignored.
 """
+
+_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 an entire row/column is NA, the result
+        will be NA.
+
+    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 an entire row/column is NA, the result
+        will be NA.
+
+    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
+"""