DOC: Fix errors in pandas.Series.argmin

pandas/core/base.py

@@ -927,16 +927,23 @@ def max(self, axis=None, skipna=True, *args, **kwargs):
         nv.validate_max(args, kwargs)
         return nanops.nanmax(self._values, skipna=skipna)
 
+    @doc(
+        op="max",
+        oppose="min",
+        value="largest",
+        position="third",
+        example_values="2",
+    )
     def argmax(self, axis=None, skipna=True, *args, **kwargs):
         """
-        Return int position of the largest value in the Series.
+        Return int position of the {value} value in the Series.
 
-        If the maximum is achieved in multiple locations,
+        If the {op}imum is achieved in multiple locations,
         the first row position is returned.
 
         Parameters
         ----------
-        axis : {None}
+        axis : {{None}}
             Dummy argument for consistency with Series.
         skipna : bool, default True
             Exclude NA/null values when showing the result.
@@ -946,32 +953,32 @@ def argmax(self, axis=None, skipna=True, *args, **kwargs):
         Returns
         -------
         int
-            Row position of the maximum values.
+            Row position of the {op}imum value.
 
         See Also
         --------
-        numpy.ndarray.argmax : Equivalent method for numpy arrays.
-        Series.argmin : Similar method, but returning the minimum.
+        numpy.ndarray.arg{op} : Equivalent method for numpy arrays.
+        Series.arg{oppose} : Similar method, but returning the {oppose}imum.
         Series.idxmax : Return index label of the maximum values.
         Series.idxmin : Return index label of the minimum values.
 
         Examples
         --------
         Consider dataset containing cereal calories
 
-        >>> s = pd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0,
-        ...                'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0})
+        >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,
+        ...                'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})
         >>> s
         Corn Flakes              100.0
         Almond Delight           110.0
         Cinnamon Toast Crunch    120.0
         Cocoa Puff               110.0
         dtype: float64
 
-        >>> s.argmax()
-        2
+        >>> s.arg{op}()
+        {example_values}
 
-        The maximum cereal calories is in the third element,
+        The {op}imum cereal calories is the {position} element,
         since series is zero-indexed.
         """
         nv.validate_minmax_axis(axis)
@@ -1019,25 +1026,15 @@ def min(self, axis=None, skipna=True, *args, **kwargs):
         nv.validate_min(args, kwargs)
         return nanops.nanmin(self._values, skipna=skipna)
 
+    @doc(
+        argmax,
+        op="min",
+        oppose="max",
+        value="smallest",
+        position="first",
+        example_values="0",
+    )
     def argmin(self, axis=None, skipna=True, *args, **kwargs):
-        """
-        Return a ndarray of the minimum argument indexer.
-
-        Parameters
-        ----------
-        axis : {None}
-            Dummy argument for consistency with Series.
-        skipna : bool, default True
-
-        Returns
-        -------
-        numpy.ndarray
-
-        See Also
-        --------
-        numpy.ndarray.argmin : Return indices of the minimum values along
-            the given axis.
-        """
         nv.validate_minmax_axis(axis)
         nv.validate_argmax_with_skipna(skipna, args, kwargs)
         return nanops.nanargmin(self._values, skipna=skipna)