Update Groupby.prod

Author: ktseng4096

pandas/core/groupby/groupby.py

@@ -164,32 +164,6 @@ class providing the base-class of operations.
             to each row or column of a DataFrame.
 """
 
-_groupby_agg_method_template = """
-Compute {fname} of group values.
-
-Parameters
-----------
-numeric_only : bool, default {no}
-    Include only float, int, boolean columns.
-
-    .. versionchanged:: 2.0.0
-
-        numeric_only no longer accepts ``None``.
-
-min_count : int, default {mc}
-    The required number of valid values to perform the operation. If fewer
-    than ``min_count`` non-NA values are present the result will be NA.
-
-Returns
--------
-Series or DataFrame
-    Computed {fname} of values within each group.
-
-Examples
---------
-{example}
-"""
-
 _groupby_agg_method_engine_template = """
 Compute {fname} of group values.
 
@@ -3029,13 +3003,30 @@ def sum(
             return result
 
     @final
-    @doc(
-        _groupby_agg_method_template,
-        fname="prod",
-        no=False,
-        mc=0,
-        example=dedent(
-            """\
+    def prod(self, numeric_only: bool = False, min_count: int = 0) -> NDFrameT:
+        """
+        Compute prod of group values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None``.
+
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` non-NA values are present the result will be NA.
+
+        Returns
+        -------
+        Series or DataFrame
+            Computed prod of values within each group.
+
+        Examples
+        --------
         For SeriesGroupBy:
 
         >>> lst = ['a', 'a', 'b', 'b']
@@ -3066,10 +3057,12 @@ def sum(
              b    c
         a
         1   16   10
-        2   30   72"""
-        ),
-    )
-    def prod(self, numeric_only: bool = False, min_count: int = 0) -> NDFrameT:
+        2   30   72
+
+        See Also
+        --------
+        DataFrame.prod : Return the product of the values over the requested axis.
+        """
         return self._agg_general(
             numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
         )