DOC: add See Also section to groupby.DataFrameGroupBy.prod

ci/code_checks.sh

@@ -235,7 +235,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
-        -i "pandas.core.groupby.DataFrameGroupBy.prod SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.sum SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
@@ -252,7 +251,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
-        -i "pandas.core.groupby.SeriesGroupBy.prod SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.sum SA01" \
         -i "pandas.core.resample.Resampler.__iter__ RT03,SA01" \

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.
 
@@ -3029,16 +3003,38 @@ 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.
+
+        See Also
+        --------
+        Series.prod : Return the product of the values over the requested axis.
+        DataFrame.prod : Return the product of the values over the requested axis.
+
+        Examples
+        --------
         For SeriesGroupBy:
 
-        >>> lst = ['a', 'a', 'b', 'b']
+        >>> lst = ["a", "a", "b", "b"]
         >>> ser = pd.Series([1, 2, 3, 4], index=lst)
         >>> ser
         a    1
@@ -3054,8 +3050,11 @@ def sum(
         For DataFrameGroupBy:
 
         >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
-        >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
-        ...                   index=["tiger", "leopard", "cheetah", "lion"])
+        >>> df = pd.DataFrame(
+        ...     data,
+        ...     columns=["a", "b", "c"],
+        ...     index=["tiger", "leopard", "cheetah", "lion"],
+        ... )
         >>> df
                   a  b  c
           tiger   1  8  2
@@ -3066,10 +3065,8 @@ 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
+        """
         return self._agg_general(
             numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
         )