DOC: Add documentation for groupby.expanding()

doc/source/reference/groupby.rst

@@ -80,6 +80,7 @@ Function application
    DataFrameGroupBy.describe
    DataFrameGroupBy.diff
    DataFrameGroupBy.ewm
+   DataFrameGroupBy.expanding
    DataFrameGroupBy.ffill
    DataFrameGroupBy.first
    DataFrameGroupBy.head
@@ -132,6 +133,7 @@ Function application
    SeriesGroupBy.describe
    SeriesGroupBy.diff
    SeriesGroupBy.ewm
+   SeriesGroupBy.expanding
    SeriesGroupBy.ffill
    SeriesGroupBy.first
    SeriesGroupBy.head

pandas/core/groupby/groupby.py

@@ -3803,16 +3803,58 @@ def rolling(
         )
 
     @final
-    @Substitution(name="groupby")
-    @Appender(_common_see_also)
     def expanding(self, *args, **kwargs) -> ExpandingGroupby:
         """
-        Return an expanding grouper, providing expanding
-        functionality per group.
+        Return an expanding grouper, providing expanding functionality per group.
+
+        Arguments are the same as `:meth:DataFrame.rolling` except that ``step`` cannot
+        be specified.
+
+        Parameters
+        ----------
+        *args : tuple
+            Positional arguments passed to the expanding window constructor.
+        **kwargs : dict
+            Keyword arguments passed to the expanding window constructor.
 
         Returns
         -------
         pandas.api.typing.ExpandingGroupby
+            An object that supports expanding transformations over each group.
+
+        See Also
+        --------
+        Series.expanding : Expanding transformations for Series.
+        DataFrame.expanding : Expanding transformations for DataFrames.
+        Series.groupby : Apply a function groupby to a Series.
+        DataFrame.groupby : Apply a function groupby.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame(
+        ...     {
+        ...         "Class": ["A", "A", "A", "B", "B", "B"],
+        ...         "Value": [10, 20, 30, 40, 50, 60],
+        ...     }
+        ... )
+        >>> df
+          Class  Value
+        0     A     10
+        1     A     20
+        2     A     30
+        3     B     40
+        4     B     50
+        5     B     60
+
+        >>> df.groupby("Class").expanding().mean()
+                 Value
+        Class
+        A     0   10.0
+              1   15.0
+              2   20.0
+        B     3   40.0
+              4   45.0
+              5   50.0
         """
         from pandas.core.window import ExpandingGroupby