DOC: Add rolling in reference/groupby.rst (#51119)

* Add rolling in reference/groupby.rst

* Add more docstring

* Add see also

* Change See also

* Fix ci

* Fix ci

* Remove substituion

Author: luke396

doc/source/reference/groupby.rst

@@ -97,6 +97,7 @@ Function application
    DataFrameGroupBy.quantile
    DataFrameGroupBy.rank
    DataFrameGroupBy.resample
+   DataFrameGroupBy.rolling
    DataFrameGroupBy.sample
    DataFrameGroupBy.sem
    DataFrameGroupBy.shift
@@ -152,6 +153,7 @@ Function application
    SeriesGroupBy.quantile
    SeriesGroupBy.rank
    SeriesGroupBy.resample
+   SeriesGroupBy.rolling
    SeriesGroupBy.sample
    SeriesGroupBy.sem
    SeriesGroupBy.shift

pandas/core/groupby/groupby.py

@@ -2650,11 +2650,134 @@ def resample(self, rule, *args, **kwargs):
         return get_resampler_for_grouping(self, rule, *args, **kwargs)
 
     @final
-    @Substitution(name="groupby")
-    @Appender(_common_see_also)
     def rolling(self, *args, **kwargs) -> RollingGroupby:
         """
         Return a rolling grouper, providing rolling functionality per group.
+
+        Parameters
+        ----------
+        window : int, timedelta, str, offset, or BaseIndexer subclass
+            Size of the moving window.
+
+            If an integer, the fixed number of observations used for
+            each window.
+
+            If a timedelta, str, or offset, the time period of each window. Each
+            window will be a variable sized based on the observations included in
+            the time-period. This is only valid for datetimelike indexes.
+            To learn more about the offsets & frequency strings, please see `this link
+            <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
+
+            If a BaseIndexer subclass, the window boundaries
+            based on the defined ``get_window_bounds`` method. Additional rolling
+            keyword arguments, namely ``min_periods``, ``center``, ``closed`` and
+            ``step`` will be passed to ``get_window_bounds``.
+
+        min_periods : int, default None
+            Minimum number of observations in window required to have a value;
+            otherwise, result is ``np.nan``.
+
+            For a window that is specified by an offset,
+            ``min_periods`` will default to 1.
+
+            For a window that is specified by an integer, ``min_periods`` will default
+            to the size of the window.
+
+        center : bool, default False
+            If False, set the window labels as the right edge of the window index.
+
+            If True, set the window labels as the center of the window index.
+
+        win_type : str, default None
+            If ``None``, all points are evenly weighted.
+
+            If a string, it must be a valid `scipy.signal window function
+            <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>`__.
+
+            Certain Scipy window types require additional parameters to be passed
+            in the aggregation function. The additional parameters must match
+            the keywords specified in the Scipy window type method signature.
+
+        on : str, optional
+            For a DataFrame, a column label or Index level on which
+            to calculate the rolling window, rather than the DataFrame's index.
+
+            Provided integer column is ignored and excluded from result since
+            an integer index is not used to calculate the rolling window.
+
+        axis : int or str, default 0
+            If ``0`` or ``'index'``, roll across the rows.
+
+            If ``1`` or ``'columns'``, roll across the columns.
+
+            For `Series` this parameter is unused and defaults to 0.
+
+        closed : str, default None
+            If ``'right'``, the first point in the window is excluded from calculations.
+
+            If ``'left'``, the last point in the window is excluded from calculations.
+
+            If ``'both'``, the no points in the window are excluded from calculations.
+
+            If ``'neither'``, the first and last points in the window are excluded
+            from calculations.
+
+            Default ``None`` (``'right'``).
+
+        method : str {'single', 'table'}, default 'single'
+            Execute the rolling operation per single column or row (``'single'``)
+            or over the entire object (``'table'``).
+
+            This argument is only implemented when specifying ``engine='numba'``
+            in the method call.
+
+        Returns
+        -------
+        RollingGroupby
+            Return a new grouper with our rolling appended.
+
+        See Also
+        --------
+        Series.rolling : Calling object with Series data.
+        DataFrame.rolling : Calling object with DataFrames.
+        Series.groupby : Apply a function groupby to a Series.
+        DataFrame.groupby : Apply a function groupby.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'A': [1, 1, 2, 2],
+        ...                    'B': [1, 2, 3, 4],
+        ...                    'C': [0.362, 0.227, 1.267, -0.562]})
+        >>> df
+              A  B      C
+        0     1  1  0.362
+        1     1  2  0.227
+        2     2  3  1.267
+        3     2  4 -0.562
+
+        >>> df.groupby('A').rolling(2).sum()
+            B      C
+        A
+        1 0  NaN    NaN
+          1  3.0  0.589
+        2 2  NaN    NaN
+          3  7.0  0.705
+
+        >>> df.groupby('A').rolling(2, min_periods=1).sum()
+            B      C
+        A
+        1 0  1.0  0.362
+          1  3.0  0.589
+        2 2  3.0  1.267
+          3  7.0  0.705
+
+        >>> df.groupby('A').rolling(2, on='B').sum()
+            B      C
+        A
+        1 0  1    NaN
+          1  2  0.589
+        2 2  3    NaN
+          3  4  0.705
         """
         from pandas.core.window import RollingGroupby