by.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
    * [ pandas.core.groupby.SeriesGroupBy.skew ](pandas.core.groupby.SeriesGroupBy.skew.html)
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.DataFrameGroupBy.rolling  #

DataFrameGroupBy.  rolling  (  _ *  args  _ , _ **  kwargs  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L3747-L3885) #

    

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