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# pandas.api.typing.DataFrameGroupBy.rolling

DataFrameGroupBy.rolling(window, min_periods=None, center=False, win_type=None, on=None, closed=None, method='single')

  Return a rolling grouper, providing rolling functionality per group.

  Allows the application of rolling window operations (e.g., moving averages) independently within each group defined by the groupby keys.

  Parameters:
    window int, timedelta, str, offset, or BaseIndexer subclass

      Interval of the moving window.

      If an integer, the delta between the start and end of each window. The number of points in the window depends on the `closed` argument.

      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. The offset must correspond to a fixed frequency (for example, `'2D'` or `'1h'`); non-fixed frequencies such as `'B'` (business day) or `'ME'` (month end) are not supported and raise `ValueError`. To learn more about the offsets & frequency strings, please see this link.

      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.

      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.

    closed str, default None

      Determines the inclusivity of points in the window

      If `'right'`, uses the window (first, last] meaning the last point is included in the calculations.

      If `'left'`, uses the window [first, last) meaning the first point is included in the calculations.

      If `'both'`, uses the window [first, last] meaning all points in the window are included in the calculations.

      If `'neither'`, uses the window (first, last) meaning the first and last points in the window are excluded from calculations.

      () and [] are referencing open and closed set notation respetively.

      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:
    pandas.api.typing.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