Skip to main content
`Ctrl`+`K`

# pandas.core.groupby.SeriesGroupBy.rolling

SeriesGroupBy.rolling(*args, **kwargs)

  Return a rolling grouper, providing rolling functionality per group.

  Parameters:
    windowint, 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.

      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_periodsint, 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.

    centerbool, 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_typestr, 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.

    onstr, 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.

    axisint 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.

    closedstr, 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'`, 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'`).

    methodstr {‘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