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# pandas.api.typing.SeriesGroupBy.ewm

SeriesGroupBy.ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, times=None, method='single')

  Return an ewm grouper, providing ewm functionality per group.

  The decay can be specified in terms of center of mass, span, half-life, or smoothing factor alpha.

  Parameters:
    com float, optional

      Specify decay in terms of center of mass. Alternative to `span`, `halflife`, and `alpha`.

    span float, optional

      Specify decay in terms of span.

    halflife float, str, or Timedelta, optional

      Specify decay in terms of half-life.

    alpha float, optional

      Specify smoothing factor directly.

    min_periods int, default 0

      Minimum number of observations in the window required to have a value; otherwise, result is `np.nan`.

    adjust bool, default True

      Divide by decaying adjustment factor to account for imbalance in relative weights.

    ignore_na bool, default False

      Ignore missing values when calculating weights.

    times str or array-like of datetime64, optional

      Times corresponding to the observations.

    method{‘single’, ‘table’}, default ‘single’

      Execute the operation per group independently (`'single'`) or over the entire object before regrouping (`'table'`). Only applicable to `mean()`, and only when using `engine='numba'`.

  Returns:
    pandas.api.typing.ExponentialMovingWindowGroupby

      An object that supports exponentially weighted moving transformations over each group.

  See also

  `Series.ewm`

    EWM transformations for Series.

  `DataFrame.ewm`

    EWM 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").ewm(com=0.5).mean()
                   Value
      Class
      A     0  10.000000
            1  17.500000
            2  26.153846
      B     3  40.000000
            4  47.500000
            5  56.153846