pandas.core.groupby.SeriesGroupBy.ewm#

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

Return an ewm grouper, providing ewm functionality per group.

Parameters:

comfloat, optional: Specify decay in terms of center of mass. Alternative to span, halflife, and alpha.
spanfloat, optional: Specify decay in terms of span.
halflifefloat, str, or Timedelta, optional: Specify decay in terms of half-life.
alphafloat, optional: Specify smoothing factor directly.
min_periodsint, default 0: Minimum number of observations in the window required to have a value; otherwise, result is np.nan.
adjustbool, default True: Divide by decaying adjustment factor to account for imbalance in relative weights.
ignore_nabool, default False: Ignore missing values when calculating weights.
timesstr 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

pandas.core.groupby.SeriesGroupBy.ewm#

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