pandas.api.typing.DataFrameGroupBy.ewm — pandas 3.0.0rc0+33.g1fd184de2a documentation (original) (raw)

DataFrameGroupBy.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 tomean(), 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