pyspark.pandas.groupby.GroupBy.ewm#
- GroupBy.ewm(com=None, span=None, halflife=None, alpha=None, min_periods=None, ignore_na=False)[source]#
Return an ewm grouper, providing ewm functionality per group.
Note
‘min_periods’ in pandas-on-Spark works as a fixed window size unlike pandas. Unlike pandas, NA is also counted as the period. This might be changed soon.
New in version 3.4.0.
- Parameters
- comfloat, optional
Specify decay in terms of center of mass. alpha = 1 / (1 + com), for com >= 0.
- spanfloat, optional
Specify decay in terms of span. alpha = 2 / (span + 1), for span >= 1.
- halflifefloat, optional
Specify decay in terms of half-life. alpha = 1 - exp(-ln(2) / halflife), for halflife > 0.
- alphafloat, optional
Specify smoothing factor alpha directly. 0 < alpha <= 1.
- min_periodsint, default None
Minimum number of observations in window required to have a value (otherwise result is NA).
- ignore_nabool, default False
Ignore missing values when calculating weights.
When
ignore_na=False(default), weights are based on absolute positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \((1-lpha)^2\) and \(1\) ifadjust=True, and \((1-lpha)^2\) and \(lpha\) ifadjust=False.When
ignore_na=True, weights are based on relative positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \(1-lpha\) and \(1\) ifadjust=True, and \(1-lpha\) and \(lpha\) ifadjust=False.