pyspark.pandas.Series.ewm

Series.ewm(com=None, span=None, halflife=None, alpha=None, min_periods=None, ignore_na=False)

Provide exponentially weighted window transformations.

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

com: float, optional

Specify decay in terms of center of mass. alpha = 1 / (1 + com), for com >= 0.

span: float, optional

Specify decay in terms of span. alpha = 2 / (span + 1), for span >= 1.

halflife: float, optional

Specify decay in terms of half-life. alpha = 1 - exp(-ln(2) / halflife), for halflife > 0.

alpha: float, optional

Specify smoothing factor alpha directly. 0 < alpha <= 1.

min_periods: int, default None

Minimum number of observations in window required to have a value (otherwise result is NA).

ignore_na: bool, 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 $\text{Math input error}$ and $1$ if adjust=True, and $\text{Math input error}$ and $\text{Math input error}$ if adjust=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 $\text{Math input error}$ and $1$ if adjust=True, and $\text{Math input error}$ and $\text{Math input error}$ if adjust=False.

Returns

a Window sub-classed for the operation