pandas.Series.ewm — pandas 0.24.0rc1 documentation (original) (raw)
Series. ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0)[source]¶
Provides exponential weighted functions.
New in version 0.18.0.
| Parameters: | com : float, optional Specify decay in terms of center of mass,\(\alpha = 1 / (1 + com),\text{ for } com \geq 0\) span : float, optional Specify decay in terms of span,\(\alpha = 2 / (span + 1),\text{ for } span \geq 1\) halflife : float, optional Specify decay in terms of half-life,\(\alpha = 1 - exp(log(0.5) / halflife),\text{ for } halflife > 0\) alpha : float, optional Specify smoothing factor \(\alpha\) directly,\(0 < \alpha \leq 1\) New in version 0.18.0. min_periods : int, default 0 Minimum number of observations in window required to have a value (otherwise result is NA). adjust : bool, default True Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average) ignore_na : bool, default False Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior |
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| Returns: | a Window sub-classed for the particular operation |
See also
Provides rolling window calculations.
Provides expanding transformations.
Notes
Exactly one of center of mass, span, half-life, and alpha must be provided. Allowed values and relationship between the parameters are specified in the parameter descriptions above; see the link at the end of this section for a detailed explanation.
When adjust is True (default), weighted averages are calculated using weights (1-alpha)**(n-1), (1-alpha)**(n-2), …, 1-alpha, 1.
When adjust is False, weighted averages are calculated recursively as:
weighted_average[0] = arg[0]; weighted_average[i] = (1-alpha)*weighted_average[i-1] + alpha*arg[i].
When ignore_na is False (default), weights are based on absolute positions. For example, the weights of x and y used in calculating the final weighted average of [x, None, y] are (1-alpha)**2 and 1 (if adjust is True), and (1-alpha)**2 and alpha (if adjust is False).
When ignore_na is True (reproducing pre-0.15.0 behavior), weights are based on relative positions. For example, the weights of x and y used in calculating the final weighted average of [x, None, y] are 1-alpha and 1 (if adjust is True), and 1-alpha and alpha (if adjust is False).
More details can be found athttp://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows
Examples
df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) B 0 0.0 1 1.0 2 2.0 3 NaN 4 4.0
df.ewm(com=0.5).mean() B 0 0.000000 1 0.750000 2 1.615385 3 1.615385 4 3.670213