cudf.DataFrame.ewm#

DataFrame.ewm(com: float | None = None, span: float | None = None, halflife: float | None = None, alpha: float | None = None, min_periods: int | None = 0, adjust: bool = True, ignore_na: bool = False, axis: int = 0, times: str | ndarray | None = None, method: Literal['single', 'table'] = 'single')[source]#

Provide exponential weighted (EW) functions. Available EW functions: mean() Exactly one parameter: com, span, halflife, or alpha must be provided.

Parameters:
comfloat, optional

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

spanfloat, optional

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

halflifefloat, str, timedelta, optional

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

alphafloat, optional

Specify smoothing factor \(\alpha\) directly, \(0 < \alpha \leq 1\).

min_periodsint, default 0

Not Supported

adjustbool, default True

Controls assumptions about the first value in the sequence. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ewm.html for details.

ignore_nabool, default False

Not Supported

axis{0, 1}, default 0

Not Supported

timesstr, np.ndarray, Series, default None

Not Supported

Returns:
ExponentialMovingWindow object

Notes

cuDF input data may contain both nulls and nan values. For the purposes of this method, they are taken to have the same meaning, meaning nulls in cuDF will affect the result the same way that nan values would using the equivalent pandas method.

Pandas Compatibility Note

pandas.DataFrame.ewm()

The parameters min_periods, ignore_na, axis, and times are not yet supported. Behavior is defined only for data that begins with a valid (non-null) element.

Currently, only mean is a supported method.

Examples

>>> df = cudf.DataFrame({'B': [0, 1, 2, cudf.NA, 4]})
>>> df
      B
0     0
1     1
2     2
3  <NA>
4     4
>>> df.ewm(com=0.5).mean()
          B
0  0.000000
1  0.750000
2  1.615385
3  1.615385
4  3.670213

>>> df.ewm(com=0.5, adjust=False).mean()
          B
0  0.000000
1  0.666667
2  1.555556
3  1.555556
4  3.650794