Window

Rolling objects are returned by .rolling calls: pandas.DataFrame.rolling(), pandas.Series.rolling(), etc. Expanding objects are returned by .expanding calls: pandas.DataFrame.expanding(), pandas.Series.expanding(), etc. EWM objects are returned by .ewm calls: pandas.DataFrame.ewm(), pandas.Series.ewm(), etc.

Standard moving window functions

Rolling.count()

The rolling count of any non-NaN observations inside the window.

Rolling.sum(*args, **kwargs)

Calculate rolling sum of given DataFrame or Series.

Rolling.mean(*args, **kwargs)

Calculate the rolling mean of the values.

Rolling.median(**kwargs)

Calculate the rolling median.

Rolling.var([ddof])

Calculate unbiased rolling variance.

Rolling.std([ddof])

Calculate rolling standard deviation.

Rolling.min(*args, **kwargs)

Calculate the rolling minimum.

Rolling.max(*args, **kwargs)

Calculate the rolling maximum.

Rolling.corr([other, pairwise])

Calculate rolling correlation.

Rolling.cov([other, pairwise, ddof])

Calculate the rolling sample covariance.

Rolling.skew(**kwargs)

Unbiased rolling skewness.

Rolling.kurt(**kwargs)

Calculate unbiased rolling kurtosis.

Rolling.apply(func[, raw, args, kwargs])

The rolling function’s apply function.

Rolling.aggregate(arg, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Rolling.quantile(quantile[, interpolation])

Calculate the rolling quantile.

Window.mean(*args, **kwargs)

Calculate the window mean of the values.

Window.sum(*args, **kwargs)

Calculate window sum of given DataFrame or Series.

Standard expanding window functions

Expanding.count(**kwargs)

The expanding count of any non-NaN observations inside the window.

Expanding.sum(*args, **kwargs)

Calculate expanding sum of given DataFrame or Series.

Expanding.mean(*args, **kwargs)

Calculate the expanding mean of the values.

Expanding.median(**kwargs)

Calculate the expanding median.

Expanding.var([ddof])

Calculate unbiased expanding variance.

Expanding.std([ddof])

Calculate expanding standard deviation.

Expanding.min(*args, **kwargs)

Calculate the expanding minimum.

Expanding.max(*args, **kwargs)

Calculate the expanding maximum.

Expanding.corr([other, pairwise])

Calculate expanding correlation.

Expanding.cov([other, pairwise, ddof])

Calculate the expanding sample covariance.

Expanding.skew(**kwargs)

Unbiased expanding skewness.

Expanding.kurt(**kwargs)

Calculate unbiased expanding kurtosis.

Expanding.apply(func[, raw, args, kwargs])

The expanding function’s apply function.

Expanding.aggregate(arg, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Expanding.quantile(quantile[, interpolation])

Calculate the expanding quantile.

Exponentially-weighted moving window functions

EWM.mean(*args, **kwargs)

Exponential weighted moving average.

EWM.std([bias])

Exponential weighted moving stddev.

EWM.var([bias])

Exponential weighted moving variance.

EWM.corr([other, pairwise])

Exponential weighted sample correlation.

EWM.cov([other, pairwise, bias])

Exponential weighted sample covariance.

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