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. ExponentialMovingWindow objects are returned by .ewm calls: pandas.DataFrame.ewm(), pandas.Series.ewm(), etc.

Standard moving window functions

Rolling.count(self)	The rolling count of any non-NaN observations inside the window.
Rolling.sum(self, \*args, \*\*kwargs)	Calculate rolling sum of given DataFrame or Series.
Rolling.mean(self, \*args, \*\*kwargs)	Calculate the rolling mean of the values.
Rolling.median(self, \*\*kwargs)	Calculate the rolling median.
Rolling.var(self[, ddof])	Calculate unbiased rolling variance.
Rolling.std(self[, ddof])	Calculate rolling standard deviation.
Rolling.min(self, \*args, \*\*kwargs)	Calculate the rolling minimum.
Rolling.max(self, \*args, \*\*kwargs)	Calculate the rolling maximum.
Rolling.corr(self[, other, pairwise])	Calculate rolling correlation.
Rolling.cov(self[, other, pairwise, ddof])	Calculate the rolling sample covariance.
Rolling.skew(self, \*\*kwargs)	Unbiased rolling skewness.
Rolling.kurt(self, \*\*kwargs)	Calculate unbiased rolling kurtosis.
Rolling.apply(self, func[, raw, engine, …])	Apply an arbitrary function to each rolling window.
Rolling.aggregate(self, func, \*args, \*\*kwargs)	Aggregate using one or more operations over the specified axis.
Rolling.quantile(self, quantile[, interpolation])	Calculate the rolling quantile.
Window.mean(self, \*args, \*\*kwargs)	Calculate the window mean of the values.
Window.sum(self, \*args, \*\*kwargs)	Calculate window sum of given DataFrame or Series.
Window.var(self[, ddof])	Calculate unbiased window variance.
Window.std(self[, ddof])	Calculate window standard deviation.

Standard expanding window functions

Expanding.count(self, \*\*kwargs)	The expanding count of any non-NaN observations inside the window.
Expanding.sum(self, \*args, \*\*kwargs)	Calculate expanding sum of given DataFrame or Series.
Expanding.mean(self, \*args, \*\*kwargs)	Calculate the expanding mean of the values.
Expanding.median(self, \*\*kwargs)	Calculate the expanding median.
Expanding.var(self[, ddof])	Calculate unbiased expanding variance.
Expanding.std(self[, ddof])	Calculate expanding standard deviation.
Expanding.min(self, \*args, \*\*kwargs)	Calculate the expanding minimum.
Expanding.max(self, \*args, \*\*kwargs)	Calculate the expanding maximum.
Expanding.corr(self[, other, pairwise])	Calculate expanding correlation.
Expanding.cov(self[, other, pairwise, ddof])	Calculate the expanding sample covariance.
Expanding.skew(self, \*\*kwargs)	Unbiased expanding skewness.
Expanding.kurt(self, \*\*kwargs)	Calculate unbiased expanding kurtosis.
Expanding.apply(self, func, raw, engine, …)	Apply an arbitrary function to each expanding window.
Expanding.aggregate(self, func, \*args, …)	Aggregate using one or more operations over the specified axis.
Expanding.quantile(self, quantile[, …])	Calculate the expanding quantile.

Exponentially-weighted moving window functions

ExponentialMovingWindow.mean(self, \*args, …)	Exponential weighted moving average.
ExponentialMovingWindow.std(self, bias, …)	Exponential weighted moving stddev.
ExponentialMovingWindow.var(self, bias, …)	Exponential weighted moving variance.
ExponentialMovingWindow.corr(self, other, …)	Exponential weighted sample correlation.
ExponentialMovingWindow.cov(self, other, …)	Exponential weighted sample covariance.

Window indexer

Base class for defining custom window boundaries.

api.indexers.BaseIndexer([index_array, …])	Base class for window bounds calculations.
api.indexers.FixedForwardWindowIndexer([…])	Creates window boundaries for fixed-length windows that include the current row.
api.indexers.VariableOffsetWindowIndexer([…])	Calculate window boundaries based on a non-fixed offset such as a BusinessDay

pandas.tseries.frequencies.to_offset pandas.core.window.rolling.Rolling.count