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(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, …])	The rolling function’s apply function.
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, args, kwargs])	The expanding function’s apply function.
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

EWM.mean(self, \*args, \*\*kwargs)	Exponential weighted moving average.
EWM.std(self[, bias])	Exponential weighted moving stddev.
EWM.var(self[, bias])	Exponential weighted moving variance.
EWM.corr(self[, other, pairwise])	Exponential weighted sample correlation.
EWM.cov(self[, other, pairwise, bias])	Exponential weighted sample covariance.

Window Indexer

Base class for defining custom window boundaries.

api.indexers.BaseIndexer([index_array, …])

Base class for window bounds calculations

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