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()	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, engine, …])	Apply an arbitrary function to each rolling window.
Rolling.aggregate(func, *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.
Window.var([ddof])	Calculate unbiased window variance.
Window.std([ddof])	Calculate window standard deviation.

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, engine, …])	Apply an arbitrary function to each expanding window.
Expanding.aggregate(func, *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

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