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


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