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.
.rolling
pandas.DataFrame.rolling()
pandas.Series.rolling()
.expanding
pandas.DataFrame.expanding()
pandas.Series.expanding()
.ewm
pandas.DataFrame.ewm()
pandas.Series.ewm()
Rolling.count(self)
Rolling.count
The rolling count of any non-NaN observations inside the window.
Rolling.sum(self, \*args, \*\*kwargs)
Rolling.sum
Calculate rolling sum of given DataFrame or Series.
Rolling.mean(self, \*args, \*\*kwargs)
Rolling.mean
Calculate the rolling mean of the values.
Rolling.median(self, \*\*kwargs)
Rolling.median
Calculate the rolling median.
Rolling.var(self[, ddof])
Rolling.var
Calculate unbiased rolling variance.
Rolling.std(self[, ddof])
Rolling.std
Calculate rolling standard deviation.
Rolling.min(self, \*args, \*\*kwargs)
Rolling.min
Calculate the rolling minimum.
Rolling.max(self, \*args, \*\*kwargs)
Rolling.max
Calculate the rolling maximum.
Rolling.corr(self[, other, pairwise])
Rolling.corr
Calculate rolling correlation.
Rolling.cov(self[, other, pairwise, ddof])
Rolling.cov
Calculate the rolling sample covariance.
Rolling.skew(self, \*\*kwargs)
Rolling.skew
Unbiased rolling skewness.
Rolling.kurt(self, \*\*kwargs)
Rolling.kurt
Calculate unbiased rolling kurtosis.
Rolling.apply(self, func[, raw, engine, …])
Rolling.apply
Apply an arbitrary function to each rolling window.
Rolling.aggregate(self, func, \*args, \*\*kwargs)
Rolling.aggregate
Aggregate using one or more operations over the specified axis.
Rolling.quantile(self, quantile[, interpolation])
Rolling.quantile
Calculate the rolling quantile.
Window.mean(self, \*args, \*\*kwargs)
Window.mean
Calculate the window mean of the values.
Window.sum(self, \*args, \*\*kwargs)
Window.sum
Calculate window sum of given DataFrame or Series.
Window.var(self[, ddof])
Window.var
Calculate unbiased window variance.
Window.std(self[, ddof])
Window.std
Calculate window standard deviation.
Expanding.count(self, \*\*kwargs)
Expanding.count
The expanding count of any non-NaN observations inside the window.
Expanding.sum(self, \*args, \*\*kwargs)
Expanding.sum
Calculate expanding sum of given DataFrame or Series.
Expanding.mean(self, \*args, \*\*kwargs)
Expanding.mean
Calculate the expanding mean of the values.
Expanding.median(self, \*\*kwargs)
Expanding.median
Calculate the expanding median.
Expanding.var(self[, ddof])
Expanding.var
Calculate unbiased expanding variance.
Expanding.std(self[, ddof])
Expanding.std
Calculate expanding standard deviation.
Expanding.min(self, \*args, \*\*kwargs)
Expanding.min
Calculate the expanding minimum.
Expanding.max(self, \*args, \*\*kwargs)
Expanding.max
Calculate the expanding maximum.
Expanding.corr(self[, other, pairwise])
Expanding.corr
Calculate expanding correlation.
Expanding.cov(self[, other, pairwise, ddof])
Expanding.cov
Calculate the expanding sample covariance.
Expanding.skew(self, \*\*kwargs)
Expanding.skew
Unbiased expanding skewness.
Expanding.kurt(self, \*\*kwargs)
Expanding.kurt
Calculate unbiased expanding kurtosis.
Expanding.apply(self, func, raw, engine, …)
Expanding.apply
Apply an arbitrary function to each expanding window.
Expanding.aggregate(self, func, \*args, …)
Expanding.aggregate
Expanding.quantile(self, quantile[, …])
Expanding.quantile
Calculate the expanding quantile.
EWM.mean(self, \*args, \*\*kwargs)
EWM.mean
Exponential weighted moving average.
EWM.std(self[, bias])
EWM.std
Exponential weighted moving stddev.
EWM.var(self[, bias])
EWM.var
Exponential weighted moving variance.
EWM.corr(self[, other, pairwise])
EWM.corr
Exponential weighted sample correlation.
EWM.cov(self[, other, pairwise, bias])
EWM.cov
Exponential weighted sample covariance.
Base class for defining custom window boundaries.
api.indexers.BaseIndexer([index_array, …])
api.indexers.BaseIndexer
Base class for window bounds calculations.