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, args, kwargs]) |
The rolling function’s apply function. |
Rolling.aggregate(self, arg, \*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. |
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, arg, \*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. |