Window — pandas 0.24.2 documentation (original) (raw)
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() | 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, args, kwargs]) | The rolling function’s apply function. |
| Rolling.aggregate(arg, *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. |
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, args, kwargs]) | The expanding function’s apply function. |
| Expanding.aggregate(arg, *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¶
| EWM.mean(*args, **kwargs) | Exponential weighted moving average. |
|---|---|
| EWM.std([bias]) | Exponential weighted moving stddev. |
| EWM.var([bias]) | Exponential weighted moving variance. |
| EWM.corr([other, pairwise]) | Exponential weighted sample correlation. |
| EWM.cov([other, pairwise, bias]) | Exponential weighted sample covariance. |