Window#
Rolling objects are returned by .rolling
calls: xorbits.pandas.DataFrame.rolling()
,
xorbits.pandas.Series.rolling()
, etc. Expanding objects are returned by .expanding
calls:
xorbits.pandas.DataFrame.expanding()
, xorbits.pandas.Series.expanding()
, etc.
ExponentialMovingWindow objects are returned by .ewm
calls: xorbits.pandas.DataFrame.ewm()
, xorbits.pandas.Series.ewm()
, etc.
Rolling window functions#
|
Calculate the rolling count of non NaN observations. |
|
Calculate the rolling sum. |
|
Calculate the rolling mean. |
|
Calculate the rolling median. |
|
Calculate the rolling variance. |
|
Calculate the rolling standard deviation. |
|
Calculate the rolling minimum. |
|
Calculate the rolling maximum. |
|
Calculate the rolling correlation. |
|
Calculate the rolling sample covariance. |
|
Calculate the rolling unbiased skewness. |
|
Calculate the rolling Fisher's definition of kurtosis without bias. |
|
Aggregate using one or more operations over the specified axis. |
Weighted window functions#
Expanding window functions#
Calculate the expanding count of non NaN observations. |
|
Calculate the expanding sum. |
|
Calculate the expanding mean. |
|
Calculate the expanding variance. |
|
Calculate the expanding standard deviation. |
|
Calculate the expanding minimum. |
|
Calculate the expanding maximum. |
|
|
Aggregate using one or more operations over the specified axis. |
Exponentially-weighted window functions#
Calculate the ewm (exponential weighted moment) mean. |
|
Calculate the ewm (exponential weighted moment) standard deviation. |
|
Calculate the ewm (exponential weighted moment) variance. |
Window indexer#
Base class for defining custom window boundaries.