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. |
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Calculate the rolling sum. |
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Calculate the rolling mean. |
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Calculate the rolling median. |
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Calculate the rolling variance. |
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Calculate the rolling standard deviation. |
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Calculate the rolling minimum. |
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Calculate the rolling maximum. |
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Calculate the rolling unbiased skewness. |
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Calculate the rolling Fisher's definition of kurtosis without bias. |
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Aggregate using one or more operations over the specified axis. |
Weighted window functions#
Expanding window functions#
Calculate the expanding count of non NaN observations. |
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Calculate the expanding sum. |
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Calculate the expanding mean. |
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Calculate the expanding variance. |
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Calculate the expanding standard deviation. |
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Calculate the expanding minimum. |
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Calculate the expanding maximum. |
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Aggregate using one or more operations over the specified axis. |
Exponentially-weighted window functions#
Calculate the ewm (exponential weighted moment) mean. |
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Calculate the ewm (exponential weighted moment) standard deviation. |
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Calculate the ewm (exponential weighted moment) variance. |
Window indexer#
Base class for defining custom window boundaries.