Yikun commented on code in PR #37836:
URL: https://github.com/apache/spark/pull/37836#discussion_r970431630
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python/pyspark/pandas/window.py:
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@@ -561,6 +573,101 @@ def mean(self) -> FrameLike:
"""
return super().mean()
+ def quantile(self, quantile: float, accuracy: int = 10000) -> FrameLike:
+ """
+ Calculate the rolling quantile of the values.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ quantile : float
+ Value between 0 and 1 providing the quantile to compute.
+ accuracy : int, optional
+ Default accuracy of approximation. Larger value means better
accuracy.
+ The relative error can be deduced by 1.0 / accuracy.
+ This is a panda-on-Spark specific parameter.
+
+ Returns
+ -------
+ Series or DataFrame
+ Returned object type is determined by the caller of the rolling
+ calculation.
+
+ Notes
+ -----
+ `quantile` in pandas-on-Spark are using distributed percentile
approximation
+ algorithm unlike pandas, the result might different with pandas, also
`interpolation`
+ parameters are not supported yet.
+
+ the current implementation of this API uses Spark's Window without
+ specifying partition specification. This leads to move all data into
+ single partition in single machine and could cause serious
+ performance degradation. Avoid this method against very large dataset.
+
+ See Also
+ --------
+ Series.rolling : Calling object with Series data.
+ DataFrame.rolling : Calling object with DataFrames.
+ Series.quantile : Equivalent method for Series.
+ DataFrame.quantile : Equivalent method for DataFrame.
Review Comment:
will address, thanks!
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