icexelloss commented on a change in pull request #22305:
[SPARK-24561][SQL][Python] User-defined window aggregation functions with
Pandas UDF (bounded window)
URL: https://github.com/apache/spark/pull/22305#discussion_r241453632
##########
File path: python/pyspark/sql/functions.py
##########
@@ -2993,20 +2992,25 @@ def pandas_udf(f=None, returnType=None,
functionType=None):
>>> @pandas_udf("double", PandasUDFType.GROUPED_AGG) # doctest: +SKIP
... def mean_udf(v):
... return v.mean()
- >>> w = Window \\
- ... .partitionBy('id') \\
- ... .rowsBetween(Window.unboundedPreceding,
Window.unboundedFollowing)
+ >>> w = (Window.partitionBy('id')
+ ... .orderBy('v')
+ ... .rowsBetween(-1, 0))
>>> df.withColumn('mean_v', mean_udf(df['v']).over(w)).show() #
doctest: +SKIP
+---+----+------+
| id| v|mean_v|
+---+----+------+
- | 1| 1.0| 1.5|
+ | 1| 1.0| 1.0|
| 1| 2.0| 1.5|
- | 2| 3.0| 6.0|
- | 2| 5.0| 6.0|
- | 2|10.0| 6.0|
+ | 2| 3.0| 3.0|
+ | 2| 5.0| 4.0|
+ | 2|10.0| 7.5|
+---+----+------+
+ .. note:: For performance reasons, the input series to window functions
are not copied.
+ Therefore, changing the value of the input series is not allowed
and will
+ result incorrect results. For the same reason, users should also
not rely
Review comment:
Sounds good!
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