You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/04/16 22:09:00 UTC
[jira] [Commented] (SPARK-22239) User-defined window functions with
pandas udf
[ https://issues.apache.org/jira/browse/SPARK-22239?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16440094#comment-16440094 ]
Apache Spark commented on SPARK-22239:
--------------------------------------
User 'icexelloss' has created a pull request for this issue:
https://github.com/apache/spark/pull/21082
> User-defined window functions with pandas udf
> ---------------------------------------------
>
> Key: SPARK-22239
> URL: https://issues.apache.org/jira/browse/SPARK-22239
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 2.2.0
> Environment:
> Reporter: Li Jin
> Priority: Major
>
> Window function is another place we can benefit from vectored udf and add another useful function to the pandas_udf suite.
> Example usage (preliminary):
> {code:java}
> w = Window.partitionBy('id').orderBy('time').rangeBetween(-200, 0)
> @pandas_udf(DoubleType())
> def ema(v1):
> return v1.ewm(alpha=0.5).mean().iloc[-1]
> df.withColumn('v1_ema', ema(df.v1).over(window))
> {code}
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org