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Posted to issues@spark.apache.org by "Yikun Jiang (Jira)" <ji...@apache.org> on 2021/04/21 09:33:00 UTC
[jira] [Updated] (SPARK-35173) Support with_columns in
PySpark.dataframe
[ https://issues.apache.org/jira/browse/SPARK-35173?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Yikun Jiang updated SPARK-35173:
--------------------------------
Issue Type: Improvement (was: Bug)
> Support with_columns in PySpark.dataframe
> -----------------------------------------
>
> Key: SPARK-35173
> URL: https://issues.apache.org/jira/browse/SPARK-35173
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 3.2.0
> Reporter: Yikun Jiang
> Priority: Major
>
> Now, the pyspark can only use withColumn to do column adding a column or replacing the existing column that has the same name. There was already have a withColumns api in scala and java to help user adding columns. [1]
>
> Before this added, the user can only use withColumn again and again like:
>
> {code:java}
> self.df.withColumn("key1", col("key1")).withColumn("key2", col("key2")).withColumn("key3", col("key3")){code}
>
> After the support, you user can use the with_columns complete batch operations:
>
> {code:java}
> self.df.with_columns(["key1", "key2", "key3"], [col("key1"), col("key2"), col("key3")]){code}
>
> [1] https://github.com/apache/spark/blob/b5241c97b17a1139a4ff719bfce7f68aef094d95/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala#L2402
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