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Posted to issues@spark.apache.org by "sandeshyapuram (Jira)" <ji...@apache.org> on 2019/11/14 09:37:00 UTC
[jira] [Comment Edited] (SPARK-29890) Unable to fill na with 0 with
duplicate columns
[ https://issues.apache.org/jira/browse/SPARK-29890?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16974087#comment-16974087 ]
sandeshyapuram edited comment on SPARK-29890 at 11/14/19 9:36 AM:
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I've raised it as a bug because I feel fill.na(0) needs to fill 0 regardless of duplicate column names.
[~cloud_fan] Thoughts
was (Author: sandeshyapuram):
I've raised it as a bug because I feel fill.na(0) needs to fill 0 regardless of duplicate column names.
> Unable to fill na with 0 with duplicate columns
> -----------------------------------------------
>
> Key: SPARK-29890
> URL: https://issues.apache.org/jira/browse/SPARK-29890
> Project: Spark
> Issue Type: Bug
> Components: Spark Shell
> Affects Versions: 2.3.3
> Reporter: sandeshyapuram
> Priority: Major
>
> Trying to fill out na values with 0.
> {noformat}
> scala> :paste
> // Entering paste mode (ctrl-D to finish)
> val parent = spark.sparkContext.parallelize(Seq((1,2),(3,4),(5,6))).toDF("nums", "abc")
> val c1 = parent.filter(lit(true))
> val c2 = parent.filter(lit(true))
> c1.join(c2, Seq("nums"), "left")
> .na.fill(0).show{noformat}
> {noformat}
> 9/11/14 04:24:24 ERROR org.apache.hadoop.security.JniBasedUnixGroupsMapping: error looking up the name of group 820818257: No such file or directory
> org.apache.spark.sql.AnalysisException: Reference 'abc' is ambiguous, could be: abc, abc.;
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:213)
> at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveQuoted(LogicalPlan.scala:117)
> at org.apache.spark.sql.Dataset.resolve(Dataset.scala:220)
> at org.apache.spark.sql.Dataset.col(Dataset.scala:1246)
> at org.apache.spark.sql.DataFrameNaFunctions.org$apache$spark$sql$DataFrameNaFunctions$$fillCol(DataFrameNaFunctions.scala:443)
> at org.apache.spark.sql.DataFrameNaFunctions$$anonfun$7.apply(DataFrameNaFunctions.scala:500)
> at org.apache.spark.sql.DataFrameNaFunctions$$anonfun$7.apply(DataFrameNaFunctions.scala:492)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
> at org.apache.spark.sql.DataFrameNaFunctions.fillValue(DataFrameNaFunctions.scala:492)
> at org.apache.spark.sql.DataFrameNaFunctions.fill(DataFrameNaFunctions.scala:171)
> at org.apache.spark.sql.DataFrameNaFunctions.fill(DataFrameNaFunctions.scala:155)
> at org.apache.spark.sql.DataFrameNaFunctions.fill(DataFrameNaFunctions.scala:134)
> ... 54 elided{noformat}
>
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