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https://issues.apache.org/jira/browse/SPARK-19044?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15790208#comment-15790208
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Josh Rosen commented on SPARK-19044:
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In fact, this is an instance of a more general problem when working with
ambiguous columns in Python DataFrames: in the above example {{v2[0]}} also
throws the same error because there are several places in the code that assume
that entries in {{self.columns}} are unique.
> PySpark dropna() can fail with AnalysisException
> ------------------------------------------------
>
> Key: SPARK-19044
> URL: https://issues.apache.org/jira/browse/SPARK-19044
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Reporter: Josh Rosen
> Priority: Minor
>
> In PySpark, the following fails with an AnalysisException:
> {code}
> v1 = spark.range(10)
> v2 = v1.crossJoin(v1)
> v2.dropna()
> {code}
> {code}
> AnalysisException: u"Reference 'id' is ambiguous, could be: id#66L, id#69L.;"
> {code}
> However, the equivalent Scala code works fine:
> {code}
> val v1 = spark.range(10)
> val v2 = v1.crossJoin(v1)
> v1.na.drop()
> {code}
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