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https://issues.apache.org/jira/browse/SPARK-11569?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15001439#comment-15001439
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Jia Li commented on SPARK-11569:
--------------------------------
Hi [~josephkb] [~holdenk_amp],
I'd like to hear your opinion on the expected behavior for this test case. I
can think of these possibilities:
1) the tuple with null gets the last index as shown below
+-----+----+---+---+-----+
| x0| x1| x2| x3|x0idx|
+-----+----+---+---+-----+
|asd2s|1e1e|1.1| 0| 0.0|
|asd2s|1e1e|0.1| 0| 0.0|
| null|1e3e|1.2| 0| 2.0|
|bd34t|1e1e|5.1| 1| 1.0|
|asd2s|1e3e|0.2| 0| 0.0|
|bd34t|1e2e|4.3| 1| 1.0|
+-----+----+---+---+-----+
2) the tuple with null gets index 0 before everything else
3) eliminate the tuple from the result
Which one do you prefer?
Thanks,
> StringIndexer transform fails when column contains nulls
> --------------------------------------------------------
>
> Key: SPARK-11569
> URL: https://issues.apache.org/jira/browse/SPARK-11569
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 1.4.0, 1.5.0, 1.6.0
> Reporter: Maciej Szymkiewicz
>
> Transforming column containing {{null}} values using {{StringIndexer}}
> results in {{java.lang.NullPointerException}}
> {code}
> from pyspark.ml.feature import StringIndexer
> df = sqlContext.createDataFrame([("a", 1), (None, 2)], ("k", "v"))
> df.printSchema()
> ## root
> ## |-- k: string (nullable = true)
> ## |-- v: long (nullable = true)
> indexer = StringIndexer(inputCol="k", outputCol="kIdx")
> indexer.fit(df).transform(df)
> ## <repr(<pyspark.sql.dataframe.DataFrame at 0x7f4b0d8e7110>) failed:
> py4j.protocol.Py4JJavaError: An error occurred while calling o75.json.
> ## : java.lang.NullPointerException
> {code}
> Problem disappears when we drop
> {code}
> df1 = df.na.drop()
> indexer.fit(df1).transform(df1)
> {code}
> or replace {{nulls}}
> {code}
> from pyspark.sql.functions import col, when
> k = col("k")
> df2 = df.withColumn("k", when(k.isNull(), "__NA__").otherwise(k))
> indexer.fit(df2).transform(df2)
> {code}
> and cannot be reproduced using Scala API
> {code}
> import org.apache.spark.ml.feature.StringIndexer
> val df = sc.parallelize(Seq(("a", 1), (null, 2))).toDF("k", "v")
> df.printSchema
> // root
> // |-- k: string (nullable = true)
> // |-- v: integer (nullable = false)
> val indexer = new StringIndexer().setInputCol("k").setOutputCol("kIdx")
> indexer.fit(df).transform(df).count
> // 2
> {code}
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