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https://issues.apache.org/jira/browse/SPARK-27612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-27612.
----------------------------------
       Resolution: Fixed
    Fix Version/s: 3.0.0

Issue resolved by pull request 24519
[https://github.com/apache/spark/pull/24519]

> Creating a DataFrame in PySpark with ArrayType produces some Rows with Arrays 
> of None
> -------------------------------------------------------------------------------------
>
>                 Key: SPARK-27612
>                 URL: https://issues.apache.org/jira/browse/SPARK-27612
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark, SQL
>    Affects Versions: 3.0.0
>            Reporter: Bryan Cutler
>            Assignee: Hyukjin Kwon
>            Priority: Blocker
>              Labels: correctness
>             Fix For: 3.0.0
>
>
> This seems to only affect Python 3.
> When creating a DataFrame with type {{ArrayType(IntegerType(), True)}} there 
> ends up being rows that are filled with None.
>  
> {code:java}
> In [1]: from pyspark.sql.types import ArrayType, IntegerType                  
>                                                
> In [2]: df = spark.createDataFrame([[1, 2, 3, 4]] * 100, 
> ArrayType(IntegerType(), True))                                     
> In [3]: df.distinct().collect()                                               
>                                                
> Out[3]: [Row(value=[None, None, None, None]), Row(value=[1, 2, 3, 4])]
> {code}
>  
> From this example, it is consistently at elements 97, 98:
> {code}
> In [5]: df.collect()[-5:]                                                     
>                                                
> Out[5]: 
> [Row(value=[1, 2, 3, 4]),
>  Row(value=[1, 2, 3, 4]),
>  Row(value=[None, None, None, None]),
>  Row(value=[None, None, None, None]),
>  Row(value=[1, 2, 3, 4])]
> {code}
> This also happens with a type of {{ArrayType(ArrayType(IntegerType(), True))}}



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