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https://issues.apache.org/jira/browse/SPARK-19732?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-19732:
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Assignee: Apache Spark
> DataFrame.fillna() does not work for bools in PySpark
> -----------------------------------------------------
>
> Key: SPARK-19732
> URL: https://issues.apache.org/jira/browse/SPARK-19732
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 2.1.0
> Reporter: Len Frodgers
> Assignee: Apache Spark
> Priority: Minor
>
> In PySpark, the fillna function of DataFrame inadvertently casts bools to
> ints, so fillna cannot be used to fill True/False.
> e.g.
> `spark.createDataFrame([Row(a=True),Row(a=None)]).fillna(True).collect()`
> yields
> `[Row(a=True), Row(a=None)]`
> It should be a=True for the second Row
> The cause is this bit of code:
> {code}
> if isinstance(value, (int, long)):
> value = float(value)
> {code}
> There needs to be a separate check for isinstance(bool), since in python,
> bools are ints too
> Additionally there's another anomaly:
> Spark (and pyspark) supports filling of bools if you specify the args as a
> map:
> {code}
> fillna({"a": False})
> {code}
> , but not if you specify it as
> {code}
> fillna(False)
> {code}
> This is because (scala-)Spark has no
> {code}
> def fill(value: Boolean): DataFrame = fill(value, df.columns)
> {code}
> method. I find that strange/buggy
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