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https://issues.apache.org/jira/browse/SPARK-46189?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun closed SPARK-46189.
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> Various Pandas functions fail in interpreted mode
> -------------------------------------------------
>
> Key: SPARK-46189
> URL: https://issues.apache.org/jira/browse/SPARK-46189
> Project: Spark
> Issue Type: Bug
> Components: Pandas API on Spark, SQL
> Affects Versions: 3.4.1, 3.5.0
> Reporter: Bruce Robbins
> Assignee: Bruce Robbins
> Priority: Major
> Labels: pull-request-available
> Fix For: 3.5.1, 3.4.3, 4.0.0
>
>
> Various Pandas functions ({{kurt}}, {{var}}, {{skew}}, {{cov}}, and
> {{stddev}}) fail with an unboxing-related exception when run in interpreted
> mode.
> Here are some reproduction cases for pyspark interactive mode:
> {noformat}
> spark.sql("set spark.sql.codegen.wholeStage=false")
> spark.sql("set spark.sql.codegen.factoryMode=NO_CODEGEN")
> import numpy as np
> import pandas as pd
> import pyspark.pandas as ps
> pser = pd.Series([1, 2, 3, 7, 9, 8], index=np.random.rand(6), name="a")
> psser = ps.from_pandas(pser)
> # each of the following actions gets an unboxing error
> psser.kurt()
> psser.var()
> psser.skew()
> # set up for covariance test
> pdf = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)], columns=["a", "b"])
> psdf = ps.from_pandas(pdf)
> # this gets an unboxing error
> psdf.cov()
> # set up for stddev resr
> from pyspark.pandas.spark import functions as SF
> from pyspark.sql.functions import col
> from pyspark.sql import Row
> df = spark.createDataFrame([Row(a=1), Row(a=2), Row(a=3), Row(a=7), Row(a=9),
> Row(a=8)])
> # this gets an unboxing error
> df.select(SF.stddev(col("a"), 1)).collect()
> {noformat}
> Exception from the first case ({{psser.kurt()}}) is
> {noformat}
> java.lang.ClassCastException: class java.lang.Integer cannot be cast to class
> java.lang.Double (java.lang.Integer and java.lang.Double are in module
> java.base of loader 'bootstrap')
> at scala.runtime.BoxesRunTime.unboxToDouble(BoxesRunTime.java:112)
> at
> org.apache.spark.sql.catalyst.types.PhysicalDoubleType$$anonfun$2.compare(PhysicalDataType.scala:184)
> at scala.math.Ordering.lt(Ordering.scala:98)
> at scala.math.Ordering.lt$(Ordering.scala:98)
> at
> org.apache.spark.sql.catalyst.types.PhysicalDoubleType$$anonfun$2.lt(PhysicalDataType.scala:184)
> at
> org.apache.spark.sql.catalyst.expressions.LessThan.nullSafeEval(predicates.scala:1196)
> {noformat}
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