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https://issues.apache.org/jira/browse/SPARK-55818?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-55818:
-----------------------------------
Labels: pull-request-available (was: )
> Decimal-float mixed arithmetic should always raise TypeError
> ------------------------------------------------------------
>
> Key: SPARK-55818
> URL: https://issues.apache.org/jira/browse/SPARK-55818
> Project: Spark
> Issue Type: Bug
> Components: Pandas API on Spark
> Affects Versions: 4.1.1
> Reporter: Devin Petersohn
> Priority: Major
> Labels: pull-request-available
>
> When performing arithmetic between a float Series and a decimal.Decimal
> scalar, behavior changes depending on spark.sql.ansi.enabled:
>
> * ANSI OFF: silently computes (potentially incorrect precision)
> * ANSI ON: raises TypeError: Multiplication can not be applied to given types
> * pandas: raises TypeError: unsupported operand type(s) for *
>
>
> The ANSI ON behavior matches pandas. In order to match pandas, the TypeError
> should always be raised regardless of ANSI mode.
>
> Reproduction:
>
>
> {code:java}
> import decimal
> import pyspark.pandas as ps
>
> spark.conf.set("spark.sql.ansi.enabled", "false")
> ps.Series([1.0, 2.0, 3.0]) * decimal.Decimal("1.5") # returns [1.5, 3.0, 4.5]
>
> spark.conf.set("spark.sql.ansi.enabled", "true")
> ps.Series([1.0, 2.0, 3.0]) * decimal.Decimal("1.5") # raises TypeError
>
> # pandas
> import pandas as pd
>
> pd.Series([1.0, 2.0, 3.0]) * decimal.Decimal("1.5") # raises TypeError
> {code}
>
> This is a child of SPARK-55791.
>
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