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https://issues.apache.org/jira/browse/SPARK-55977?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-55977:
-----------------------------------
Labels: pull-request-available (was: )
> isin() should not match values of incompatible types
> ----------------------------------------------------
>
> Key: SPARK-55977
> URL: https://issues.apache.org/jira/browse/SPARK-55977
> Project: Spark
> Issue Type: Bug
> Components: Pandas API on Spark
> Affects Versions: 4.1.1
> Reporter: Devin Petersohn
> Priority: Major
> Labels: pull-request-available
>
> DataFrame.isin() and Series.isin() return True when comparing values of
> incompatible types. Spark's implicit type coercion causes string "1" to match
> integer 1, while pandas uses strict type matching and returns False.
>
> {{import pandas as pd}}
> {{import pyspark.pandas as ps}}
> {{# DataFrame.isin with list}}
> {{pdf = pd.DataFrame(\{"a": [1, 2, 3]})}}
> {{{}psdf = ps.from_pandas(pdf){}}}{{{}pdf.isin(["1", "2"])["a"].tolist() #
> [False, False, False] {}}}
> {{psdf.isin(["1", "2"])["a"].tolist() # [True, True, False]}}
> {{# DataFrame.isin with dict}}
> {{pdf.isin(\{"a": ["1", "2"]})["a"].tolist() # [False, False, False]}}
> {{psdf.isin(\{"a": ["1", "2"]})["a"].tolist() # [True, True, False]}}
> {{# Series.isin }}
> {{pd.Series([1, 2, 3]).isin(["1", "2"]).tolist() # [False, False, False] }}
> {{ps.Series([1, 2, 3]).isin(["1", "2"]).tolist() # [True, True, False]}}
> {{# Numeric cross-type works correctly in both (int col, float values)}}
> {{pd.Series([1, 2, 3]).isin([1.0, 2.0]).tolist() # [True, True, False] }}
> {{ps.Series([1, 2, 3]).isin([1.0, 2.0]).tolist() # [True, True, False]}}
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