Details
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Bug
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Status: Resolved
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Major
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Resolution: Duplicate
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2.3.0
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None
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None
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PySpark
Spark 2.3.0
Description
The return struct of a pandas_udf should be mapped to the provided schema by name. Currently it's not the case.
Consider these two examples, where the only change is the order of the fields in the provided schema struct:
from pyspark.sql.functions import pandas_udf, PandasUDFType df = spark.createDataFrame( [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id", "v")) @pandas_udf("v double,id long", PandasUDFType.GROUPED_MAP) def normalize(pdf): v = pdf.v return pdf.assign(v=(v - v.mean()) / v.std()) df.groupby("id").apply(normalize).show()
and this one:
from pyspark.sql.functions import pandas_udf, PandasUDFType df = spark.createDataFrame( [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id", "v")) @pandas_udf("id long,v double", PandasUDFType.GROUPED_MAP) def normalize(pdf): v = pdf.v return pdf.assign(v=(v - v.mean()) / v.std()) df.groupby("id").apply(normalize).show()
The results should be the same but they are different:
For the first code:
+---+---+ | v| id| +---+---+ |1.0| 0| |1.0| 0| |2.0| 0| |2.0| 0| |2.0| 1| +---+---+
For the second code:
+---+-------------------+ | id| v| +---+-------------------+ | 1|-0.7071067811865475| | 1| 0.7071067811865475| | 2|-0.8320502943378437| | 2|-0.2773500981126146| | 2| 1.1094003924504583| +---+-------------------+
Attachments
Issue Links
- duplicates
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SPARK-24324 Pandas Grouped Map UserDefinedFunction mixes column labels
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- Resolved
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- is duplicated by
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- Resolved
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