Omri created SPARK-23929:
----------------------------
Summary: pandas_udf schema mapped by position and not by name
Key: SPARK-23929
URL: https://issues.apache.org/jira/browse/SPARK-23929
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 2.3.0
Environment: PySpark
Spark 2.3.0
Reporter: Omri
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:
{code:java}
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()
{code}
and this one:
{code:java}
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()
{code}
The results should be the same but they are different:
For the first code:
{code:java}
+---+---+
| v| id|
+---+---+
|1.0| 0|
|1.0| 0|
|2.0| 0|
|2.0| 0|
|2.0| 1|
+---+---+
{code}
For the second code:
{code:java}
+---+-------------------+
| id| v|
+---+-------------------+
| 1|-0.7071067811865475|
| 1| 0.7071067811865475|
| 2|-0.8320502943378437|
| 2|-0.2773500981126146|
| 2| 1.1094003924504583|
+---+-------------------+
{code}
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