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[SPARK-52561][DOCS][FOLLOW-UP] Update migration guide about Python 3.9 dropped #51631

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[SPARK-52561][DOCS][FOLLOW-UP] Update migration guide about Python 3.9 dropped #51631

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4 changes: 1 addition & 3 deletions python/docs/source/migration_guide/pyspark_upgrade.rst
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Expand Up @@ -22,12 +22,10 @@ Upgrading PySpark
Upgrading from PySpark 4.0 to 4.1
---------------------------------

* In Spark 4.1, Python 3.9 support was dropped in PySpark.
* In Spark 4.1, ``DataFrame['name']`` on Spark Connect Python Client no longer eagerly validate the column name. To restore the legacy behavior, set ``PYSPARK_VALIDATE_COLUMN_NAME_LEGACY`` environment variable to ``1``.

* In Spark 4.1, Arrow-optimized Python UDF supports UDT input / output instead of falling back to the regular UDF. To restore the legacy behavior, set ``spark.sql.execution.pythonUDF.arrow.legacy.fallbackOnUDT`` to ``true``.

* In Spark 4.1, unnecessary conversion to pandas instances is removed when ``spark.sql.execution.pythonUDF.arrow.enabled`` is enabled. As a result, the type coercion changes when the produced output has a schema different from the specified schema. To restore the previous behavior, enable ``spark.sql.legacy.execution.pythonUDF.pandas.conversion.enabled``.

* In Spark 4.1, unnecessary conversion to pandas instances is removed when ``spark.sql.execution.pythonUDTF.arrow.enabled`` is enabled. As a result, the type coercion changes when the produced output has a schema different from the specified schema. To restore the previous behavior, enable ``spark.sql.legacy.execution.pythonUDTF.pandas.conversion.enabled``.

Upgrading from PySpark 3.5 to 4.0
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