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https://issues.apache.org/jira/browse/SPARK-40559?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-40559:
------------------------------------
Assignee: Apache Spark
> Add applyInArrow to pyspark.sql.GroupedData
> -------------------------------------------
>
> Key: SPARK-40559
> URL: https://issues.apache.org/jira/browse/SPARK-40559
> Project: Spark
> Issue Type: New Feature
> Components: PySpark
> Affects Versions: 3.4.0
> Reporter: Enrico Minack
> Assignee: Apache Spark
> Priority: Major
>
> PySpark allows to transform a {{DataFrame}} via Pandas and Arrow API:
> {code:python}
> def map_arrow(iter: Iterator[pyarrow.RecordBatch]) ->
> Iterator[pyarrow.RecordBatch]:
> return iter
> def map_pandas(iter: Iterator[pandas.DataFrame]) ->
> Iterator[pandas.DataFrame]:
> return iter
> df.mapInArrow(map_arrow, schema="...")
> df.mapInPandas(map_pandas, schema="...")
> {code}
> A grouped {{DataFrame}} currently supports only the Pandas API:
> {code:python}
> def apply_pandas(df: pandas.DataFrame) -> pandas.DataFrame:
> return df
> df.groupBy("id").applyInPandas(apply_pandas, schema="...")
> {code}
> A similar method for the Arrow API would useful, especially given that Arrow
> is used by many other libraries.
> An Arrow-based method allows to process the {{DataFrame}} with *any*
> Arrow-based API, e.g. Polars:
> {code:python}
> def apply_polars(df: polars.DataFrame) -> polars.DataFrame:
> return df
> def apply_arrow(iter: Iterator[pyarrow.RecordBatch]) ->
> Iterator[pyarrow.RecordBatch]:
> for batch in iter:
> df = polars.from_arrow(pyarrow.Table.from_batches([batch]))
> for b in apply_polars(df).to_arrow().to_batches():
> yield b
> df.groupBy("id").applyInArrow(apply_arrow, schema="...")
> {code}
> https://stackoverflow.com/questions/71606278/is-there-an-apache-arrow-equivalent-of-the-spark-pandas-udf
> https://stackoverflow.com/questions/73203318/how-to-transform-spark-dataframe-to-polars-dataframe
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