HyukjinKwon commented on a change in pull request #24997: [SPARK-28198][PYTHON]
Add mapPartitionsInPandas to allow an iterator of DataFrames
URL: https://github.com/apache/spark/pull/24997#discussion_r299030810
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File path: python/pyspark/sql/dataframe.py
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@@ -2192,6 +2193,51 @@ def toPandas(self):
_check_series_convert_timestamps_local_tz(pdf[field.name], timezone)
return pdf
+ def mapPartitionsInPandas(self, udf):
+ """
+ Maps each partition of the current :class:`DataFrame` using a pandas
udf and returns
+ the result as a `DataFrame`.
+
+ The user-defined function should take an iterator of
`pandas.DataFrame`s and return another
+ iterator of `pandas.DataFrame`s. For each partition, all columns are
passed together as an
+ iterator of `pandas.DataFrame`s to the user-function and the returned
iterator of
+ `pandas.DataFrame`s are combined as a :class:`DataFrame`.
+ Each `pandas.DataFrame` size can be controlled by
+ `spark.sql.execution.arrow.maxRecordsPerBatch`.
+ Its schema must match the returnType of the pandas udf.
+
+ :param udf: A function object returned by
:meth:`pyspark.sql.functions.pandas_udf`
+
+ >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
+ >>> df = spark.createDataFrame([(1, 21), (2, 30)],
+ ... ("id", "age")) # doctest: +SKIP
+ >>> @pandas_udf(df.schema, PandasUDFType.SCALAR_ITER) # doctest: +SKIP
Review comment:
Yea .. I will do that in a separate PR. I need to make another PR to
document (and/or rename) anyway. But out of curiosity, do you guys have some
preferences? @mengxr, @icexelloss, @BryanCutler, @viirya? we can,
1. Rename `SCALAR_ITER` -> `ITER` (or something else?)
2. Introduce `DATAFRAME_ITER` here.
My impression is that UDF type defines execution type and existing
`SCALAR_ITER` and the current one share virtually same execution path (like we
share `GROUPED_AGG` for both Grouped Agg and Window function)
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