HyukjinKwon commented on a change in pull request #27466: [SPARK-30722][PYTHON][DOCS] Update documentation for Pandas UDF with Python type hints URL: https://github.com/apache/spark/pull/27466#discussion_r375586166
########## File path: docs/sql-pyspark-pandas-with-arrow.md ########## @@ -65,132 +65,188 @@ Spark will fall back to create the DataFrame without Arrow. ## Pandas UDFs (a.k.a. Vectorized UDFs) -Pandas UDFs are user defined functions that are executed by Spark using Arrow to transfer data and -Pandas to work with the data. A Pandas UDF is defined using the keyword `pandas_udf` as a decorator -or to wrap the function, no additional configuration is required. Currently, there are two types of -Pandas UDF: Scalar and Grouped Map. +Pandas UDFs are user defined functions that are executed by Spark using +Arrow to transfer data and Pandas to work with the data, which allows vectorized operations. A Pandas +UDF is defined using the `pandas_udf` as a decorator or to wrap the function, and no additional +configuration is required. A Pandas UDF behaves as a regular PySpark function API in general. -### Scalar +Before Spark 3.0, Pandas UDFs used to be defined with `PandasUDFType`. From Spark 3.0 +with Python 3.6+, you can also use Python type hints. Using Python type hints are preferred and the +previous way will be deprecated in the future release. -Scalar Pandas UDFs are used for vectorizing scalar operations. They can be used with functions such -as `select` and `withColumn`. The Python function should take `pandas.Series` as inputs and return -a `pandas.Series` of the same length. Internally, Spark will execute a Pandas UDF by splitting -columns into batches and calling the function for each batch as a subset of the data, then -concatenating the results together. +The below combinations of the type hints are supported by Python type hints for Pandas UDFs. +Note that `pandas.DataFrame` is mapped to the column of `StructType`; otherwise, `pandas.Series` is Review comment: Yeah, `StructType` -> `pandas.DataFrame` is a bit variant. In fact, PySpark column is equivalent to pandas' Series. So, I just have chosen the term `Series to Series`, rather then `Series or DataFrame to Series or DataFrame` which is a bit ugly. Here I really meant, all `pandas.Series` within this section can be `pandas.DataFrame`. Let me clarify here. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
