[GitHub] [spark] BryanCutler commented on a change in pull request #27466: [SPARK-30722][PYTHON][DOCS] Update documentation for Pandas UDF with Python type hints

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BryanCutler 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_r375575696
 
 

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 File path: docs/sql-pyspark-pandas-with-arrow.md
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 @@ -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.
 
 Review comment:
   instead of "previous way" maybe say "using a `PandasUDFType` will be deprecated.." so it's clear what is planned to be deprecated.

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