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Answer: The Pandas UDF leverages Apache Arrow to convert data between Spark and Pandas formats
Apache Arrow is used by Pandas UDF to efficiently transfer data between Spark and Pandas formats. This enables the Pandas UDF to perform operations using the Pandas API on data in Spark DataFrames. It’s a critical component in providing high-performance and user-friendly interoperability between pandas and Spark.
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Why is Pandas API syntax compatible within a Pandas UDF function when applied to a Spark DataFrame?
A
The Pandas UDF automatically translates the function into Spark DataFrame syntax
B
The pandas API syntax cannot be implemented within a Pandas UDF function on a Spark DataFrame
C
The Pandas UDF leverages Apache Arrow to convert data between Spark and Pandas formats
D
The Pandas UDF invokes Pandas Function APIs internally
E
The Pandas UDF utilizes pandas API on Spark within its function