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pandas_udf are optimized and faster for grouped operations, like applying a pandas_udf after a groupBy. The grouping allows pandas to perform vectorized operations and will be faster than normal udf. for normal case like a*b, a normal spark udf will suffice and be faster.
Vectorized Pandas UDFs offer improved performance compared to standard PySpark UDFs by leveraging the power of Pandas and operating on entire columns of data at once, rather than row by row.
They provide a more intuitive and familiar programming interface for data manipulation and transformation, as they allow you to use Pandas functions and syntax directly.
Vectorized Pandas UDFs enable seamless integration with existing Pandas code, making it easier to reuse and adapt code from other Python data analysis workflows.
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