Pandas API on Spark is a distributed DataFrame implementation that provides a pandas-like interface on top of Apache Spark, enabling data scientists to scale pandas workflows beyond single-machine memory limits without rewriting code. Introduced in Spark 3.2, it bridges the gap between pandas' ease of use and Spark's distributed computing power, allowing you to process larger-than-memory datasets while leveraging familiar pandas syntax. The key mental model: it's pandas syntax with Spark execution — lazy evaluation, distributed processing, and eventual computation on clusters, but with the same .groupby(), .merge(), and .fillna() methods you already know.

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