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Answer: Lazy evaluation in Pandas API on Spark means that operations are queued and executed only when necessary, allowing for optimization of the execution plan.
Lazy evaluation is a concept in Pandas API on Spark that refers to the deferral of operation execution until the results are needed. This allows for optimization of the execution plan, as the operations are queued and executed only when necessary. By delaying the execution of operations, Pandas API on Spark can optimize the performance of data processing tasks, especially for complex operations or large datasets. This is in contrast to eager evaluation, where operations are executed immediately when they are called, without any opportunity for optimization.
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In the context of using Pandas API on Spark, explain the concept of 'lazy evaluation' and its significance in optimizing the performance of data processing tasks.
A
Lazy evaluation is not applicable to Pandas API on Spark, as it is a concept specific to native Spark operations.
B
Lazy evaluation in Pandas API on Spark means that operations are executed immediately when they are called, without any optimization.
C
Lazy evaluation in Pandas API on Spark means that operations are queued and executed only when necessary, allowing for optimization of the execution plan.
D
Lazy evaluation in Pandas API on Spark means that operations are always executed in a distributed manner, regardless of their complexity.
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