Data partitioning is important when working with Pandas on Spark DataFrames, as it can impact the performance of distributed operations. While the Pandas API on Spark provides a familiar Pandas-like interface, it is built on top of Spark DataFrames and leverages their distributed computing capabilities. Understanding data partitioning can help optimize the performance of operations on Pandas on Spark DataFrames, as it determines how data is distributed across the cluster. However, the Pandas API on Spark handles the partitioning automatically, abstracting away some of the complexities involved in managing data distribution.