Pandas API on Spark provides a distributed implementation of the Pandas DataFrame API on top of Apache Spark.

Advantages:

Executes transformations in parallel across all nodes and cores in the cluster.

Maintains Pandas-like syntax, making it easy for Python users to transition.

Enables scaling of existing Pandas code to handle large datasets without memory limits.

Therefore, it combines Pandas usability with Spark’s distributed power, offering both speed and scalability.

Why the other options are incorrect:

B: While it uses Python, that’s not its main advantage.

C: It runs distributed across the cluster, not on a single node.

D: Pandas API on Spark uses lazy evaluation, not eager computation.