A pandas API on Spark DataFrame is an abstraction layer built directly on top of a native Spark DataFrame. It acts as a wrapper, utilizing the underlying distributed Spark DataFrame for data storage and computation. To provide the familiar, pandas-like API and features (such as a specific index), it maintains additional metadata alongside the Spark DataFrame. This design allows data scientists to leverage the distributed power of Spark using the well-known pandas syntax, effectively scaling their single-node workflows to big data environments without a steep learning curve.

A. pandas API on Spark DataFrames are distributed, not single-node. Their primary purpose is to enable distributed computation using a pandas-like interface.

B. They are a wrapper API and are not inherently more performant than the native, highly optimized Spark DataFrame API.

D. Both are built on Spark's immutable data structures. The pandas API on Spark does not change this fundamental characteristic.

E. They are fundamentally related; a pandas API on Spark DataFrame cannot exist without an underlying Spark DataFrame.

1. Apache Spark Documentation

Pandas API on Spark

Internals: "Internally

pandas API on Spark DataFrames are composed of a Spark DataFrame and an 'internal frame'. The internal frame holds the information about index and column labels to map the pandas-like API to the Spark DataFrame." This directly supports that it is made up of a Spark DataFrame and additional metadata.

Source: Apache Spark 3.5.1 Documentation

Pandas API on Spark

Internals section.

2. Databricks Documentation

Pandas API on Spark: "The pandas API on Spark provides pandas-equivalent APIs that work on Apache Spark... You can create a pandas API on Spark DataFrame by calling pyspark.pandas.frompandas or pyspark.pandas.readcsv. You can also convert to and from pandas API on Spark DataFrames and PySpark DataFrames..." This demonstrates the direct relationship and interoperability

refuting that they are unrelated (E) and confirming they are built upon Spark's foundation.

Source: Databricks Documentation > Develop on Databricks > Libraries and scripts > Pandas API on Spark.

3. Learning Spark

2nd Edition (O'Reilly)

Chapter 11: Pandas API on Spark: "The pandas API on Spark was created to provide a pandas-like API on top of Spark

so that data scientists can make an easy transition from a single-node machine to a distributed environment... Under the hood

every pandas API on Spark DataFrame is backed by a PySpark DataFrame."

Source: Chambers

B.

& Zaharia

M. (2020). Learning Spark

2nd Edition. O'Reilly Media

Inc. Chapter 11

"What Is the pandas API on Spark?" section.