The pandas API on Spark is specifically designed to provide a familiar, pandas-like interface that executes on a distributed Apache Spark cluster. This allows data scientists to scale their existing pandas workloads with minimal code changes. Often, the primary modification involves changing the import statement from import pandas as pd to import pyspark.pandas as ps. This approach directly addresses the requirement to spend the "least amount of time refactoring" because it leverages existing code and knowledge of the pandas API, unlike rewriting the logic using the PySpark, Scala, or SQL APIs, which have different syntax and execution paradigms.

A. This describes the goal of using Spark (parallel processing) but is not a specific, actionable refactoring approach or API.

B. Refactoring to the native PySpark DataFrame API would require a significant rewrite, as its syntax and methods differ from the pandas API.

C. This would require rewriting the entire notebook in a different programming language (Scala), representing the most time-consuming refactoring effort.

D. Translating programmatic pandas operations into declarative Spark SQL queries is a fundamentally different approach and requires a complete code rewrite.

1. Databricks Documentation

"What is pandas API on Spark?": "Pandas API on Spark makes data scientists productive with big data

by allowing them to use the pandas API they are familiar with to work with terabyte-scale datasets... with minimal code change."

Source: Databricks Documentation > Apache Spark > Pandas API on Spark > Overview.

2. Apache Spark Documentation

"Pandas API on Spark": "It provides pandas-equivalent APIs that work on Apache Spark. Pandas API on Spark is useful for users who are already familiar with pandas and want to leverage Spark for big data."

Source: Apache Spark™ 3.5.0 Documentation > PySpark > Pandas API on Spark.

3. Databricks Blog

"Koalas: Easy Transition from pandas to Apache Spark": "The Koalas project makes data scientists more productive when interacting with big data

by implementing the pandas DataFrame API on top of Apache Spark... Data scientists can now make a seamless transition from a single machine to a distributed environment."

Source: Databricks Blog

January 24

2020

"Koalas: Easy Transition from pandas to Apache Spark".