What is the purpose of Pandas on Spark (formerly Koalas) in a Databricks ML workflow?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Pandas on Spark lets you write familiar df.groupby().mean() pandas code on a Spark-backed DataFrame — so your feature engineering scripts scale to terabytes without a full rewrite in PySpark.
Full explanation below image
Full Explanation
Pandas on Spark (import pyspark.pandas as ps, formerly Koalas) implements a large subset of the pandas API where the underlying computation runs on Spark (distributed). This enables: 1) Familiar pandas syntax — ps.read_delta(path) returns a pyspark.pandas.DataFrame with the same API as pd.DataFrame. 2) Distributed execution — operations that would OOM a single pandas node run across the cluster. 3) Interoperability — easy conversion: .to_pandas() for local analysis, .to_spark() for native Spark operations. Limitations: not 100% of pandas API is supported; some operations require collecting to driver (e.g., display, some joins); performance may differ from native PySpark for complex operations. Common use in ML: data exploration and feature engineering code that needs to handle large datasets without rewriting in PySpark syntax.