Benchmarks urge replacing Pandas with Polars, DuckDB

// 1h agoBENCHMARK RESULT

Benchmarks urge replacing Pandas with Polars, DuckDB

Software engineer Eddie Atkinson argues that Pandas should be phased out of modern data stacks because its memory bloat and single-threaded evaluation push teams toward complex distributed frameworks prematurely. Benchmarks across the 1 Billion Row Challenge and NYC Taxi datasets reveal that modern columnar engines like Polars and DuckDB run orders of magnitude faster while using up to 19x less memory through native multithreading and query optimization.

// ANALYSIS

Most teams do not have a "Big Data" problem—they have a Pandas problem that tricks them into over-engineering expensive distributed architectures. Fleet telemetry reveals that the vast majority of analytical workloads remain well under 100GB, meaning modern single-node engines can replace expensive distributed infrastructure for most organizations. DuckDB and Polars leverage query optimization, vectorization, and automatic multithreading to outperform Pandas by over 50x in execution time while slashing RAM consumption and avoiding OS swap thrashing. Because DuckDB, Polars, and modern Pandas all support Apache Arrow, teams can interchange data in-memory with zero copy overhead, drastically reducing switching friction despite Pandas' persistent ecosystem inertia.

// TAGS

pandaspolarsduckdbpythondata-engineeringbenchmarksapache-arrow

DISCOVERED

1h ago

2026-09-12

PUBLISHED

2h ago

2026-09-12

RELEVANCE

8/ 10

AUTHOR

__eddie__

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Benchmarks urge replacing Pandas with Polars, DuckDB