Using Polars Instead of Pandas: Performance Deep Dive

2026-05-12 · Source: KDnuggets · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

This article, published on May 12, 2026, by Nate Rosidi, compares the performance of Polars against Pandas for common data manipulation tasks using three real-world data problems from StrataScratch. Polars, a DataFrame library built in Rust on Apache Arrow, is designed for parallelism and lazy evaluation, allowing it to optimize query plans and execute operations concurrently across CPU cores. In contrast, Pandas executes operations sequentially. The comparison highlights Polars' superior speed and memory efficiency, particularly with large datasets, demonstrating 5-10x improvements in wall-clock time for tasks like activity ranking, identifying returning users, and calculating rolling averages. Polars achieves this through optimized functions, predicate pushdown, and reduced intermediate data allocations.

Key takeaway

For Data Scientists and Machine Learning Engineers encountering performance bottlenecks with Pandas on datasets reaching millions of rows, consider migrating to Polars. Your existing Pandas habits will require syntax adjustments, but Polars' Rust-level parallelism, single-pass algorithms, and lazy execution can drastically reduce processing times and memory consumption, making it a valuable tool for scaling data operations.

Key insights

Polars significantly outperforms Pandas for large datasets due to its Rust-based parallelism and lazy evaluation.

Principles

• Lazy evaluation optimizes query plans before execution.
• Parallel processing across CPU cores enhances performance.
• Minimize intermediate data allocations for efficiency.

Method

Build a lazy query plan, push computations into the optimizer, and materialize results only when needed via `.collect()` to leverage Polars' performance benefits.

In practice

• Use `.with_row_count()` instead of `rank()` for unique ranking.
• Employ window expressions like `.over()` for grouped calculations.
• Filter data early to reduce dataset size before joins.

Topics

• Polars DataFrame
• Pandas DataFrame
• Performance Benchmarking
• Lazy Evaluation
• Apache Arrow

Best for: Data Scientist, Machine Learning Engineer, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.