Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory

2025-12-22 · Source: KDnuggets · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

This article provides a comprehensive comparison between the Python data manipulation libraries pandas (version 2.2.2) and Polars (version 1.31.0), highlighting differences in syntax, speed, and memory efficiency. Using a generated 1-million-row CSV dataset, Polars demonstrated significantly faster CSV reading times, being 8.2x quicker than pandas. For filtering and grouping operations, Polars achieved up to 97.1% memory savings compared to pandas, attributed to its columnar storage and optimized execution engine. The comparison also details syntax variations for common operations like column selection, row filtering, and adding new columns, noting Polars' use of `.select()`, `.filter()`, and `.with_columns()` methods with `pl.col()` expressions, which promote immutability. Furthermore, the article introduces Polars' lazy evaluation feature, where queries are optimized before execution, leading to performance gains.

Key takeaway

For Data Engineers and Data Scientists working with large datasets or performance-critical data pipelines, adopting Polars can significantly reduce processing times and memory footprint. While pandas remains valuable for smaller tasks and its extensive ecosystem, consider migrating computationally intensive operations to Polars to leverage its speed, memory efficiency, and lazy evaluation capabilities. Start by integrating Polars into a specific slow-running data pipeline to evaluate its impact.

Key insights

Polars offers substantial performance and memory advantages over pandas, especially for large-scale data operations.

Principles

• Columnar storage enhances memory efficiency.
• Lazy evaluation optimizes query execution.
• Explicit API design improves readability.

Method

To compare data manipulation libraries, generate a large synthetic dataset, perform common operations (read, filter, group), and measure execution time and memory usage using `time` and `psutil`.

In practice

• Use `pl.read_csv()` for faster CSV ingestion.
• Employ `pl.scan_csv()` for lazy query optimization.
• Utilize `pl.col()` for expressive column operations.

Topics

• Polars
• Pandas
• Data Manipulation
• Performance Benchmarking
• Lazy Evaluation

Code references

• balapriyac/data-science-tutorials

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

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