Pandas vs Polars vs DuckDB: Which Library Should You Choose?

2026-05-23 · Source: Analytics Vidhya · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

This article compares three popular Python data processing libraries: pandas, Polars, and DuckDB, detailing their architectures, performance, and optimal use cases. Pandas remains the default for interactive notebooks, exploratory data analysis (EDA), visualization, and machine learning workflows, offering strong ecosystem compatibility. Polars excels in fast, memory-efficient DataFrame processing, particularly for ETL and feature engineering, leveraging a columnar engine and lazy execution. DuckDB offers a SQL-first approach, functioning as an embedded analytical database ideal for complex joins, aggregations, and direct querying of local files. The comparison highlights that while each tool has distinct strengths, a hybrid workflow combining them often yields the most efficient results.

Key takeaway

For Data Scientists or Machine Learning Engineers evaluating local data processing tools, recognize no single library is universally superior. If your workflow involves interactive exploration and ML model integration, prioritize pandas. For high-speed ETL and large DataFrame transformations, Polars is your best bet. When SQL-centric analytics or direct file querying is needed, opt for DuckDB. A hybrid approach, combining these tools for specific tasks, often optimizes performance and compatibility.

Key insights

Pandas, Polars, and DuckDB each optimize for distinct data processing paradigms, making hybrid workflows highly effective.

Principles

• Pandas prioritizes compatibility and ease of use.
• Polars focuses on speed and memory efficiency.
• DuckDB offers SQL-first local analytics.

Method

The article demonstrates a data pipeline involving reading, filtering, joining, aggregating, and saving data, implemented across Pandas, Polars, and DuckDB.

In practice

• Use Pandas for ML library integration.
• Employ Polars for high-performance ETL.
• Leverage DuckDB for SQL-based file queries.

Topics

• Pandas
• Polars
• DuckDB
• DataFrames
• SQL Analytics
• ETL Workflows
• Performance Benchmarking

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

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