Overview
Polars is a blazingly fast DataFrame library written in Rust with Python bindings. It's designed as a modern alternative to pandas, offering better performance and memory efficiency through lazy evaluation and multi-threaded execution.
Key Features
- ✓ Lazy Evaluation: Query optimization before execution
- ✓ Multi-threaded: Parallel execution by default
- ✓ Arrow Backend: Efficient memory format
- ✓ Streaming: Process larger-than-memory datasets
- ✓ Expressive API: Modern, chainable syntax
- ✓ Rust Core: Performance and safety
Pros
- 👍 10-100x faster than pandas
- 👍 Lower memory footprint
- 👍 Lazy API enables optimization
- 👍 Great for larger-than-memory data
- 👍 Active development
Cons
- 👎 Smaller ecosystem than pandas
- 👎 API differs from pandas
- 👎 Some pandas features missing
- 👎 Newer, less documentation
Best For
Data engineers and scientists working with medium-to-large datasets who need performance. Great pandas replacement for ETL scripts.
Founded: 2020 HQ: Netherlands