Polars
What it is: Lightning-fast DataFrame library written in Rust. Built on Apache Arrow. 10-100x faster than pandas with similar API. Supports lazy evaluation and parallel execution.
What It Does Best
Insane speed. Processes GBs of data on laptop. Parallel by default. SIMD optimizations. Often faster than Spark on single machine for datasets under 100GB.
Lazy evaluation. Build query plans, optimize automatically, execute efficiently. Write readable code, get optimized performance. Like SQL query optimizer for DataFrames.
Familiar yet better API. Similar to pandas but fixes many pain points. Clear error messages. String operations that don't drive you crazy. Better memory management.
Pricing
Free. Open source, MIT license.
When to Use It
✅ Pandas code is too slow
✅ Data 1GB-100GB (sweet spot)
✅ Starting new project (no legacy pandas code)
✅ Want to avoid Spark complexity
When NOT to Use It
❌ Heavy pandas ecosystem dependency (scikit-learn integration)
❌ Data over 100GB on single machine (use Spark/Dask)
❌ Team needs time to learn new API
Bottom line: The future of DataFrames in Python. Dramatically faster than pandas. Growing ecosystem. If you're starting fresh or pandas is too slow, switch to Polars. You won't go back.