Integrating Rust with Python...
summary
Benchmarks show Polars (Rust) beats pandas once tables grow—10,000 rows × 50 cols pandas holds up but at 10,000×100 and 50,000×50 Polars is noticeably faster; pandas still wins simple filters. Polars leverages Apache Arrow, SIMD, multithreading, lazy query optimization. Practical build path uses Python + Rust with maturin and pyo3: write a Rust function (example: add), cargo build via maturin develop, import the module in Python — the module builds and runs as expected.
AI-written summary · about
chapters (6)
- 0:00Introduction
- 2:34Why integrate Rust with Python
- 10:57Pandas vs Polars performance
- 14:12Rust advantages for Python
- 17:24Bridging Rust with Python
- 21:58Q&A
related talks
38mExpressive and fast dataframes in Python with polars
44mPolars; DataFrames in the multi-core era
28mExpressive & fast dataframes in Python with polars
58mFrom Pandas to Polars Upgrading Your Data Workflow
01h 16mPower up your Polars code with Polars extention
34mRust for the Python dev
01h 32mAn Introduction to Polars
36mPolishing Python: Preventing Performance Corrosion with Rust
