Expressive & fast dataframes in Python with polars
summary
Polars is a Rust + Arrow dataframe that claims much faster, lower‑memory ops than pandas and demonstrates it on a 1.8 GB StackOverflow CSV (~1.26M question rows) using read_csv/scan_csv, a lazy query plan / collect flow and a semi-join to filter python tags without blowing RAM. The demo shows expression-based, parallel column ops, conversion to/from pandas, and a streaming engine (out‑of‑core) for datasets larger than memory; version cited is 0.15.1 and examples run successfully with those tools.
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chapters (6)
- 0:00Introduction to polars
- 1:53Why polars was created
- 6:32Alternatives to pandas
- 12:32Live demo with Stack Overflow data
- 22:32Polars design choices
- 24:36Q&A
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