Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.
安装
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-polarsnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-polars -a claude-code -gnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-polars -a codex -ggemini skills install https://github.com/AlterLab-IEU/AlterLab-Academic-Skills.git --path skills/data-science/alterlab-polarsgh skill install AlterLab-IEU/AlterLab-Academic-Skills alterlab-polarsgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-Skills /tmp/AlterLab-IEU__AlterLab-Academic-Skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-polars ~/.claude/skills/alterlab-polars # Claude Code
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-polars ~/.agents/skills/alterlab-polars # Codex, Cursor, Gemini CLI, Copilot, OpenCode