Polars
技能 已验证 活跃Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
To offer a high-performance, in-memory DataFrame library for datasets that fit in RAM, serving as a faster alternative to pandas for ETL pipelines and data processing tasks.
功能
- In-memory DataFrame operations
- Lazy evaluation for query optimization
- Parallel execution for speed
- Apache Arrow backend for efficiency
- Faster alternative to pandas
使用场景
- Replacing slow pandas operations on in-memory datasets
- Optimizing ETL pipelines for 1-100GB datasets
- Performing complex data transformations and analyses
- Migrating from pandas to a more performant library
非目标
- Handling datasets larger than available RAM
- Replacing dask or vaex for out-of-memory computation
- General-purpose data storage or database management
Compliance
- info:GDPRThe skill operates on user-provided data; while Polars itself doesn't store data, personal data submitted to the LLM for processing could be subject to GDPR if not handled carefully by the user.
Execution
- info:Pinned dependenciesDependencies are managed by uv, which typically pins versions, but explicit lockfiles for the skill itself are not detailed. The Polars library itself would have its own versioning.
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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