产品
A DataFrame library written in Rust, built on the Apache Arrow columnar memory format.
已跟踪 2 条高相关材料
最近变化
2026-05-26 · PySpark is ideal for distributed ETL and cluster-scale pipelines.
为什么值得关注
Polars 被反复提及时，通常意味着它正在影响产品路线、开发者工作流或 AI 产业判断。这个页面把分散材料合并成一个可持续更新的观察入口。
Top 7 Python Libraries for Large-Scale Data Processing
KDnuggets · 9 分
This article lists and reviews seven top Python libraries for large-scale data processing, including PySpark, Dask, Polars, Ray, Vaex, Vaex...
Using Polars Instead of Pandas: Performance Deep Dive
KDnuggets · 8.5 分
Polars 在处理大规模数据集时比 Pandas 更快，特别是在并行计算和懒加载方面。
已收录 2 条与 Polars 相关的内容，按评分排序。
This article lists and reviews seven top Python libraries for large-scale data processing, including PySpark, Dask, Polars, Ray, Vaex, Vaex-Java, and Vaex-Python.
入选理由：PySpark is ideal for distributed ETL and cluster-scale pipelines.
Polars outperforms Pandas in handling large datasets, especially in parallel computing and lazy evaluation.
入选理由：Polars 使用 Rust 构建，支持并行计算和懒加载，性能优于 Pandas。
