Polars
Polars is an open-source DataFrame library written in Rust, designed for data processing and analysis workloads. It supports interfaces for multiple programming languages including Python and Rust, and employs a columnar memory model based on Apache Arrow along with lazy evaluation to efficiently process large-scale structured data. Polars is distinguished by its multi-threaded parallel computation, delivering strong performance and memory efficiency, and is frequently used as an alternative to traditional data analysis libraries such as Pandas.
Timeline (last 90 days)
Arrow defines a language-agnostic in-memory layout specification (columnar, fixed-length offsets, buffer alignment) that lets Pandas, Polars, DuckDB, PyTorch and others pass data pointers to each other rather than the data itself.
Columnar engines (such as Arrow, DuckDB, Polars and GPU DataFrames) excel at scanning large numbers of rows and computing aggregations, making them an ideal choice for building training sets.
TPC-H-style benchmarks show that DuckDB, Polars or GPU DataFrame engines perform excellently on analytical workloads.
The open-source tool split-adjustment-tool is a vendor-neutral Python tool (based on Polars) for validating, fixing, and applying stock split adjustments, under the Apache 2.0 license, version 0.4.0.
When a project has hard performance requirements, Polars or DuckDB should be considered, while Pandas still holds advantages in teaching, prototype development, and lightweight analysis scenarios.
Polars is built on Rust and employs multi-threaded parallel execution and lazy evaluation.
In large-data scenarios, Polars often outperforms Pandas by several times.
It is recommended to use Pandas for quick exploration and small-scale data processing, Polars or DuckDB for performance-sensitive large-scale tasks, and to pass data between the two via the Arrow format.
Polars is a DataFrame library written in Rust that is 5–10 times faster than pandas.
All Facts (10)
Polars（高性能数据处理库）采用了PyO3 + Maturin技术栈
90%UnverifiedPolars is a DataFrame library written in Rust that is 5–10 times faster than pandas.
70%UnverifiedArrow defines a language-agnostic in-memory layout specification (columnar, fixed-length offsets, buffer alignment) that lets Pandas, Polars, DuckDB, PyTorch and others pass data pointers to each other rather than the data itself.
50%UnverifiedColumnar engines (such as Arrow, DuckDB, Polars and GPU DataFrames) excel at scanning large numbers of rows and computing aggregations, making them an ideal choice for building training sets.
50%UnverifiedTPC-H-style benchmarks show that DuckDB, Polars or GPU DataFrame engines perform excellently on analytical workloads.
50%UnverifiedWhen a project has hard performance requirements, Polars or DuckDB should be considered, while Pandas still holds advantages in teaching, prototype development, and lightweight analysis scenarios.
50%UnverifiedPolars is built on Rust and employs multi-threaded parallel execution and lazy evaluation.
50%UnverifiedIn large-data scenarios, Polars often outperforms Pandas by several times.
50%UnverifiedIt is recommended to use Pandas for quick exploration and small-scale data processing, Polars or DuckDB for performance-sensitive large-scale tasks, and to pass data between the two via the Arrow format.
50%UnverifiedThe open-source tool split-adjustment-tool is a vendor-neutral Python tool (based on Polars) for validating, fixing, and applying stock split adjustments, under the Apache 2.0 license, version 0.4.0.
50%