[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1333":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":37,"lastSyncTime":38,"discoverSource":39},1333,"polars","pola-rs\u002Fpolars","pola-rs","Extremely fast Query Engine for DataFrames, written in Rust","https:\u002F\u002Fdocs.pola.rs",null,"Rust",38733,2875,214,2485,0,7,60,273,40,45,"MIT License",false,"main",true,[27,28,29,30,31,5,32,33],"arrow","dataframe","dataframe-library","dataframes","out-of-core","python","rust","2026-06-12 02:00:26","\u003Ch1 align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fpola.rs\">\n    \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fpola-rs\u002Fpolars-static\u002Fmaster\u002Fbanner\u002Fpolars_github_banner.svg\" alt=\"Polars logo\">\n  \u003C\u002Fa>\n\u003C\u002Fh1>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fcrates.io\u002Fcrates\u002Fpolars\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fcrates\u002Fv\u002Fpolars.svg\" alt=\"crates.io Latest Release\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fpolars\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fpolars.svg\" alt=\"PyPi Latest Release\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fwww.npmjs.com\u002Fpackage\u002Fnodejs-polars\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fnpm\u002Fv\u002Fnodejs-polars.svg\" alt=\"NPM Latest Release\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fcommunity.r-multiverse.org\u002Fpolars\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson?url=https%3A%2F%2Fcommunity.r-multiverse.org%2Fapi%2Fpackages%2Fpolars&query=%24.Version&label=r-multiverse\" alt=\"R-multiverse Latest Release\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.7697217\">\n    \u003Cimg src=\"https:\u002F\u002Fzenodo.org\u002Fbadge\u002FDOI\u002F10.5281\u002Fzenodo.7697217.svg\" alt=\"DOI Latest Release\"\u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cp align=\"center\">\n  \u003Cb>Documentation\u003C\u002Fb>:\n  \u003Ca href=\"https:\u002F\u002Fdocs.pola.rs\u002Fapi\u002Fpython\u002Fstable\u002Freference\u002Findex.html\">Python\u003C\u002Fa>\n  -\n  \u003Ca href=\"https:\u002F\u002Fdocs.rs\u002Fpolars\u002Flatest\u002Fpolars\u002F\">Rust\u003C\u002Fa>\n  -\n  \u003Ca href=\"https:\u002F\u002Fpola-rs.github.io\u002Fnodejs-polars\u002Findex.html\">Node.js\u003C\u002Fa>\n  -\n  \u003Ca href=\"https:\u002F\u002Fpola-rs.github.io\u002Fr-polars\u002Findex.html\">R\u003C\u002Fa>\n  |\n  \u003Cb>StackOverflow\u003C\u002Fb>:\n  \u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fpython-polars\">Python\u003C\u002Fa>\n  -\n  \u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Frust-polars\">Rust\u003C\u002Fa>\n  -\n  \u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fnodejs-polars\">Node.js\u003C\u002Fa>\n  -\n  \u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fr-polars\">R\u003C\u002Fa>\n  |\n  \u003Ca href=\"https:\u002F\u002Fdocs.pola.rs\u002F\">User guide\u003C\u002Fa>\n  |\n  \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002F4UfP5cfBE7\">Discord\u003C\u002Fa>\n\u003C\u002Fp>\n\n## Polars: Extremely fast Query Engine for DataFrames, written in Rust\n\nPolars is an analytical query engine written for DataFrames. It is designed to be fast, easy to use\nand expressive. Key features are:\n\n- Lazy | Eager execution\n- Streaming (larger-than-RAM datasets)\n- Query optimization\n- Multi-threaded\n- Written in Rust\n- SIMD\n- Powerful expression API\n- Front end in Python | Rust | NodeJS | R | SQL\n- [Apache Arrow Columnar Format](https:\u002F\u002Farrow.apache.org\u002Fdocs\u002Fformat\u002FColumnar.html)\n\nTo learn more, read the [user guide](https:\u002F\u002Fdocs.pola.rs\u002F).\n\n## Performance 🚀🚀\n\n### Blazingly fast\n\nPolars is very fast. In fact, it is one of the best performing solutions available. See the\n[PDS-H benchmarks](https:\u002F\u002Fwww.pola.rs\u002Fbenchmarks.html) results.\n\n### Lightweight\n\nPolars is also very lightweight. It comes with zero required dependencies, and this shows in the\nimport times:\n\n- polars: 70ms\n- numpy: 104ms\n- pandas: 520ms\n\n### Handles larger-than-RAM data\n\nIf you have data that does not fit into memory, Polars' query engine is able to process your query\n(or parts of your query) in a streaming fashion. This drastically reduces memory requirements, so\nyou might be able to process your 250GB dataset on your laptop. Collect with\n`collect(engine='streaming')` to run the query streaming.\n\n## Setup\n\n### Python\n\nInstall the latest Polars version with:\n\n```sh\npip install polars\n```\n\nSee the [User Guide](https:\u002F\u002Fdocs.pola.rs\u002Fuser-guide\u002Finstallation\u002F#feature-flags) for more details\non optional dependencies\n\nTo see the current Polars version and a full list of its optional dependencies, run:\n\n```python\npl.show_versions()\n```\n\n## Contributing\n\nWant to contribute? Read our [contributing guide](https:\u002F\u002Fdocs.pola.rs\u002Fdevelopment\u002Fcontributing\u002F).\n\n## Managed\u002FDistributed Polars\n\nDo you want a managed solution or scale out to distributed clusters? Consider our\n[offering](https:\u002F\u002Fcloud.pola.rs\u002F) and help the project!\n\n## Python: compile Polars from source\n\nIf you want a bleeding edge release or maximal performance you should compile Polars from source.\n\nThis can be done by going through the following steps in sequence:\n\n1. Install the latest [Rust compiler](https:\u002F\u002Fwww.rust-lang.org\u002Ftools\u002Finstall)\n2. Install [maturin](https:\u002F\u002Fmaturin.rs\u002F): `pip install maturin`\n3. `cd py-polars` and choose one of the following:\n   - `make build`, slow binary with debug assertions and symbols, fast compile times\n   - `make build-release`, fast binary without debug assertions, minimal debug symbols, long compile\n     times\n   - `make build-nodebug-release`, same as build-release but without any debug symbols, slightly\n     faster to compile\n   - `make build-debug-release`, same as build-release but with full debug symbols, slightly slower\n     to compile\n   - `make build-dist-release`, fastest binary, extreme compile times\n\nBy default the binary is compiled with optimizations turned on for a modern CPU. Specify `LTS_CPU=1`\nwith the command if your CPU is older and does not support e.g. AVX2.\n\nNote that the Rust crate implementing the Python bindings is called `py-polars` to distinguish from\nthe wrapped Rust crate `polars` itself. However, both the Python package and the Python module are\nnamed `polars`, so you can `pip install polars` and `import polars`.\n\n## Using custom Rust functions in Python\n\nExtending Polars with UDFs compiled in Rust is easy. We expose PyO3 extensions for `DataFrame` and\n`Series` data structures. See more in https:\u002F\u002Fgithub.com\u002Fpola-rs\u002Fpolars\u002Ftree\u002Fmain\u002Fpyo3-polars.\n\n## Going big...\n\nDo you expect more than 2^32 (~4.2 billion) rows? Compile Polars with the `bigidx` feature flag or,\nfor Python users, install `pip install polars[rt64]`.\n\nDon't use this unless you hit the row boundary as the default build of Polars is faster and consumes\nless memory.\n\n## Legacy\n\nDo you want Polars to run on an old CPU (e.g. dating from before 2011), or on an `x86-64` build of\nPython on Apple Silicon under Rosetta? Install `pip install polars[rtcompat]`. This version of\nPolars is compiled without [AVX](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAdvanced_Vector_Extensions) target\nfeatures.\n","Polars 是一个用 Rust 编写的极快速 DataFrame 查询引擎。其核心功能包括懒惰和急切执行模式、流处理能力以支持超出内存大小的数据集、查询优化以及多线程处理，同时利用 SIMD 技术提升性能。此外，Polars 支持多种前端语言如 Python、Rust、NodeJS 和 R，并且采用 Apache Arrow 列式存储格式。该项目非常适合需要高效处理大规模数据集的场景，尤其是在数据分析、机器学习预处理等对性能要求较高的应用中。",2,"2026-06-11 02:43:08","top_all"]