Metadata-Version: 2.4 Name: polars Version: 1.35.2 Summary: Blazingly fast DataFrame library Author-email: Ritchie Vink License: Copyright (c) 2025 Ritchie Vink Some portions Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Project-URL: Homepage, https://www.pola.rs/ Project-URL: Documentation, https://docs.pola.rs/api/python/stable/reference/index.html Project-URL: Repository, https://github.com/pola-rs/polars Project-URL: Changelog, https://github.com/pola-rs/polars/releases Keywords: dataframe,arrow,out-of-core Classifier: Development Status :: 5 - Production/Stable Classifier: Environment :: Console Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3 :: Only Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Programming Language :: Python :: 3.13 Classifier: Programming Language :: Rust Classifier: Topic :: Scientific/Engineering Classifier: Typing :: Typed Requires-Python: >=3.9 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: polars-runtime-32==1.35.2 Provides-Extra: rt64 Requires-Dist: polars-runtime-64==1.35.2; extra == "rt64" Provides-Extra: rtcompat Requires-Dist: polars-runtime-compat==1.35.2; extra == "rtcompat" Provides-Extra: polars-cloud Requires-Dist: polars_cloud>=0.0.1a1; extra == "polars-cloud" Provides-Extra: numpy Requires-Dist: numpy>=1.16.0; extra == "numpy" Provides-Extra: pandas Requires-Dist: pandas; extra == "pandas" Requires-Dist: polars[pyarrow]; extra == "pandas" Provides-Extra: pyarrow Requires-Dist: pyarrow>=7.0.0; extra == "pyarrow" Provides-Extra: pydantic Requires-Dist: pydantic; extra == "pydantic" Provides-Extra: calamine Requires-Dist: fastexcel>=0.9; extra == "calamine" Provides-Extra: openpyxl Requires-Dist: openpyxl>=3.0.0; extra == "openpyxl" Provides-Extra: xlsx2csv Requires-Dist: xlsx2csv>=0.8.0; extra == "xlsx2csv" Provides-Extra: xlsxwriter Requires-Dist: xlsxwriter; extra == "xlsxwriter" Provides-Extra: excel Requires-Dist: polars[calamine,openpyxl,xlsx2csv,xlsxwriter]; extra == "excel" Provides-Extra: adbc Requires-Dist: adbc-driver-manager[dbapi]; extra == "adbc" Requires-Dist: adbc-driver-sqlite[dbapi]; extra == "adbc" Provides-Extra: connectorx Requires-Dist: connectorx>=0.3.2; extra == "connectorx" Provides-Extra: sqlalchemy Requires-Dist: sqlalchemy; extra == "sqlalchemy" Requires-Dist: polars[pandas]; extra == "sqlalchemy" Provides-Extra: database Requires-Dist: polars[adbc,connectorx,sqlalchemy]; extra == "database" Provides-Extra: fsspec Requires-Dist: fsspec; extra == "fsspec" Provides-Extra: deltalake Requires-Dist: deltalake>=1.0.0; extra == "deltalake" Provides-Extra: iceberg Requires-Dist: pyiceberg>=0.7.1; extra == "iceberg" Provides-Extra: async Requires-Dist: gevent; extra == "async" Provides-Extra: cloudpickle Requires-Dist: cloudpickle; extra == "cloudpickle" Provides-Extra: graph Requires-Dist: matplotlib; extra == "graph" Provides-Extra: plot Requires-Dist: altair>=5.4.0; extra == "plot" Provides-Extra: style Requires-Dist: great-tables>=0.8.0; extra == "style" Provides-Extra: timezone Requires-Dist: tzdata; platform_system == "Windows" and extra == "timezone" Provides-Extra: gpu Requires-Dist: cudf-polars-cu12; extra == "gpu" Provides-Extra: all Requires-Dist: polars[async,cloudpickle,database,deltalake,excel,fsspec,graph,iceberg,numpy,pandas,plot,pyarrow,pydantic,style,timezone]; extra == "all" Dynamic: license-file

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Documentation: Python - Rust - Node.js - R | StackOverflow: Python - Rust - Node.js - R | User guide | Discord

## Polars: Extremely fast Query Engine for DataFrames, written in Rust Polars is an analytical query engine written for DataFrames. It is designed to be fast, easy to use and expressive. Key features are: - Lazy | Eager execution - Streaming (larger-than-RAM datasets) - Query optimization - Multi-threaded - Written in Rust - SIMD - Powerful expression API - Front end in Python | Rust | NodeJS | R | SQL - [Apache Arrow Columnar Format](https://arrow.apache.org/docs/format/Columnar.html) To learn more, read the [user guide](https://docs.pola.rs/). ## Performance 🚀🚀 ### Blazingly fast Polars is very fast. In fact, it is one of the best performing solutions available. See the [PDS-H benchmarks](https://www.pola.rs/benchmarks.html) results. ### Lightweight Polars is also very lightweight. It comes with zero required dependencies, and this shows in the import times: - polars: 70ms - numpy: 104ms - pandas: 520ms ### Handles larger-than-RAM data If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a streaming fashion. This drastically reduces memory requirements, so you might be able to process your 250GB dataset on your laptop. Collect with `collect(engine='streaming')` to run the query streaming. ## Setup ### Python Install the latest Polars version with: ```sh pip install polars ``` See the [User Guide](https://docs.pola.rs/user-guide/installation/#feature-flags) for more details on optional dependencies To see the current Polars version and a full list of its optional dependencies, run: ```python pl.show_versions() ``` ## Contributing Want to contribute? Read our [contributing guide](https://docs.pola.rs/development/contributing/). ## Managed/Distributed Polars Do you want a managed solution or scale out to distributed clusters? Consider our [offering](https://cloud.pola.rs/) and help the project! ## Python: compile Polars from source If you want a bleeding edge release or maximal performance you should compile Polars from source. This can be done by going through the following steps in sequence: 1. Install the latest [Rust compiler](https://www.rust-lang.org/tools/install) 2. Install [maturin](https://maturin.rs/): `pip install maturin` 3. `cd py-polars` and choose one of the following: - `make build`, slow binary with debug assertions and symbols, fast compile times - `make build-release`, fast binary without debug assertions, minimal debug symbols, long compile times - `make build-nodebug-release`, same as build-release but without any debug symbols, slightly faster to compile - `make build-debug-release`, same as build-release but with full debug symbols, slightly slower to compile - `make build-dist-release`, fastest binary, extreme compile times By default the binary is compiled with optimizations turned on for a modern CPU. Specify `LTS_CPU=1` with the command if your CPU is older and does not support e.g. AVX2. Note that the Rust crate implementing the Python bindings is called `py-polars` to distinguish from the wrapped Rust crate `polars` itself. However, both the Python package and the Python module are named `polars`, so you can `pip install polars` and `import polars`. ## Using custom Rust functions in Python Extending Polars with UDFs compiled in Rust is easy. We expose PyO3 extensions for `DataFrame` and `Series` data structures. See more in https://github.com/pola-rs/polars/tree/main/pyo3-polars. ## Going big... Do you expect more than 2^32 (~4.2 billion) rows? Compile Polars with the `bigidx` feature flag or, for Python users, install `pip install polars[rt64]`. Don't use this unless you hit the row boundary as the default build of Polars is faster and consumes less memory. ## Legacy Do you want Polars to run on an old CPU (e.g. dating from before 2011), or on an `x86-64` build of Python on Apple Silicon under Rosetta? Install `pip install polars[rtcompat]`. This version of Polars is compiled without [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) target features.