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DriverTrac/venv/lib/python3.12/site-packages/polars-1.35.2.dist-info/METADATA
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Metadata-Version: 2.4
Name: polars
Version: 1.35.2
Summary: Blazingly fast DataFrame library
Author-email: Ritchie Vink <ritchie46@gmail.com>
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
<h1 align="center">
<a href="https://pola.rs">
<img src="https://raw.githubusercontent.com/pola-rs/polars-static/master/banner/polars_github_banner.svg" alt="Polars logo">
</a>
</h1>
<div align="center">
<a href="https://crates.io/crates/polars">
<img src="https://img.shields.io/crates/v/polars.svg" alt="crates.io Latest Release"/>
</a>
<a href="https://pypi.org/project/polars/">
<img src="https://img.shields.io/pypi/v/polars.svg" alt="PyPi Latest Release"/>
</a>
<a href="https://www.npmjs.com/package/nodejs-polars">
<img src="https://img.shields.io/npm/v/nodejs-polars.svg" alt="NPM Latest Release"/>
</a>
<a href="https://community.r-multiverse.org/polars">
<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fcommunity.r-multiverse.org%2Fapi%2Fpackages%2Fpolars&query=%24.Version&label=r-multiverse" alt="R-multiverse Latest Release"/>
</a>
<a href="https://doi.org/10.5281/zenodo.7697217">
<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.7697217.svg" alt="DOI Latest Release"/>
</a>
</div>
<p align="center">
<b>Documentation</b>:
<a href="https://docs.pola.rs/api/python/stable/reference/index.html">Python</a>
-
<a href="https://docs.rs/polars/latest/polars/">Rust</a>
-
<a href="https://pola-rs.github.io/nodejs-polars/index.html">Node.js</a>
-
<a href="https://pola-rs.github.io/r-polars/index.html">R</a>
|
<b>StackOverflow</b>:
<a href="https://stackoverflow.com/questions/tagged/python-polars">Python</a>
-
<a href="https://stackoverflow.com/questions/tagged/rust-polars">Rust</a>
-
<a href="https://stackoverflow.com/questions/tagged/nodejs-polars">Node.js</a>
-
<a href="https://stackoverflow.com/questions/tagged/r-polars">R</a>
|
<a href="https://docs.pola.rs/">User guide</a>
|
<a href="https://discord.gg/4UfP5cfBE7">Discord</a>
</p>
## 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.
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