polars-runtime-compat

What it is and what it does

Polars-runtime-compat distributes pre-compiled binary wheels for Polars, a DataFrame query engine written in Rust. Polars is designed for analytical workloads on tabular data, offering both lazy (optimized) and eager execution modes. It uses vectorized SIMD execution and multi-threading to achieve high throughput, and includes a streaming engine for processing datasets larger than available RAM. The package supports composition of complex queries through expressions, interoperates with Apache Arrow for zero-copy data sharing, and provides bindings across Python, Rust, Node.js, R, and SQL.

The wheels in this package enable installation on modern Python versions (3.10+) across multiple operating systems and CPU architectures without requiring compilation. Polars is positioned as a high-performance alternative to pandas and other DataFrame libraries, with benchmarks available on the project website. It has no runtime dependencies, making installation straightforward once the appropriate binary wheel is selected for your platform.

Use it for:

• Perform fast aggregations and filtering on large Parquet or CSV files with lazy query optimization
• Process datasets larger than RAM using the streaming engine to reduce memory footprint
• Compose complex analytical queries with expressions and group-by operations across distributed cores
• Replace pandas workflows where performance is critical for data transformation pipelines
• Interoperate with Apache Arrow-based tools and libraries without data copying overhead

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Polars-runtime-compat provides binary wheels for Polars, a Rust-based analytical query engine for DataFrames, enabling fast vectorized execution with lazy and eager evaluation modes.

Yes, if you need fast analytical DataFrame operations on modern Python (3.10+). The package is actively maintained, has no runtime dependencies, carries a permissive MIT license, and is widely adopted (top 15000 PyPI packages). Install friction is medium due to platform-specific wheels, but pre-built binaries are available for common architectures. No known security vulnerabilities.

Install

polars-runtime-compat on PyPI

pip install polars-runtime-compat

uv add polars-runtime-compat

poetry add polars-runtime-compat

Installing polars-runtime-compat

Before you install

Medium install friction due to platform-specific binary wheels (cp310-abi3 across macOS, Linux, Windows, and ARM architectures). Package is actively maintained with recent releases; repo shows 39355 stars and last commit on 2026-08-14.

License in practice

MIT license (permissive) means you can use, modify, and distribute this package with minimal restrictions, provided you include the license notice.

Quickstart

pip install polars-runtime-compat

import polars as pl

df = pl.scan_parquet("data.parquet").filter(pl.col("status") == "active").collect()

Requires Python 3.10 or later; pre-built wheels available for macOS (x86_64, arm64), Linux (x86_64, aarch64), Windows (amd64, arm64), and musl-based systems.

Verify before relying

• Whether polars-runtime-compat is a separate package or a distribution artifact of the main Polars project
• Specific performance characteristics compared to eager-only DataFrame libraries
• GPU acceleration availability and setup requirements for NVIDIA systems

Package facts

Evidence: polars_runtime_compat-1.43.2-cp310-abi3-macosx_10_12_x86_64.whl; polars_runtime_compat-1.43.2-cp310-abi3-macosx_11_0_arm64.whl; polars_runtime_compat-1.43.2-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; polars_runtime_compat-1.43.2-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; polars_runtime_compat-1.43.2-cp310-abi3-musllinux_1_2_aarch64.whl; polars_runtime_compat-1.43.2-cp310-abi3-musllinux_1_2_x86_64.whl; polars_runtime_compat-1.43.2-cp310-abi3-win_amd64.whl; polars_runtime_compat-1.43.2-cp310-abi3-win_arm64.whl

Keywords: dataframe, arrow, out-of-core

Tags

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