polars-runtime-32
Blazingly fast DataFrame library
Decision gist · record as of 2026-08-14
Yes. Polars-runtime-32 is actively maintained, has no known vulnerabilities, and carries a permissive MIT license. Install friction is moderate but manageable for modern Python environments (3.10+). Choose it if you need fast, memory-efficient DataFrame operations or plan to work with larger-than-RAM datasets.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Pre-compiled wheels available for common platforms; older CPUs (pre-2011) or non-standard architectures may require source compilation with Rust toolchain.
- Medium install friction due to compiled wheels for multiple platforms (x86_64, ARM, macOS, Linux, Windows).
License · maintenance · safety
MIT (permissive) — MIT license (permissive) allows use in commercial and proprietary projects with minimal restrictions—only attribution required. No license compatibility concerns for most use cases.
last release 2026-08-01 (13 days) · last repo commit 2026-08-13 · 39,349 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 56,523,911 downloads/mo, #527 on PyPI
Alternatives
Verify before relying
pip install polars-runtime-32
import polars as pl
df = (
pl.scan_parquet("data.parquet")
.filter(pl.col("status") == "active")
.group_by("id")
.agg(pl.col("amount").sum())
.collect()
)- Whether polars-runtime-32 is a standalone package or a dependency of another package—documentation does not clarify its role.
- Performance benchmarks against other DataFrame libraries (PDS-H mentioned but not detailed in excerpt).
- GPU acceleration requirements and NVIDIA driver compatibility for optional GPU support.
- Exact relationship between polars-runtime-32 and the main analytical engine it powers.
What it is and what it does
Polars-runtime-32 provides compiled runtime binaries for a high-performance analytical query engine written in Rust. It enables fast, memory-efficient data processing on DataFrames through multi-threaded, vectorized (SIMD) execution. The engine supports both lazy evaluation (with automatic query optimization) and eager execution modes, and can process datasets larger than available RAM using a streaming engine.
The runtime integrates with Apache Arrow for zero-copy data sharing and offers an expressive API for composing complex queries through expressions. It runs on Python 3.10+, supports multiple platforms (macOS, Linux, Windows, ARM), and is designed for analytical workloads in data science and research. No runtime dependencies are required beyond Python itself.
Use it for
- Process large Parquet or CSV files with filtering, grouping, and aggregation in a single optimized query.
- Analyze datasets larger than available RAM using streaming execution to reduce memory footprint.
- Build data pipelines with lazy evaluation to defer computation until explicitly collected.
- Perform complex multi-step analytics (joins, window functions, expressions) with automatic parallelization.
- Share data with other Arrow-compatible tools and libraries without serialization overhead.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Polars-runtime-32 is actively maintained, has no known vulnerabilities, and carries a permissive MIT license. Install friction is moderate but manageable for modern Python environments (3.10+). Choose it if you need fast, memory-efficient DataFrame operations or plan to work with larger-than-RAM datasets.
Install
polars-runtime-32 on PyPI
Before you install
Medium install friction due to compiled wheels for multiple platforms (x86_64, ARM, macOS, Linux, Windows). Actively maintained with recent release (13 days old) and strong repository signals (39349 stars, last commit 2026-08-13). Requires Python 3.10 or later.
Requires Python 3.10 or later. Pre-compiled wheels available for common platforms; older CPUs (pre-2011) or non-standard architectures may require source compilation with Rust toolchain.
License in practice
MIT license (permissive) allows use in commercial and proprietary projects with minimal restrictions—only attribution required. No license compatibility concerns for most use cases.
Quickstart
pip install polars-runtime-32
import polars as pl
df = (
pl.scan_parquet("data.parquet")
.filter(pl.col("status") == "active")
.group_by("id")
.agg(pl.col("amount").sum())
.collect()
)Verify before relying
- Whether polars-runtime-32 is a standalone package or a dependency of another package—documentation does not clarify its role.
- Performance benchmarks against other DataFrame libraries (PDS-H mentioned but not detailed in excerpt).
- GPU acceleration requirements and NVIDIA driver compatibility for optional GPU support.
- Exact relationship between polars-runtime-32 and the main analytical engine it powers.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 13 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 56,523,911 / month, #527 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: RustTopic :: Scientific/EngineeringTyping :: Typed |
Evidence: polars_runtime_32-1.43.2-cp310-abi3-macosx_10_12_x86_64.whl; polars_runtime_32-1.43.2-cp310-abi3-macosx_11_0_arm64.whl; polars_runtime_32-1.43.2-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; polars_runtime_32-1.43.2-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; polars_runtime_32-1.43.2-cp310-abi3-musllinux_1_2_aarch64.whl; polars_runtime_32-1.43.2-cp310-abi3-musllinux_1_2_x86_64.whl; polars_runtime_32-1.43.2-cp310-abi3-win_amd64.whl; polars_runtime_32-1.43.2-cp310-abi3-win_arm64.whl
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See also polars · polars-runtime-64 · polars-runtime-compat · polars-ols · polars-cloud · polars-lts-cpu · grizz · patito · polars-ds · dataframely