polars-runtime-64
Blazingly fast DataFrame library
Decision gist · record as of 2026-08-14
Yes. This package is production-stable, actively maintained, permissively licensed, and carries no known vulnerabilities. Install it if you need fast analytical queries on large datasets or want performance-critical data transformation work. The medium install friction (compiled wheels, Python 3.10+ requirement) is standard for Rust-based Python packages and not a barrier for most modern environments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; pre-built wheels target modern CPUs (AVX2 support assumed unless LTS_CPU=1 flag used during source compilation).
- Medium install friction due to compiled Rust binaries; wheels are pre-built for common platforms (macOS, Linux, Windows on x86_64 and ARM64) but require Python 3.10+.
- Active maintenance with recent release (13 days old) and strong repository signals (39355 stars, current commit 2026-08-14).
License · maintenance · safety
MIT (permissive) — MIT license (permissive) allows commercial and private use with minimal restrictions—suitable for most projects without licensing concerns.
last release 2026-08-01 (13 days) · last repo commit 2026-08-14 · 39,355 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 161,231 downloads/mo, #10,639 on PyPI
Alternatives
Verify before relying
pip install polars-runtime-64
import polars as pl
df = (
pl.scan_parquet("orders.parquet")
.filter(pl.col("status") == "shipped")
.group_by("customer_id")
.agg(pl.col("amount").sum().alias("total"))
.collect()
)- Whether GPU acceleration (NVIDIA) is included in the standard PyPI wheel or requires separate installation.
- Memory overhead of lazy query optimization and expression compilation for typical workloads.
- Performance comparison to other dataframe libraries on small-to-medium datasets.
What it is and what it does
This package is a DataFrame query engine written in Rust that prioritizes speed and memory efficiency. It supports both lazy (optimized) and eager execution modes, allowing you to compose complex analytical queries using a chainable expression API. The lazy execution model optimizes your query plan before running it, and the streaming engine can process datasets that exceed available RAM by processing data in chunks.
You install it via pip and import it as `polars`. It has no runtime dependencies beyond the compiled Rust library bundled in the wheel. It's designed for analytical workloads—filtering, grouping, aggregating, and sorting large datasets—and integrates with Apache Arrow for zero-copy data sharing. The package is actively maintained, widely used (top 15000 PyPI packages by downloads), and carries no known security vulnerabilities.
Use it for
- Process multi-gigabyte CSV or Parquet files on a machine with limited RAM using the streaming engine.
- Build analytical pipelines that filter, group, and aggregate time-series or transactional data with lazy query optimization.
- Compose complex multi-step queries (joins, window functions, aggregations) that benefit from automatic query optimization.
- Interoperate with Arrow-based tools and libraries by leveraging zero-copy columnar data sharing.
- Execute vectorized data transformations where compiled Rust execution delivers measurable performance gains.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This package is production-stable, actively maintained, permissively licensed, and carries no known vulnerabilities. Install it if you need fast analytical queries on large datasets or want performance-critical data transformation work. The medium install friction (compiled wheels, Python 3.10+ requirement) is standard for Rust-based Python packages and not a barrier for most modern environments.
Install
polars-runtime-64 on PyPI
Before you install
Medium install friction due to compiled Rust binaries; wheels are pre-built for common platforms (macOS, Linux, Windows on x86_64 and ARM64) but require Python 3.10+. Active maintenance with recent release (13 days old) and strong repository signals (39355 stars, current commit 2026-08-14).
Requires Python 3.10 or later; pre-built wheels target modern CPUs (AVX2 support assumed unless LTS_CPU=1 flag used during source compilation).
License in practice
MIT license (permissive) allows commercial and private use with minimal restrictions—suitable for most projects without licensing concerns.
Quickstart
pip install polars-runtime-64
import polars as pl
df = (
pl.scan_parquet("orders.parquet")
.filter(pl.col("status") == "shipped")
.group_by("customer_id")
.agg(pl.col("amount").sum().alias("total"))
.collect()
)Verify before relying
- Whether GPU acceleration (NVIDIA) is included in the standard PyPI wheel or requires separate installation.
- Memory overhead of lazy query optimization and expression compilation for typical workloads.
- Performance comparison to other dataframe libraries on small-to-medium datasets.
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 | 161,231 / month, #10,639 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_64-1.43.2-cp310-abi3-macosx_10_12_x86_64.whl; polars_runtime_64-1.43.2-cp310-abi3-macosx_11_0_arm64.whl; polars_runtime_64-1.43.2-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; polars_runtime_64-1.43.2-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; polars_runtime_64-1.43.2-cp310-abi3-musllinux_1_2_aarch64.whl; polars_runtime_64-1.43.2-cp310-abi3-musllinux_1_2_x86_64.whl; polars_runtime_64-1.43.2-cp310-abi3-win_amd64.whl; polars_runtime_64-1.43.2-cp310-abi3-win_arm64.whl
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See also polars · polars-lts-cpu · polars-runtime-32 · polars-runtime-compat · polars-cloud · polars-ds · polars-ols · grizz · arcticdb · patito