polars-lts-cpu
Blazingly fast DataFrame library
Decision gist · record as of 2026-08-14
Yes. polars-lts-cpu is actively maintained, has no runtime dependencies, supports current Python versions, carries no known vulnerabilities, and is licensed permissively. Install friction is moderate due to compiled wheels, but pre-built binaries are available for all major platforms. It is a solid choice if you need fast DataFrame operations or must handle larger-than-RAM data.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; CPU-optimized build may not support older processors lacking AVX2 (use LTS_CPU=1 when compiling from source for older CPUs).
- Medium install friction due to compiled wheels; no runtime dependencies.
- Active maintenance with recent commits and frequent releases (weekly or more often).
License · maintenance · safety
permissive license (permissive) — MIT license (permissive) means you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2025-09-09 (339 days) · last repo commit 2026-08-14 · 39,355 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 632,150 downloads/mo, #5,654 on PyPI
Alternatives
Verify before relying
pip install polars-lts-cpu
import polars as pl
df = pl.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
result = df.select("A").filter(pl.col("A") > 1)- Exact performance improvements over pandas or other dataframe libraries in specific workloads
- Memory overhead of the Rust runtime compared to pure Python implementations
- Streaming mode performance characteristics and typical slowdown vs. in-memory execution
What it is and what it does
polars-lts-cpu is a DataFrame library implemented in Rust that provides an OLAP query engine using Apache Arrow as its columnar memory format. It offers both lazy (query-optimized) and eager execution modes, with multi-threading and SIMD acceleration built in. The package has zero required runtime dependencies and imports quickly.
You use it to load, transform, and analyze tabular data through an expressive query API. It supports SQL queries directly on DataFrames, handles datasets larger than available RAM through streaming execution, and is designed for scientific and data engineering workflows. The LTS CPU variant is optimized for standard processors and includes pre-built wheels for macOS (Intel and ARM), Linux (x86_64 and ARM64), and Windows (x86_64 and ARM64).
Use it for
- Process CSV or Parquet files larger than available memory using streaming execution
- Write SQL queries directly on DataFrames for exploratory analysis and aggregations
- Build data pipelines with lazy evaluation to optimize query plans before execution
- Perform grouped aggregations and window functions on large datasets with multi-threaded parallelism
- Replace pandas workflows where performance or memory efficiency is critical
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
polars-lts-cpu is actively maintained, has no runtime dependencies, supports current Python versions, carries no known vulnerabilities, and is licensed permissively. Install friction is moderate due to compiled wheels, but pre-built binaries are available for all major platforms. It is a solid choice if you need fast DataFrame operations or must handle larger-than-RAM data.
Install
polars-lts-cpu on PyPI
Before you install
Medium install friction due to compiled wheels; no runtime dependencies. Active maintenance with recent commits and frequent releases (weekly or more often). Supports Python 3.9 through 3.13 across multiple platforms.
Requires Python 3.9 or later; CPU-optimized build may not support older processors lacking AVX2 (use LTS_CPU=1 when compiling from source for older CPUs).
License in practice
MIT license (permissive) means you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install polars-lts-cpu
import polars as pl
df = pl.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
result = df.select("A").filter(pl.col("A") > 1)Verify before relying
- Exact performance improvements over pandas or other dataframe libraries in specific workloads
- Memory overhead of the Rust runtime compared to pure Python implementations
- Streaming mode performance characteristics and typical slowdown vs. in-memory execution
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 339 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 632,150 / month, #5,654 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 :: Python :: 3.9Programming Language :: RustTopic :: Scientific/EngineeringTyping :: Typed |
Evidence: polars_lts_cpu-1.33.1-cp39-abi3-macosx_10_12_x86_64.whl; polars_lts_cpu-1.33.1-cp39-abi3-macosx_11_0_arm64.whl; polars_lts_cpu-1.33.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; polars_lts_cpu-1.33.1-cp39-abi3-manylinux_2_24_aarch64.whl; polars_lts_cpu-1.33.1-cp39-abi3-win_amd64.whl; polars_lts_cpu-1.33.1-cp39-abi3-win_arm64.whl
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See also polars-ds · polars-runtime-64 · polars-ols · polars · polars-runtime-compat · polars-runtime-32 · polars-cloud · dataframely · narwhals