---
id: polars-lts-cpu
version: "1.33.1"
license: unclear
license_treatment: permissive
maintenance: active
---

# polars-lts-cpu — Blazingly fast DataFrame library

License: permissive · Maintenance: active · Downloads: 632.1K/mo

## 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 above — verify before relying.

polars-lts-cpu is a CPU-optimized DataFrame library that executes queries in Rust with lazy or eager evaluation, multi-threading, and SIMD support, handling datasets larger than RAM through streaming.

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

pip install polars-lts-cpu
uv add polars-lts-cpu
poetry add polars-lts-cpu

## Installing polars-lts-cpu

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.

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)

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).

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: not declared (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 632.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

fast dataframe library, arrow columnar format, lazy query execution, out-of-core data processing, rust-backed dataframes, multi-threaded analytics, streaming larger-than-ram datasets, dataframe-engine, query-optimization, streaming-analytics

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