Polars Has a Free DataFrame Library — Pandas Alternative That is 10-100x Faster

A data engineer processed a 5GB CSV with pandas. RAM usage: 15GB. Processing time: 8 minutes. The laptop fan sounded like a jet engine.

Polars is a DataFrame library written in Rust. 10-100x faster than pandas, uses less memory, and has a cleaner API.

What Polars Offers for Free

• 10-100x Faster - Written in Rust with SIMD and multi-threading
• Lazy Evaluation - Query optimizer plans the best execution strategy
• Streaming - Process larger-than-RAM datasets
• Rust/Python/Node.js - Available in multiple languages
• Apache Arrow - Zero-copy data exchange
• SQL Interface - Query DataFrames with SQL
• Expressive API - Clean, chainable operations

Quick Start

import polars as pl

df = pl.read_csv('data.csv')  # 10x faster than pd.read_csv

result = (
    df.lazy()
    .filter(pl.col('age') > 25)
    .group_by('city')
    .agg(pl.col('salary').mean())
    .sort('salary', descending=True)
    .collect()  # executes optimized query plan
)

GitHub: pola-rs/polars - 32K+ stars

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