Polars

If you're still using pandas out of habit, this is your nudge to try Polars. It's built on Apache Arrow and genuinely faster, especially once your datasets get big enough to hurt. The expression API is cleaner than pandas (no more lambda chaining), lazy evaluation means you can scan giant CSVs without loading them entirely into memory, and operations parallelize by default. The skill covers the core patterns: select, filter, group_by, window functions with over(), and practical stuff like when to use LazyFrame versus DataFrame. The pandas migration reference is honest about the differences, no index, strict typing, different syntax, which saves you from discovering them the hard way at 2am.

skills.sh installs: 539
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last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill polars --agent claude-code

Installs into .claude/skills of the current project.

Files

Polars

Overview

Polars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.

Quick Start

Installation and Basic Usage

Install Polars:

uv pip install polars

Basic DataFrame creation and operations:

import polars as pl

# Create DataFrame
df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["NY", "LA", "SF"]
})

# Select columns
df.select("name", "age")

# Filter rows
df.filter(pl.col("age") > 25)

# Add computed columns
df.with_columns(
    age_plus_10=pl.col("age") + 10
)

Core Concepts

Expressions

Expressions are the fundamental building blocks of Polars operations. They describe transformations on data and can be composed, reused, and optimized.

SKILL.md on GitHub