polars

name	polars
version	1.0.0
description	High-performance DataFrame library for fast data processing with lazy evaluation, parallel execution, and memory efficiency
type	reference
author	workspace-hub
category	data-analysis
capabilities	["Lazy evaluation and query optimization","Parallel processing on all CPU cores","Memory-efficient operations for large datasets","Expression-based API for complex transformations","Streaming processing for out-of-memory datasets","Zero-copy data sharing with Arrow"]
tools	["polars","pyarrow","connectorx"]
tags	["polars","dataframe","performance","parallel","lazy-evaluation","arrow","rust","data-processing"]
platforms	["python","rust"]
related_skills	["pandas-data-processing","numpy-numerical-analysis","streamlit","dash"]
requires	[]
scripts_exempt	true

Polars

When to Use This Skill

USE Polars when:

• Large datasets - Working with data too large for pandas (10GB+)
• Performance critical - Need maximum speed for data transformations
• Memory constrained - Limited RAM requires efficient memory usage
• Parallel processing - Want to utilize all CPU cores automatically
• Complex aggregations - Group by, window functions, rolling calculations
• Lazy evaluation - Query optimization before execution matters
• ETL pipelines - Building production data pipelines
• Streaming data - Processing data larger than memory

DON'T USE Polars when:

• Pandas ecosystem required - Need specific pandas-only libraries
• Small datasets - Under 100MB where pandas is sufficient
• Legacy code - Extensive existing pandas codebase
• Matplotlib/Seaborn direct integration - These work better with pandas
• Time series with specialized needs - Some pandas time series features are more mature

Prerequisites

# Basic installation
pip install polars

# With all optional dependencies
pip install 'polars[all]'

# Specific extras
pip install 'polars[numpy,pandas,pyarrow,fsspec,connectorx,xlsx2csv,deltalake,timezone]'

# Using uv (recommended)
uv pip install polars pyarrow connectorx

Complete Examples

Example 1: ETL Pipeline for Sales Data

import polars as pl
from pathlib import Path
from datetime import datetime

def etl_sales_pipeline(
    input_dir: Path,
    output_dir: Path,
    min_date: str = "2025-01-01"
) -> dict:

*See sub-skills  full details.*


```python
 polars  pl
 numpy  np
 datetime  datetime, timedelta

 (