polars

# Polars ## When to Use - You need a faster in-memory DataFrame workflow than pandas for data that still fits in RAM. - You are building ETL, analytics, or transformation pipelines that benefit from lazy evaluation and parallel execution. - You want expression-based tabular operations on top of Apache Arrow semantics. ## 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: ```python uv pip install polars ``` Basic DataFrame creation and operations: ```python 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. **Key principles:** - Use `pl.col("column_name")` to reference columns - Chain methods to build complex transformations - Expressions are lazy and only execute within contexts (select, with_columns, fil...