polars

# 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: ```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, filter, group_by) **Example:** ```python # Expression-based computation df.select( pl.col("name"), (pl.col("age") * 12).alias("age_in_months") ) ``` ### Lazy vs Eager Evaluation **Eager (DataFrame):** Operations execute immediately ```python df = pl.read_csv("file.csv") # Reads immediately result = df.f...