Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB da
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB da
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.
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)
