polars

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. Install Polars: Basic DataFrame creation and operations: Expressions are the fundamental building blocks of Polars operations. They describe transformations on data and can be composed, reused, and optimized. Key principles: Example: Eager (DataFrame): Operations execute immediately Lazy (LazyFrame): Operations build a query plan, optimized before execution When to use lazy: Benefits of lazy evaluation: For detailed concepts, load references/core_concepts.md . Select and manipulate columns: Filter rows by conditions: Add or modify columns while preserving existing ones: Group data and compute aggregations: For detailed operation patterns, load references/operations.md . Common aggregations within group_by context: Apply aggregations while preserving row count: Mapping strategies: Polars supports reading and writing: CSV: Parquet (recommended for performance): JSON: For comprehensive I/O documentation, load references/io_guide.md . Combine DataFrames: Stack DataFrames: Reshape data: For detailed transformation examples, load references/transformations.md .