polars

To build a Polars lazy pipeline for large CSV files, use `scan_*` functions to define operations like selecting columns and filtering rows before execution, allowing Polars to optimize the query graph via predicate and projection pushdown.

The best way to migrate pandas groupby and apply patterns to Polars is to translate them into idiomatic Polars expressions using `pl.col(...)` and `.over(...)` for window calculations, avoiding Python UDFs to maintain parallel execution speed.

Lazy evaluation in Polars optimizes DataFrame transformations by building a query graph instead of executing immediately, enabling the engine to push down predicates for filtering and projections for column selection to reduce memory usage.

Yes, you can use Polars expressions for feature engineering and window calculations by applying `.over(...)` to compute complex aggregations across analytical pipelines while keeping execution parallel and efficient.

You should avoid Python UDFs in Polars data processing workflows because they break the native Rust execution path, preventing the engine from parallelizing operations and applying critical optimizations like predicate pushdown.

Polars supports streaming for large dataset aggregation, allowing you to process data that exceeds available memory by chunking the execution and applying grouping and aggregation operations efficiently across the pipeline.