polars

You can process large datasets faster than pandas by using a high-performance DataFrame library built in Python and Rust. It leverages parallel execution and Apache Arrow to optimize in-memory data manipulation tasks like filtering and aggregating.

Lazy evaluation in data manipulation defers computations until results are explicitly requested. Using an expression-based API, it optimizes query plans for vectorized operations, enabling efficient execution and parallel processing for large datasets.

Yes, this approach supports ETL pipelines for datasets up to 100GB. It targets large datasets that fit in memory, providing fast data filtering, selection, and aggregation without compromising performance.

To aggregate a CSV file using a pandas alternative, you create a DataFrame from the CSV file. You can then perform high-efficiency data aggregation and filtering operations using the library's expression-based API.

Yes, the polars library is required to run these data manipulation tasks. It provides the underlying Rust-based implementation and Apache Arrow integration required for parallel execution and in-memory data processing.

The primary limitation of in-memory DataFrame libraries is that datasets must fit within available system memory. While it handles 1-100GB datasets efficiently, exceeding memory capacity prevents successful data processing and ETL pipeline execution.