polars

To process large datasets faster than pandas, use Polars for high-performance DataFrame operations with lazy evaluation, parallel execution, and an Apache Arrow backend to optimize query plans before executing.

You need lazy evaluation for data processing when working with large datasets from 1-100GB, as it optimizes query plans before execution, making complex analysis and ETL pipelines significantly faster.

To read and process a large CSV file efficiently, use Polars to parse the file and perform high-speed data manipulation, leveraging its expression-based API for selection, filtering, and aggregation.

Polars is an ideal alternative for ETL pipelines handling 1-100GB of data, bridging the gap when processing is too slow with pandas but the dataset is too large for distributed tools like dask.

A limitation of using this pandas alternative is that it requires the Polars library to parse CSV files and perform data manipulation, relying heavily on its expression-based API rather than traditional pandas syntax.