polars-v2 - Process in-memory datasets with Polars
Process in-memory datasets with Polars using expression-based transformations, lazy evaluation, parallel execution, and Apache Arrow.
Tags
Updated: 2026-10-03Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Load tabular datasets
- Build lazy query plans
- Transform DataFrames with expressions
- Filter and select columns
- Run parallel data processing
- Migrate pandas workflows
- Preserve workflow provenance
Inputs
- Tabular datasets
- Processing task requirements
- Copied upstream support files
- Provenance metadata
Outputs
- Data processing results
- Transformed DataFrames
- Workflow summaries
- Provenance records
- Handoff recommendations
Requirements
- Python environment
- Installed Polars package
- Datasets fitting available RAM