Transform data with high-performance Polars
polarsskillsetup L2★27,559
K-Dense-AI/scientific-agent-skills ↗What it does
Process DataFrames at scale with lazy evaluation and vectorized operations
Best for
Data transformations faster and more memory-efficient than pandas
Inputs
- · CSV/Parquet/JSON files
- · SQL queries
Outputs
- · aggregated DataFrame
- · statistics
- · joined tables
Requires
- · Polars
- · DuckDB
Preconditions
Data in supported format; sufficient disk space
Failure modes
Schema inference wrong; out-of-memory; SQL dialect mismatch