data-aggregator

name	data-aggregator
description	Aggregate tabular data by one or more dimensions with configurable metrics — sum, mean, count, min, max, median. Supports multi-level grouping and pivot table generation.
version	0.8.0

Data Aggregator

Skill Summary

Performs group-by aggregations on tabular datasets. Supports single and multi-level grouping, configurable aggregate functions (sum, mean, count, min, max, median, std), and pivot table generation for cross-dimensional analysis. Can output flat aggregation tables or pivot matrices.

Aggregation Modes

• flat: Standard group-by → one row per group
• pivot: Cross-tabulation with row/column dimensions and value metric
• rolling: Time-based rolling window aggregation

Inputs

• --input / -i: Path to input file (CSV or Parquet) (required)
• --group-by / -g: Grouping columns (comma-separated) (required)
• --metrics: Aggregation specs like revenue:sum,quantity:mean,order_id:count
• --mode: flat, pivot, or rolling (default: flat)
• --pivot-columns: Column for pivot table columns (for pivot mode)
• --pivot-values: Value column for pivot table (for pivot mode)
• --window: Window size for rolling mode
• --output / -o: Output path
• --sort-by: Sort result by specified column (descending)

Processing Steps

1. Load dataset
2. Parse metric specifications (column:function pairs)
3. Apply group-by aggregation or pivot
4. Sort results by specified column if provided
5. Calculate group-level subtotals if multi-level grouping
6. Save aggregated result

Output

• Aggregated dataset (CSV or Parquet)
• Console: Group count, top 10 groups by first metric

Implementation

• Command: python3 ./skills/data_aggregator/aggregate.py -i <file> -g <columns> --metrics <specs>
• Dependencies: pandas, numpy