percategory

Percategory is a term used in data processing and software design to describe performing operations independently within each category of a dataset. It denotes a pattern in which grouping by a category key is followed by applying a transformation or aggregation that is scoped to that category, with results organized by category.

In practice, percategory appears in analytics pipelines, dashboards, and machine learning preprocessing, where categories may represent products, regions, user segments, or time intervals. It can be implemented in batch processing languages via a group-by operation and per-group computation, or in streaming systems by maintaining per-category state and updating it as new records arrive.

Common percategory operations include counting, summing, averaging, computing min or max values, or calculating category-specific distributions. The approach supports dynamic category sets and handles missing or new categories by initializing per-category state as needed.

Advantages include clear semantic separation by category, natural parallelism, and targeted insights per category. Drawbacks include memory overhead for many categories, potential skew where a few categories dominate workload, and complexity in maintaining consistent state across updates.

In related terms, percategory overlaps with per-group or per-key processing found in SQL with GROUP BY, pandas groupby, or streaming per-key aggregations. Some systems label it as per-category aggregation or per-group computation.