Dataframen

Dataframen is a theoretical data structure that extends the familiar data frame concept with enhanced metadata and reproducibility features. Like a traditional data frame, it stores tabular data as a collection of labeled columns and indexed rows and supports common operations such as selection, filtering, and aggregation. The distinguishing aspect is the integration of data lineage, versioning, and optional immutability, which together help preserve the history of transformations and support rollback and auditability.

The term dataframen appears mainly in theoretical discussions, library proposals, and educational material rather than as a standardized, widely adopted construct. It is used to explore design tradeoffs for reproducible data workflows and to compare approaches to tracking provenance alongside data transformations.

Core features commonly described for dataframen include typed columns, explicit schemas with validation, support for columnar storage, and efficient, lazy evaluation of chained operations. Metadata about data provenance, transformation steps, and versioned states are typically stored alongside the data, enabling snapshotting and selective replay of pipelines. Interoperability with existing data frame APIs and file formats is considered important for practical adoption.

As a concept, dataframen has no single reference implementation or standard API. Real-world usage depends on the host language and library ecosystem, and users may face interoperability or performance considerations when integrating with non-versioned data frames.

See also related concepts such as data frame, data provenance, and reproducible data workflows.