Crate matten_data 

matten-data — a tiny table-to-Tensor preparation companion for small PoC datasets.

Status

Experimental (scaffold). This crate is an approved, scope-locked companion (RFC-033) but currently contains no public API. Table ingestion and conversion land in later releases once RFC-034 (table model) and RFC-035 (CSV ingestion and numeric conversion) are accepted and implemented. Maturity is expressed by this Status label, not by the crate version: under lock-step family versioning (RFC-030) the crate shares the workspace family version.

What it will be

A small helper for the boring step between table-like input and a numeric matten::Tensor:

small CSV / table-like data
  -> inspect schema
  -> select columns by name
  -> clean missing values explicitly
  -> convert to numeric explicitly
  -> matten::Tensor

What it is not

matten-data is not a dataframe library. It has no joins, group-by, pivot, query DSL, lazy execution, indexing/loc/iloc, rolling/window operations, datetime engine, categorical dtype system, or large-data streaming. For those workloads use Polars, DataFusion, Pandas, or another dataframe/query tool.

Relationship to core dynamic

Core matten’s dynamic feature is value-level ingestion (mixed values inside a Tensor, with explicit try_numeric()). matten-data is table-level preparation (headers, named columns, schema summary, table-shaped missing-value policy) whose end goal is a numeric Tensor. matten-data may use core dynamic internally, but it does not expose a second computation engine.

Dependency direction

matten-data depends on core matten; core matten never depends on matten-data. The dependency-boundary CI check enforces this.