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PydanTable

PydanTable gives you Pydantic-validated tables with a DataFrame API (filter, join, groupby) and a Rust/Polars execution core — so FastAPI services can accept, transform, and return typed columnar data without maintaining parallel pandas and schema definitions.

This site is the full manual; the repository README is the short entrypoint for install one-liners.

See also

Execution builds on Polars (see their user guide) and ships as a native Rust extension. PydanTable adds Pydantic-first schemas, SQLModel-style tabular models, and service / data I/O patterns on top.

Note

Current release: see CHANGELOG — stable 1.x under VERSIONING. Roadmap: ROADMAP; SQLModel milestones: SQLMODEL_SQL_ROADMAP.

Minimal example

from pydantable import DataFrameModel


class Sales(DataFrameModel):
    id: int
    score: float


df = Sales({"id": [1, 2], "score": [10.0, 20.5]})

Choose your path

Where pydantable fits

Guide map