pydantable 1.9.0

Strongly-typed DataFrames for Python, powered by Rust.

PydanTable

Typed DataFrame workflows for Python services, with Pydantic schemas and a Rust execution core.

Why PydanTable

• Define table shape once using Pydantic types.
• Catch many errors early with typed expressions.
• Use familiar DataFrame operations (select, filter, join, group_by, windows).
• Materialize as row models or dict[str, list], depending on API needs.
• FastAPI: optional pydantable.fastapi helpers — shared executor lifespan, NDJSON streaming from astream(), OpenAPI-friendly columnar bodies (columnar_dependency / rows_dependency), and register_exception_handlers (503 / 400 / 422 for common failures). See the FastAPI guide and golden path.

Install

pip install pydantable

Common extras:

pip install "pydantable[polars]"  # to_polars
pip install "pydantable[arrow]"   # to_arrow / Arrow constructors
pip install "pydantable[io]"      # full file I/O convenience (arrow + polars)
pip install "pydantable[sql]"     # fetch_sql / write_sql helpers
pip install "pydantable[fastapi]" # FastAPI integration helpers (pydantable.fastapi)

Quick start

from pydantable import DataFrameModel

class User(DataFrameModel):
    id: int
    age: int | None

df = User({"id": [1, 2], "age": [20, None]})
result = (
    df.with_columns(age2=df.age * 2)
      .filter(df.age > 10)
      .select("id", "age2")
)

print(result.to_dict())   # {'id': [1], 'age2': [40]}
print(result.collect())   # list of Pydantic row models

Core concepts

• DataFrameModel: SQLModel-like table class (class Orders(DataFrameModel): ...).
• DataFrame[Schema]: generic API over your own Pydantic BaseModel.
• Expr: typed expressions used in transforms.
• Errors: predictable ingest failures such as column length mismatch raise ColumnLengthMismatchError (subclass of ValueError) from pydantable.errors; map to HTTP 400 in FastAPI via register_exception_handlers.
• Static typing:
  • mypy can infer schema-evolving return types for many transform chains (via the mypy plugin).
  • pyright/Pylance relies on shipped stubs; use as_model(...) / try_as_model(...) / assert_model(...) when you want an explicit after-schema model.
• 1.2.0 column types (typing.Literal[...], ipaddress IPv4/IPv6, WKB, Annotated[str, ...]) are documented in SUPPORTED_TYPES (including Expr comparison notes).
• Materialization:
  • collect() -> list of row models
  • to_dict() -> dict[str, list]
  • to_polars() / to_arrow() with matching extras installed

I/O at a glance

• Default: DataFrameModel / DataFrame[Schema] — lazy read_* / aread_*, export_*, write_sql / awrite_sql, …; eager materialize_* / fetch_sql / iter_sql live on pydantable.io → pass dict[str, list] into constructors for typed frames.
• Lazy file pipelines: MyModel.read_* / await MyModel.aread_* → transform → write_*
• The pydantable.io package exposes raw helpers (dict[str, list], ScanFileRoot) for scripts and glue code — see IO_OVERVIEW in the docs.

Validation controls

• Strict by default on constructors.
• Optional ingest controls: trusted_mode, ignore_errors, on_validation_errors.
• Missing optional fields are controlled by fill_missing_optional (default True).

Documentation

• Docs home: pydantable.readthedocs.io
• Pandas-flavored API (assign, merge, duplicates, get_dummies, …): PANDAS_UI
• Where to read what: DOCS_MAP
• Quickstart: QUICKSTART
• DataFrameModel guide: DATAFRAMEMODEL
• I/O overview: IO_OVERVIEW
• FastAPI: GOLDEN_PATH_FASTAPI → FASTAPI → FASTAPI_ENHANCEMENTS (roadmap, troubleshooting)
• Cookbooks (FastAPI): columnar bodies · async materialization · observability (request IDs + observe) · background submit · lazy async pipeline — index: Cookbook
• Example multi-router app (copy from repo): docs/examples/fastapi/service_layout/
• Tests: pydantable.testing.fastapi (fastapi_test_client, fastapi_app_with_executor) — see FASTAPI
• Execution & materialization: EXECUTION · MATERIALIZATION
• Behavioral contract: INTERFACE_CONTRACT
• Troubleshooting: TROUBLESHOOTING
• Versioning policy: VERSIONING
• Changelog: changelog

Development

Create and use a virtual environment at .venv in the repo root (the Makefile defaults to .venv/bin/python). Contributor setup, Maturin/Rust builds, and release workflow: DEVELOPER.

make check-full

License

MIT