pydantable 1.1.0

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.
• Fit cleanly into FastAPI request/response flows.

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

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.
• 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.
• Materialization:
  • collect() -> list of row models
  • to_dict() -> dict[str, list]
  • to_polars() / to_arrow() with matching extras installed

I/O at a glance

• Lazy file pipelines: read_* / aread_* -> transform -> write_*
• Eager reads: materialize_*, fetch_sql, fetch_*_url
• Eager writes: export_*, write_sql
• Full I/O API is in pydantable.io

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
• Quickstart: QUICKSTART
• DataFrameModel guide: DATAFRAMEMODEL
• I/O overview: IO_OVERVIEW
• FastAPI patterns: FASTAPI
• Behavioral contract: INTERFACE_CONTRACT
• Versioning policy: VERSIONING
• Changelog: changelog

Development

make check-full

Contributor setup and release workflow: DEVELOPER

License

MIT