PydanTable
Strongly typed DataFrames for Python, powered by Rust.
PydanTable combines Pydantic schemas with a Polars-backed Rust execution engine to provide a typed, service-friendly DataFrame API (with optional integrations for FastAPI, SQL, MongoDB, Spark, and more).
Current release: 1.19.2 — highlights in the changelog.
Documentation
- Docs (latest): pydantable.readthedocs.io
- Quickstart: Getting started → Quickstart
- Docs map: Getting started → Docs map
- Documentation chat (RAG): pydantable-rag/README.md — backend and deployment for the docs assistant; the chat widget is on the hosted docs above.
What you get
- Typed tables via Pydantic models:
DataFrameModelorDataFrame[Schema] - Typed expressions + lazy plans validated/lowered in Rust
- Explicit materialization:
collect()(rows) orto_dict()(columns), plus optional Arrow/Polars exports - File / HTTP / SQL I/O helpers and integration patterns for services
Key references:
- DataFrameModel: User guide → DataFrameModel
- Execution: User guide → Execution
- Materialization: User guide → Materialization
- Interface contract: Semantics → Interface contract
- I/O overview: I/O → Overview
Install
pip install pydantable
Requires Python 3.10+. Wheels include the Rust extension (pydantable-native).
Verify your install
import pydantable
pydantable.DataFrameModel # import check
from pydantable.engine import native_engine_capabilities
caps = native_engine_capabilities()
assert caps.extension_loaded, "Native extension missing — see Troubleshooting in the docs"
If verification fails, see Troubleshooting or the Installation guide.
Optional 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]" # SQLModel + SQLAlchemy + moltres-core lazy SqlDataFrame; add a DB-API driver for your URL
pip install "pydantable[pandas]" # pandas-flavored façade (pandas UI doc)
pip install "pydantable[fastapi]" # FastAPI integration (pydantable.fastapi)
pip install "pydantable[mongo]" # pymongo + Beanie + Mongo plan stack (lazy MongoDataFrame + I/O + from_beanie)
pip install "pydantable[spark]" # SparkDataFrame / SparkDataFrameModel (raikou-core + pyspark + sparkdantic)
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())
print([r.model_dump() for r in result.collect()])
Output (one run):
{'id': [1], 'age2': [40]}
[{'id': 1, 'age2': 40}]
Next steps
- Install & verify: Installation guide
- Start here: Quickstart
- Typing guide: User guide → Typing
- I/O decision tree: I/O → Decision tree
- FastAPI golden path: Integrations → FastAPI → Golden path
- Engines: SQL · Mongo · Spark
Development
Use a virtual environment at .venv in the repo root (the Makefile defaults to .venv/bin/python). See CONTRIBUTING.md and the Developer guide.
make check-full # ruff, ty, pyright, typing snippet tests, MkDocs, Rust
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for pydantable 1.19.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pydantable-1.19.2.tar.gz | 218.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pydantable-1.19.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 473.1 kB
Release files / pydantable-1.19.2.tar.gz
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| Size | 218.5 kB |
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| Size | 254.6 kB |
| Tags | Python 3 |
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