Overview

arrowmodel converts Apache Arrow RecordBatch and Table objects directly into Pydantic v2 model instances – no intermediate Python dicts, no two-step materialisation. A tight Rust loop walks the Arrow buffers via the Arrow C Data Interface and hands you back typed models, roughly 2x faster than to_pylist() + Pydantic construction.

import pyarrow as pa
from arrowmodel import ArrowModel


class User(ArrowModel):
    id: int
    name: str
    score: float


batch = pa.record_batch(
    {
        "id": [1, 2, 3],
        "name": ["Alice", "Bob", "Carol"],
        "score": [9.5, 8.0, 7.3],
    }
)

users = User.convert(batch)
# [User(id=1, name='Alice', score=9.5), ...]

Dict-Free Conversion

Skips to_pylist() entirely. Arrow buffers go straight to model_construct calls in a single Rust loop.

~2x Faster

Roughly twice as fast as the pure-Python approach for flat schemas, with less memory allocation pressure.

Any Arrow Source

Accepts any Arrow-PyCapsule-compatible input – pyarrow, Polars, nanoarrow – via the Arrow C Data Interface.

Pydantic Aliases

Full alias resolution: validation_alias, alias, field name, and populate_by_name / validate_by_name.

Nested Models

Arrow Struct columns map automatically to nested Pydantic models, including Optional structs and deeply nested hierarchies.

Validated Mode

Choose between the fast path (model_construct, no validation) and the validated path (model_validate_json, full Pydantic checks).

Getting Started

Install with pip or uv:

pip

pip install arrowmodel

uv

uv add arrowmodel

No Rust toolchain needed – pre-built wheels are provided.

Then head to the Getting Started tutorial to convert your first batch.

Get Started