pydantic-numpy
Pydantic Model integration of the NumPy array
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real gap in Pydantic's NumPy support. It's suitable for production use if you need to validate and serialize NumPy arrays within Pydantic models. The permissive BSD-3-Clause license poses no restriction.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later (supports current versions, <3.15).
- Low friction install with five runtime dependencies (pydantic, numpy, ruamel-yaml, compress-pickle, semver).
- Active maintenance with a release 13 days ago; repo is not archived and has recent commits.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive; you can use, modify, and distribute this package with minimal restrictions in commercial or open-source projects.
last release 2026-08-01 (13 days) · last repo commit 2026-08-13 · 52 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 178,398 downloads/mo, #10,194 on PyPI
Alternatives
Verify before relying
pip install pydantic-numpy
import numpy as np
from pydantic import BaseModel
import pydantic_numpy.typing as pnd
class MyModel(BaseModel):
array: pnd.Np1DArrayFp64
model = MyModel(array=np.array([1.5, 2.5, 3.5]))
json_str = model.model_dump_json()
restored = MyModel.model_validate_json(json_str)- Whether custom serialization functions can handle all array types and edge cases you need.
- Performance characteristics when working with very large arrays or deeply nested structures.
- Compatibility with all Pydantic v2 features beyond the examples shown.
What it is and what it does
pydantic-numpy bridges NumPy arrays and Pydantic's validation and serialization framework. It provides typed annotations (like NpNDArrayFp64 for 3D float64 arrays) that work with both BaseModel and dataclass, plus a NumpyModel class for seamless dump/load of arrays to disk. You can instantiate models from raw arrays, NumPy .npy files, or .npz files with keys, and serialize to JSON with full schema compliance.
The package handles the impedance mismatch between NumPy's array semantics and Pydantic's type system by wrapping arrays in validated fields. It supports custom serialization, dimension constraints, dtype specification, and model-agnostic loading when multiple models might match. Runtime dependencies are minimal and well-established (pydantic, numpy, ruamel-yaml, compress-pickle, semver).
Use it for
- Validate and serialize machine-learning model inputs/outputs that include NumPy arrays as Pydantic models.
- Define strict dtype and dimension requirements for arrays in data pipelines (e.g., float32 1D arrays only).
- Persist Pydantic models containing arrays to disk and reload them with automatic type checking.
- Generate JSON schemas for APIs that accept or return NumPy array data.
- Combine NumPy arrays with other Pydantic fields in a single validated data structure.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real gap in Pydantic's NumPy support. It's suitable for production use if you need to validate and serialize NumPy arrays within Pydantic models. The permissive BSD-3-Clause license poses no restriction.
Install
pydantic-numpy on PyPI
Before you install
Low friction install with five runtime dependencies (pydantic, numpy, ruamel-yaml, compress-pickle, semver). Active maintenance with a release 13 days ago; repo is not archived and has recent commits.
Requires Python 3.11 or later (supports current versions, <3.15).
License in practice
BSD-3-Clause is permissive; you can use, modify, and distribute this package with minimal restrictions in commercial or open-source projects.
Quickstart
pip install pydantic-numpy
import numpy as np
from pydantic import BaseModel
import pydantic_numpy.typing as pnd
class MyModel(BaseModel):
array: pnd.Np1DArrayFp64
model = MyModel(array=np.array([1.5, 2.5, 3.5]))
json_str = model.model_dump_json()
restored = MyModel.model_validate_json(json_str)Verify before relying
- Whether custom serialization functions can handle all array types and edge cases you need.
- Performance characteristics when working with very large arrays or deeply nested structures.
- Compatibility with all Pydantic v2 features beyond the examples shown.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release <3.15,>=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagescompress-pickleruamel-yamlnumpypydanticsemver |
| Maintenance | Actively maintained 13 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 178,398 / month, #10,194 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableOperating System :: OS IndependentTopic :: Software Development :: Libraries :: Python Modules |
Evidence: pydantic_numpy-9.0.2-py3-none-any.whl
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See also numpydantic · pydantic-collections · pydantic_yaml · pydantic-zarr · pydantic-to-typescript2 · drf-pydantic · pydantic-scim · pydantic-to-typescript · data-science-types · Flask-Pydantic