patito
A dataframe modelling library built on top of polars and pydantic.
Decision gist · record as of 2026-08-14
Yes. Patito is actively maintained, has low install friction, carries no known vulnerabilities, and solves a real problem for teams using polars with pydantic. It's particularly valuable if you need strict dataframe validation and test data generation. The MIT license poses no restrictions. Start with it if your workflow involves both type-annotated models and polars dataframes.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Low install friction with only three runtime dependencies (polars, pydantic, typing-extensions).
- The package is actively maintained with recent releases and 635 repository stars, indicating stable ongoing development.
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-02-04 (191 days) · last repo commit 2026-05-08 · 635 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 594,946 downloads/mo, #5,842 on PyPI
Alternatives
Verify before relying
pip install patito
import patito as pt
from typing import Literal
class Product(pt.Model):
product_id: int = pt.Field(unique=True)
temperature_zone: Literal["dry", "cold", "frozen"]
is_for_sale: bool
df = Product.examples({"is_for_sale": [True, False]})
Product.validate(df)What it is and what it does
Patito bridges pydantic and polars by letting you define dataframe schemas as type-annotated pydantic model subclasses. A single model class serves dual purpose: it defines the schema for polars DataFrames and represents individual rows as objects. This eliminates the need to maintain separate schema definitions and row representations.
The package provides three main capabilities: efficient dataframe validation with human-readable error messages, automatic generation of valid test data that respects all schema constraints, and utilities to work with dataframes in an object-oriented manner. You can validate incoming data, generate mock dataframes for tests without boilerplate, retrieve single rows as model instances, and apply schema-aware transformations. It is designed to work with polars but also supports pandas.
Use it for
- Validate incoming dataframes against strict type and constraint rules before processing, catching schema violations early.
- Generate valid test dataframes that satisfy all model constraints without manually creating dummy data for each test.
- Retrieve and work with individual dataframe rows as typed model instances rather than raw dictionary-like objects.
- Enforce a single source of truth for data models across your codebase by using the same pydantic class for both validation and ORM-like access.
- Apply field-level constraints like uniqueness, bounds, regex patterns, and custom expressions to validate dataframe contents.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Patito is actively maintained, has low install friction, carries no known vulnerabilities, and solves a real problem for teams using polars with pydantic. It's particularly valuable if you need strict dataframe validation and test data generation. The MIT license poses no restrictions. Start with it if your workflow involves both type-annotated models and polars dataframes.
Install
patito on PyPI
Before you install
Low install friction with only three runtime dependencies (polars, pydantic, typing-extensions). The package is actively maintained with recent releases and 635 repository stars, indicating stable ongoing development.
Requires Python 3.9 or later.
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install patito
import patito as pt
from typing import Literal
class Product(pt.Model):
product_id: int = pt.Field(unique=True)
temperature_zone: Literal["dry", "cold", "frozen"]
is_for_sale: bool
df = Product.examples({"is_for_sale": [True, False]})
Product.validate(df)Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagespolarspydantictyping-extensions |
| Maintenance | Actively maintained 191 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 594,946 / month, #5,842 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: patito-0.8.6-py3-none-any.whl
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