Pyfake

A Flexible and Extensible fake data generator based on Pydantic models.

Docs: pyfake.readthedocs.io/en/latest  •  Source: github.com/Mukhopadhyay/pyfake

✨ Why Pyfake?

Most fake data generators are either:

❌ Random but not structured
❌ Structured but not realistic
❌ Hard to extend

Pyfake fixes that.

It leverages Pydantic models as the single source of truth to generate:

• Validated data
• Schema-aware fake data
• Easily extensible generators
• Strong typing + IDE autocomplete

Quick Example

from pyfake import Pyfake
from pydantic import BaseModel, Field
from typing import Annotated, Union


class SubModel(BaseModel):
    sub_integer: int
    sub_string: str


class Model(BaseModel):
    integer_field: Annotated[int | None, Field(ge=0, le=100)]
    string_field: Annotated[str, Field(min_length=5, max_length=10, pattern=r"^[a-zA-Z]+$")]
    multi_type_field: Union[Annotated[int, Field(ge=0, le=100)], Annotated[str, Field(min_length=3, max_length=15)]]
    sub_model: SubModel


users = Pyfake.from_schema(User, num=5)

print(users)

Installation

uv add pyfake

python -m venv .venv
source .venv/bin/activate
pip install pyfake

How It Works

Pyfake reads your Pydantic schema and:

• Inspects field types and constraints
• Applies intelligent generators
• Produces validated fake data

flowchart LR
    A[Pydantic Model] --> B[Schema Parser]
    B --> C[Generator Engine]
    C --> D[Validated Fake Data]