pyfake 0.0.9

A fake data generator that is pydantic compatible.

A flexible, schema-driven fake data generator built on top of Pydantic v2.

Documentation · Github · PyPI

Generate realistic fake data for your Pydantic models with ease. Perfect for testing, prototyping, and anywhere you need valid mock data.

⚡ Quick Example

from typing import Annotated, List, Set, Literal
from pydantic import BaseModel, Field
from pyfake import fake
from rich import print


class Playlist(BaseModel):
    track_ids: List[int]
    genre: Literal["rock", "pop", "jazz"]
    tags: Annotated[List[str], Field(min_length=2, max_length=5)]
    unique_ratings: Set[int]

result = fake(Playlist, as_dict=True)
print(result)

# {
#     "track_ids": [28, 25, 95, 40], 
#     "genre": "pop", 
#     "tags": ["CJKHILHXTN", "qkhhjDJYiV"], 
#     "unique_ratings": {17, 49}
# }

✨ Why Pyfake?

Problem	Most fake data generators	Pyfake
Random but not structured	❌ Generates random data without understanding the schema	✅ Reads your Pydantic models to produce structured, schema-aware data
Structured but not realistic	❌ Generates data that fits the schema but isn't realistic (e.g. random strings for names)	✅ Uses intelligent generators to produce realistic fake data (e.g. names, addresses)
Hard to extend	❌ Difficult to add custom generators or handle complex types	✅ Easily extensible with a flexible generator registry and schema resolution system
Support for constraints	❌ Ignores field constraints like min_length, gt, multiple_of	✅ Respects all Pydantic field constraints when generating data
Support for python primitive types	❌ Limited support for complex types like Decimal, UUID, datetime	✅ Full support for Python primitives, including Decimal, UUID, datetime, and more
Reproducibility	❌ No built-in way to generate the same fake data across runs	✅ Supports seeding for reproducible fake data generation

🧠 How It Works

Pyfake reads your Pydantic schema and:

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

Installation

Using uv (Recommended)

uv add pyfake

Using pip

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