fauxdantic 0.1.5

A library for generating fake Pydantic models for testing and development purposes

Fauxdantic

A library for generating fake Pydantic models for testing. Fauxdantic makes it easy to create realistic test data for your Pydantic models. Pairs well with testing of fastapi endpoints.

Installation

poetry add fauxdantic

Features

• Generate fake data for any Pydantic model
• Support for nested models
• Support for common Python types:
  • Basic types (str, int, float, bool)
  • Optional types
  • Lists
  • Dicts
  • UUIDs
  • Datetimes
  • Enums
• Customizable values through keyword arguments
• Generate dictionaries of fake data without creating model instances

Usage

Basic Usage

from pydantic import BaseModel
from fauxdantic import faux, faux_dict

class User(BaseModel):
    name: str
    age: int
    email: str
    is_active: bool

# Generate a fake user
fake_user = faux(User)
print(fake_user)
# Output: name='Smith' age=2045 email='[e-mail]' is_active=True

# Generate a dictionary of fake values
fake_dict = faux_dict(User)
print(fake_dict)
# Output: {'name': 'Smith', 'age': 2045, 'email': '[e-mail]', 'is_active': True}

Nested Models

from pydantic import BaseModel
from fauxdantic import faux, faux_dict

class Address(BaseModel):
    street: str
    city: str
    zip_code: str

class User(BaseModel):
    name: str
    age: int
    address: Address

# Generate a fake user with nested address
fake_user = faux(User)
print(fake_user)
# Output: name='Smith' age=2045 address=Address(street='123 Main St', city='Anytown', zip_code='12345')

# Generate a dictionary with nested address
fake_dict = faux_dict(User)
print(fake_dict)
# Output: {'name': 'Smith', 'age': 2045, 'address': {'street': '123 Main St', 'city': 'Anytown', 'zip_code': '12345'}}

Optional Fields

from typing import Optional
from pydantic import BaseModel
from fauxdantic import faux, faux_dict

class User(BaseModel):
    name: str
    age: Optional[int]
    email: Optional[str]

# Generate a fake user with optional fields
fake_user = faux(User)
print(fake_user)
# Output: name='Smith' age=None email='[e-mail]'

# Generate a dictionary with optional fields
fake_dict = faux_dict(User)
print(fake_dict)
# Output: {'name': 'Smith', 'age': None, 'email': '[e-mail]'}

Lists and Dicts

from typing import List, Dict
from pydantic import BaseModel
from fauxdantic import faux, faux_dict

class User(BaseModel):
    name: str
    tags: List[str]
    preferences: Dict[str, str]

# Generate a fake user with lists and dicts
fake_user = faux(User)
print(fake_user)
# Output: name='Smith' tags=['tag1', 'tag2'] preferences={'key1': 'value1', 'key2': 'value2'}

# Generate a dictionary with lists and dicts
fake_dict = faux_dict(User)
print(fake_dict)
# Output: {'name': 'Smith', 'tags': ['tag1', 'tag2'], 'preferences': {'key1': 'value1', 'key2': 'value2'}}

Custom Values

from pydantic import BaseModel
from fauxdantic import faux, faux_dict

class User(BaseModel):
    name: str
    age: int
    email: str

# Generate a fake user with custom values
fake_user = faux(User, name="John Doe", age=30)
print(fake_user)
# Output: name='John Doe' age=30 email='[e-mail]'

# Generate a dictionary with custom values
fake_dict = faux_dict(User, name="John Doe", age=30)
print(fake_dict)
# Output: {'name': 'John Doe', 'age': 30, 'email': '[e-mail]'}

Enums

from enum import Enum
from pydantic import BaseModel
from fauxdantic import faux, faux_dict

class UserRole(str, Enum):
    ADMIN = "admin"
    USER = "user"
    GUEST = "guest"

class User(BaseModel):
    name: str
    role: UserRole

# Generate a fake user with enum
fake_user = faux(User)
print(fake_user)
# Output: name='Smith' role=<UserRole.ADMIN: 'admin'>

# Generate a dictionary with enum
fake_dict = faux_dict(User)
print(fake_dict)
# Output: {'name': 'Smith', 'role': 'admin'}

Development

# Install dependencies
poetry install

# Run tests
poetry run pytest

# Format code
poetry run black .
poetry run isort .

# Type checking
poetry run mypy .

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.