🎭 AutoFaker

A Python library designed to minimize the setup/arrange phase of your unit tests by removing the need to manually write code to create anonymous variables.

Get Started View on GitHub

test_calculator.py

import unittest
from autofaker import autodata

class Calculator:
    def add(self, number1: int, number2: int):
        return number1 + number2

class CalculatorTests(unittest.TestCase):
    @autodata()
    def test_can_add_two_numbers(self, 
                                sut: Calculator, 
                                number1: int, 
                                number2: int):
        result = sut.add(number1, number2)
        self.assertEqual(number1 + number2, result)

Features

🏗️

Built-in Types

Generate anonymous data for int, float, str, datetime, and more built-in Python types.

🎯

Class Support

Create instances of simple classes, dataclasses, nested classes, and enum classes automatically.

🎭

Fake Data

Generate realistic fake data using Faker library for names, addresses, emails, and more.

📊

DataFrame Support

Create Pandas DataFrames with anonymous or fake data for data science testing.

🔗

Nested Objects

Handle complex nested structures and circular references automatically.

📝

Decorators

Use @autodata and @fakedata decorators for clean, annotation-based test setup.

Installation

🐍 PyPI

RECOMMENDED

pip install autofaker

The easiest way to install AutoFaker from the Python Package Index.

🔧 Development

pip install -i https://test.pypi.org/simple/ autofaker

Install the latest development version directly from GitHub.

Quick Start

Basic Usage

AutoFaker provides two main ways to generate test data:

Anonymous data: Random values that satisfy type requirements
Fake data: Realistic data using the Faker library

You can use the Autodata class directly or leverage the @autodata decorator for cleaner test code.

quick_start.py

from autofaker import Autodata

# Create anonymous built-in types
name = Autodata.create(str)
age = Autodata.create(int)
height = Autodata.create(float)

print(f"Name: {name}")
print(f"Age: {age}")
print(f"Height: {height}")

# Create multiple instances
numbers = Autodata.create_many(int, 5)
print(f"Numbers: {numbers}")

Examples

Built-in Types

from autofaker import Autodata
from datetime import datetime

# Create anonymous built-in types
text = Autodata.create(str)
number = Autodata.create(int)
decimal = Autodata.create(float)
date = Autodata.create(datetime)

print(f'String: {text}')
print(f'Integer: {number}')
print(f'Float: {decimal}')
print(f'DateTime: {date}')

DataClasses

from dataclasses import dataclass
from autofaker import Autodata

@dataclass
class Person:
    id: int
    name: str
    age: int
    email: str

# Create an anonymous Person
person = Autodata.create(Person)
print(f'ID: {person.id}')
print(f'Name: {person.name}')
print(f'Age: {person.age}')
print(f'Email: {person.email}')

Fake Data

from dataclasses import dataclass
from autofaker import Autodata

@dataclass
class Employee:
    id: int
    first_name: str
    last_name: str
    email: str
    job: str
    company: str

# Create with realistic fake data
employee = Autodata.create(Employee, use_fake_data=True)
print(f'Name: {employee.first_name} {employee.last_name}')
print(f'Job: {employee.job}')
print(f'Email: {employee.email}')

Pandas DataFrames

from autofaker import Autodata

class SalesData:
    id = -1
    product = ''
    quantity = 0
    price = 0.0

# Create DataFrame with anonymous data
df = Autodata.create_pandas_dataframe(SalesData, rows=5)
print(df)

# Create DataFrame with fake data
df_fake = Autodata.create_pandas_dataframe(SalesData, rows=5, use_fake_data=True)
print(df_fake)

Supported Data Types

Built-in Types

int
float
str
complex
range
bytes
bytearray

Datetime Types

datetime
date

Classes

Simple classes
@dataclass
Nested classes
Classes with lists
Enum classes
typing.Literal

DataFrames

Pandas DataFrame