python-mockpy 1.0

Comprehensive realistic data generation library for testing and development

MockPy

🚀 Comprehensive Realistic Data Generation Library

MockPy is a Python library for generating realistic and comprehensive mock data for testing, development, and demonstration purposes.

🌟 Features

• 📊 Realistic Data: Based on real-world distributions
• 🔄 Cross-Data Consistency: Related data fields maintain logical relationships
• 🧩 Easy-to-Use API: Intuitive design for developer productivity
• 🔌 Framework Integration: Works with Django, Flask, FastAPI, SQLAlchemy
• 🚀 Performance Optimized: Fast generation with minimal resource usage
• 🌍 Multi-Language Support: Built-in support for English and Turkish with translation capabilities

📦 Installation

pip install python-mockpy

🚀 Quick Start

from mockpy import MockPy

# Initialize the library
mock = MockPy(locale="en_US")

# Generate a person
person = mock.person.person()
print(f"Hello, my name is {person.full_name}")

# Generate an address
address = mock.address.address()
print(f"My address: {address.street}, {address.city}")

# Generate credit card
credit_card = mock.finance.credit_card()
print(f"My card: {credit_card.formatted_number}")

📋 Data Types

MockPy can generate a wide variety of data:

• 👤 Personal Information: Names, addresses, phone numbers, emails
• 💼 Business Data: Company names, job titles, departments
• 💰 Financial Data: Bank accounts, credit cards, IBAN numbers
• 🌐 Internet: URLs, IP addresses, usernames
• 🏥 Healthcare: Patient records, diagnoses, medication names
• 🗺️ Geographic: GPS coordinates, countries, cities
• 🛒 E-commerce: Products, prices, orders
• 📚 Education: Schools, courses, grades

And many more!

🛠️ Advanced Usage

Schema-Based Data Generation

schema = {
    "id": {"type": "integer", "min": 1000, "max": 9999},
    "user": "person.person",
    "subscription": {
        "plan": {"type": "choice", "choices": ["Basic", "Premium", "Enterprise"]},
        "start_date": {"type": "date", "start": "2022-01-01", "end": "2023-01-01"},
        "price": {"type": "float", "min": 9.99, "max": 99.99, "precision": 2},
    }
}

# Generate 100 records
dataset = mock.generate_dataset(schema, count=100)

JSON Schema/OpenAPI Integration

# Generate data from JSON Schema
from mockpy.integrations import from_json_schema

schema_file = "user_schema.json"
users = from_json_schema(schema_file, count=50)

Django Integration

from mockpy.integrations.django import generate_model_instances
from myapp.models import User

# Create 10 instances of your Django model
users = generate_model_instances(User, count=10)

🌍 Multi-Language Support

# English data generation
en_mock = MockPy(locale="en_US")
en_person = en_mock.person.person()

# Turkish data generation
tr_mock = MockPy(locale="tr_TR")
tr_person = tr_mock.person.person()

# Other languages (requires 'translators' package)
fr_mock = MockPy(locale="fr_FR")
fr_person = fr_mock.person.person()

🧪 Testing Use Cases

def test_user_registration():
    mock = MockPy()
    test_user = mock.person.person()
    
    response = client.post('/register', data={
        'email': test_user.email,
        'name': test_user.full_name,
        'phone': test_user.phone
    })
    
    assert response.status_code == 200

🔧 Performance Considerations

MockPy is designed to be fast and resource-efficient:

• No external dependencies required for core functionality
• Minimal memory footprint
• Optimized for bulk data generation
• Smart caching for frequently used data

🤝 Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.

💬 Contact

Project Link: https://github.com/burakozcn01/mockpy