Django Fake Filler

A Django application for automatically generating fake/dummy data for your Django models using the Faker library.

The Story

This project was born from a very practical problem encountered while testing Django applications.

When generating test data, related objects were often recreated every time they were needed. Creating a book would automatically create a new author, even when an author already existed. Over time, this led to unrealistic databases filled with duplicated data and relationships that don’t reflect how real systems work

We wanted to create a tool that generates data mirroring real-world patterns.

Built With Gratitude

This project stands on the shoulders of giants. Special thanks to:

Faker by Daniele Faraglia - The incredible Python library that powers all our fake data generation. Without Faker's comprehensive providers and localization support, this project wouldn't exist. 🙏

Features

• 🎯 Automatic Data Generation: Intelligently generates fake data based on field names and types
• 🔗 Relationship Support: Handles ForeignKey, OneToOne, and ManyToMany relationships
• 🎨 Field Mapping: Smart field name detection (email, username, phone, etc.)
• ⚙️ Customizable: Configure field mappings and excluded models
• 📊 Progress Tracking: Built-in progress bars with tqdm
• 🛡️ Safe Unique Fields: Handles unique constraints gracefully

Installation

Install from PyPI:

pip install django_model_populator

Add to your Django project's INSTALLED_APPS:

INSTALLED_APPS = [
    # ... other apps
    'model_populator',
]

Quick Start

Generate Fake Data for All Models

python manage.py populate --all --num 10

Generate Data for Specific App

python manage.py populate myapp --num 20

Generate Data for Specific Models

python manage.py populate myapp --models Author Book --num 15

Control ManyToMany Relationships

python manage.py populate myapp --num 10 --m2m 5

Usage Examples

Basic Example

# Generate 10 fake records for all models in the "books" app
python manage.py populate books --num 10

Multiple Apps

# Generate fake data for multiple apps
python manage.py populate books authors publishers --num 25

Specific Models

# Generate fake data only for Author and Book models
python manage.py populate books --models Author Book --num 50

With ManyToMany Relations

# Generate 20 records with 5 related objects for each ManyToMany field
python manage.py populate books --num 20 --m2m 5

Command Options

Option	Description	Default
apps	App labels to fill with fake data	All apps
--all	Generate fake data for all models	False
--models	Specific model names to fill	All models in app
--num	Number of objects to generate	1
--m2m	Number of related objects for ManyToMany fields	1

Field Mapping

Django Fake Filler intelligently maps field names to appropriate Faker methods:

Supported Field Name Patterns

• Email: email, email_address, user_email
• Names: first_name, last_name, full_name, name
• Phone: phone, phone_number, mobile
• Address: address, street, city, country, zipcode
• Company: company, company_name
• URLs: url, website, domain
• Text: title, description, bio, summary
• Dates: date, birth_date, created_at
• And many more...

Field Type Mapping

The package also maps Django field types to appropriate fake data:

• CharField → Random text
• EmailField → Valid email addresses
• IntegerField → Random integers
• DecimalField → Random decimal numbers
• DateField → Random dates
• DateTimeField → Random datetime values
• BooleanField → Random boolean values
• URLField → Valid URLs
• TextField → Paragraphs of lorem ipsum
• SlugField → URL-safe slugs

Configuration

You can customize the fake data generation by modifying model_populator/field_mappings.py:

Control Related Object Creation

The Smart Default - Reuses existing related objects instead of creating duplicates:

# In your Django settings.py
AUTO_CREATE_RELATED_MODELS = True  # Default behavior

# When generating 100 books:
# - Creates a few authors/publishers first
# - Reuses them across books (realistic!)
# - Result: 100 books share ~20 authors and ~15 publishers

This is what makes our data realistic - just like in the real world where Stephen King has written dozens of books, not one book per author!

Exclude Apps or Models

EXCLUDED_APPS = ['admin', 'contenttypes', 'auth', 'sessions']
EXCLUDED_MODELS = ['LogEntry', 'Permission']

Custom Field Mappings

FIELD_NAME_MAPPING = {
    'custom_field': 'faker_method',
    # Add your custom mappings
}

Requirements

• Python >= 3.8
• Django >= 3.2
• Faker >= 37.0
• tqdm >= 4.67

Use Cases

• Development: Quickly populate development databases with realistic test data
• Testing: Generate test fixtures for unit and integration tests
• Demos: Create convincing demo data for presentations
• Prototyping: Speed up prototyping with ready-made sample data

How It Works

1. Scans your Django models and their fields
2. Matches field names and types to appropriate Faker methods
3. Handles foreign key relationships by creating related objects
4. Respects unique constraints and validation rules
5. Generates data with progress tracking

Examples

E-commerce Project

# Generate products, categories, and orders
python manage.py populate shop --models Category Product Order --num 100 --m2m 3

Blog Application

# Generate authors, posts, and comments
python manage.py populate blog --num 50

User Management

# Generate user profiles with related data
python manage.py populate accounts --models Profile --num 200

Troubleshooting

Unique Constraint Errors

The package handles unique fields automatically, but if you encounter issues:

• Reduce the --num parameter
• Check your model's unique constraints
• The package will skip records that violate unique constraints

Foreign Key Issues

If related objects aren't being created:

• Ensure AUTO_CREATE_RELATED_MODELS = True in settings
• Check that related models aren't in EXCLUDED_MODELS

Contributing

We welcome contributions! Please see our Contributing Guide for details on:

• Setting up the development environment
• Running tests
• Submitting pull requests
• Code style guidelines

💡 Fast Setup: We recommend using uv for 10-100x faster package installation. See our uv Guide for details.

Quick Start for Contributors

# Clone the repository
git clone https://github.com/YOUR_ORG/django_model_populator.git
cd django_model_populator

# Set up development environment (choose one):

# Option 1: Using uv (fastest - recommended)
uv venv && source .venv/bin/activate
uv pip install -r requirements.txt

# Option 2: Using venv
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# Run migrations and tests
python manage.py migrate
python manage.py test

# Try the example app
python manage.py populate books --num 10

See TESTING.md for comprehensive testing documentation. Please feel free to submit a Pull Request.

Credits & Acknowledgments

This project would not be possible without:

Faker

The amazing Python library by Daniele Faraglia and contributors that powers all our fake data generation. Faker provides hundreds of data providers across multiple locales, making realistic test data generation a breeze.

• Repository: https://github.com/joke2k/faker
• Documentation: https://faker.readthedocs.io/
• License: MIT

Thank you to the Faker team for creating such a versatile and well-maintained library! 🎉

Community

Thanks to all contributors who help improve this project and make Django development easier for everyone.

License

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

Author

Mathias AG mathiasag07@gmail.com

Links

• PyPI: https://pypi.org/project/django_model_populator/
• GitHub: https://github.com/mathiasag7/django_model_populator
• Issues: https://github.com/mathiasag7/django_model_populator/issues

Changelog

See CHANGELOG.md for version history.