faker-pk

faker-pk is a Python package that generates realistic Pakistani data for testing, software demos, synthetic datasets, and application development.

It supports generating localized Pakistani names, CNICs, mobile numbers, network providers, complete addresses, bank info, IBANs, company details, industry-aware job titles, clean salary figures, and educational institutions with student profiles.

It also fully integrates as a Faker Provider so you can seamlessly use it inside the standard faker library ecosystem.

Authors

• Muhammad Khubaib Ahmad (khubaib.ahmad@inference-lab.org) - Original creator of faker-pk
• INFERENCE Lab (contact@inference-lab.org) - Organization

Maintainers

• Ayesha Anwar (hayesha1744@gmail.com) - Lead developer of faker-pk v2.0
• INFERENCE Lab (contact@inference-lab.org) - Project organization

Why Use faker-pk?

Developers building Pakistani software applications often encounter issues with:

• Generating realistic, localized user profiles (CNIC, phone numbers, addresses)
• Validating Pakistani identity formats (CNIC last-digit gender checks)
• Populating development databases with real-world company, industry, and banking data
• Simulating student datasets filtered by city, province, or institution level
• Running software demonstrations safely without exposing real personal data

faker-pk resolves this by utilizing an optimized, local SQLite database backend for reliable and realistic Pakistani data generation.

Installation

pip install faker-pk

To upgrade to the latest version:

pip install --upgrade faker-pk

Quick Start (Standalone FakerPK Class)

from faker_pk import FakerPK

fake = FakerPK()

print("Male Name:", fake.male_name())
print("CNIC:", fake.cnic(gender="male"))
print("Phone Number:", fake.phone_number(provider="Jazz"))
print("Full Address:", fake.full_address(province="Punjab"))
print("Company:", fake.company_name(industry="IT"))
print("Salary (PKR):", fake.salary(industry="IT"))
print("University:", fake.institution(level="university", city="Lahore"))

Complete API Reference

Personal Information

Address Information

Company & Financial Information

Supported Industry Codes & Names:

When filtering company_name(), job_title(), or salary(), you can pass any of the following codes or full names:

• IT (Information Technology)
• Finance (Finance & Banking)
• Healthcare (Healthcare & Pharmaceuticals)
• Education (Education & Academics)
• Marketing (Marketing & Media)
• Government (Government & Public Sector)
• Engineering (Engineering & Manufacturing)
• Retail (Hospitality & Retail)
• Entrepreneur (Entrepreneur & Startups)
• Consulting (Legal & Consulting)
• Art (Art & Entertainment)
• Politics (Politics & Public Policy)
• Agriculture (Agriculture & Farming)
• Services (Domestic & Personal Services)
• Defense (Defense & Public Safety)

Job Information

Education & Student Profiles

Generating Multiple Records

Passing count > 1 returns a list of items:

from faker_pk import FakerPK

fake = FakerPK()

# Generate 3 cities in Sindh
print(fake.city(count=3, province="Sindh"))
# Output: ['Karachi', 'Hyderabad', 'Sukkur']

# Generate 5 realistic student profiles
profiles = fake.student_profile(count=5, level="university")

Standard faker Integration (FakerPKProvider)

You can register FakerPKProvider with Python's standard faker library. All methods are available prefixed with pk_ (or as alias methods):

from faker import Faker
from faker_pk import FakerPKProvider

fake = Faker()
fake.add_provider(FakerPKProvider)

print(fake.pk_male_name())
print(fake.pk_cnic(gender="male"))
print(fake.pk_full_address(province="Punjab"))
print(fake.pk_institution(level="university"))
print(fake.pk_student_profile())

Local Development & Testing

1. Clone the repository:

   git clone https://github.com/Inference-LAB/faker-pk.git
   cd faker-pk
   

2. Install in editable mode:

   pip install -e .
   

3. Run the test suite:

   pytest
   

Contributing

We welcome contributions from the community! Whether you are expanding datasets, adding validation rules, or improving performance, here is how you can help:

How to Contribute:

1. Fork & Clone: Fork Inference-LAB/faker-pk on GitHub and clone your fork locally.
2. Feature Branches: Create a dedicated feature branch for your changes (git checkout -b feat/add-new-dataset).
3. Database Updates: If adding new dataset records, update faker_pk/initialize_db.py so the SQLite database re-seeds cleanly.
4. Write Unit Tests: Add tests under tests/ for any new functions or parameters. Ensure pytest passes with 100% success.
5. Submit a Pull Request: Push your branch and open a PR against main with a clear summary of your changes.

Authors & Contact Info

Muhammad Khubaib Ahmad

• Email: khubaib.ahmad@inference-lab.org
• GitHub: Khubaib8281
• PyPI: Khubaib_01
• LinkedIn: Muhammad Khubaib Ahmad

Ayesha Anwar

• Email: hayesha1744@gmail.com
• GitHub: Ayesha-Anwar607
• LinkedIn: Ayesha Anwar

License

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

Support

If faker-pk was helpful for your project or application, please consider giving the repository a star on GitHub!
https://github.com/Inference-LAB/faker-pk