faker-pk 0.1.9

Fake data generator for Pakistani names, CNICs, personal info, address info, job titles, IBAN, Bank names, cities, provinces, castes, sect, network providers, job aware salaries and companies info.

🇵🇰 faker-pk

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faker-pk is a Python library that generates realistic Pakistani data for testing, demos, datasets, and development.
It includes names, CNICs, phone numbers, addresses, bank info, company details, jobs, salaries, and more.
It also provides full support as a Faker provider so you can integrate it directly into the Faker ecosystem. It’s designed for developers who want realistic-looking Pakistani data in their applications or ML datasets.

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Why Use faker-pk?

Developers working with Pakistani applications often struggle with:

• Generating realistic user data
• Testing CNIC and phone formats
• Filling databases with sample business information
• Creating synthetic datasets
• Running demos without exposing real personal data

faker-pk solves this by providing structured, validated, Pakistan-focused fake data.

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📦 Installation

pip install faker-pk

Upgrade:

pip install --upgrade faker-pk

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Quick Usage (FakerPK Class)

from faker_pk import FakerPK

fake = FakerPK()

print(fake.male_name())
print(fake.cnic())
print(fake.phone_number())
print(fake.full_address())
print(fake.company_name())

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🧩 All Attributes & Functions

Below is the full API that faker-pk generates.

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👤 Personal Information

Function	Description	Example
male_name(count=1)	Pakistani male names	"Bilal Khan"
female_name(count=1)	Pakistani female names	"Ayesha Malik"
cnic(count=1)	Valid CNIC format xxxxx-xxxxxxx-x	"35201-6543210-7"
phone_number(count=1)	Pakistani mobile format	[phone]
sim_provider(count=1)	Mobile network providers	"Jazz"
caste(count=1)	Castes used across Pakistan	"Ansari"
sect(count=1)	Religious sects	"Sunni"
dob(count=1)	Random date of birth	"1998-05-14"

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🏠 Address Information

Function	Description	Example
city(count=1)	Cities in Pakistan	"Karachi"
province(count=1)	Pakistani provinces	"Punjab"
full_address(count=1)	Complete Pakistani address	"House No. 45, Street 10, Lahore, Punjab, 54000"

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🏢 Company & Business Information

Function	Description	Example
company_name(count=1)	Random company names	"TechWorks Pvt Ltd"
industry_name(count=1)	Industries in Pakistan	"Telecommunications"
bank_name(count=1)	Pakistani banks	"HBL"
iban(count=1)	Pakistani IBAN format	"PK36HABB0000001234567890"
salary(count=1, industry=industry)	Salary estimates (industry-aware)	95000

Valid industry parameters that are considered:

• IT
• Finance
• Healthcare
• Education
• Marketing
• Government
• Engineering
• Retail
• Entrepreneur
• Consulting

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💼 Job Information

Function	Description	Example
job_title(count=1)	Random job titles	"Software Engineer"
job_title_with_industry(count=1)	Job title with industry	"Finance Analyst – Banking"

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🔁 Generating Multiple Items

fake = FakerPK()
    
fake.city(3)
# ['Lahore', 'Islamabad', 'Multan']

fake.male_name(5)
# ['Ali Khan', 'Usman Raza', 'Zain Qureshi', 'Ahmed Farooq', 'Sami Shah']

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🔌 Faker Provider Integration

faker-pk fully integrates with the Faker library via FakerPKProvider.

➤ Add Provider to Faker

from faker import Faker
from faker_pk import FakerPKProvider

fake = Faker()
fake.add_provider(FakerPKProvider)

print(fake.pk_male_name())
print(fake.pk_cnic())
print(fake.pk_full_address())

➤ Provider Methods

Provider Function	Description
pk_male_name()	Male name
pk_female_name()	Female name
pk_cnic()	CNIC
pk_phone_number()	Pakistani mobile number
pk_sim_provider()	Mobile network
pk_caste()	Caste
pk_sect()	Sect
pk_dob()	DOB
pk_city()	City
pk_province()	Province
pk_full_address()	Complete address
pk_company_name()	Company
pk_industry_name()	Industry
pk_job_title()	Industry-aware job title
pk_job_title_with_industry()	Combined title
pk_salary(industry=industry)	Salary range
pk_bank_name()	Bank
pk_iban()	IBAN

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🧩 API Reference

Function	Description	Example Output
male_name(count=1)	Generates one or more male names	['Ali Khan']
female_name(count=1)	Generates one or more female names	['Ayesha Malik']
cnic(count=1)	Generates valid CNIC numbers	['37405-1234567-8']
phone_number(count=1)	Generates Pakistani phone numbers	['[phone]']
city(count=1)	Returns cities from Pakistan	['Karachi']
province(count=1)	Returns Pakistani provinces	['Sindh']
full_address(count=1)	Returns complete fake addresses	['House No. 23, Street No. 8, Lahore, Punjab, 54000']
company_name(count=1)	Returns random company names	['TechNova Pvt Ltd']

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Example Script

from faker_pk import FakerPK

fake = FakerPK()

for _ in range(3):
    print({
        "Name": fake.male_name(),
        "CNIC": fake.cnic(),
        "Phone": fake.phone_number(),
        "Address": fake.full_address(),
        "Company": fake.company_name(),  
        "Salary": fake.salary(industry="Information Technology")
    })

Output:

{'Name': 'Ali Raza', 'CNIC': '35201-6543210-7', 'Phone': '[phone]', 'Address': 'House No. 12, Street No. 3, Islamabad, Islamabad, 44000', 'Company': 'Techworks'}

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🛠️ Development

Clone the repository and install locally:

git clone https://github.com/Khubaib8281/faker-pk.git
cd faker-pk
pip install -e .

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🤝 Contributing

You can contribute by:

• Adding realistic datasets ( universities, etc.)
• Improving job industries and salaries
• Expanding address and bank coverage
• Adding validation utilities

Pull requests are welcome.

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📄 License

MIT License.

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👤 Author

Muhammad Khubaib Ahmad

📧 [e-mail]
🌐 GitHub: Khubaib8281 🐍 PYPI: Khubaib_01

LinkedIn: https://www.linkedin.com/in/muhammad-khubaib-ahmad-

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⭐ Support

If this project helps you, give it a star on GitHub!

https://github.com/Khubaib8281/faker-pk