indic-faker 0.3.0

pip install indic-faker

For ML/AI dataset export (pandas DataFrame):

pip install indic-faker[ml]

That's it. No cloning, no setup — just install and use.

from indic_faker import IndicFaker

fake = IndicFaker(language="ml")  # Malayalam

# Names — Latin by default, native on demand
fake.name()                    # "Rajesh Krishnan"
fake.name(script="native")    # "രാജേഷ് കൃഷ്ണൻ"

# Indian ID numbers (algorithm-validated)
fake.aadhaar()                 # "3847 2918 4721"  ← Verhoeff checksum ✓
fake.pan()                     # "ABCPK1234F"
fake.gstin()                   # "32ABCPK1234F1Z5" ← Kerala state code ✓

# Contact
fake.phone()                   # "+91 94471 82931"
fake.upi_id()                  # "rajesh.krishnan@okicici"

# Money — Indian comma system (lakhs/crores, not millions)
fake.amount_inr()              # "₹4,29,150.00"
fake.salary_lpa()              # "₹12.5 LPA"

# Address — state-aware with valid pincodes
fake.address()                 # "TC 14/2341, Pettah, TVM - 695024"
fake.messy_address()           # "14/2341, Nr. Temple,Pettah, Trivandrum"

# Company & Career
fake.company_indian()          # "Sharma Technologies Pvt. Ltd."
fake.college()                 # "IIT Bombay"
fake.job_title()               # "Senior Software Engineer"

# Faker pass-through — these just work
fake.ipv4()                    # "192.168.1.1"
fake.text()                    # "Lorem ipsum..."
fake.uuid4()                   # "a1b2c3d4-..."

Every name is available in both native script and Latin transliteration. Default is Latin for database compatibility.

# Switch languages at any time
fake = IndicFaker(language="ta")
fake.name()                       # "Murugan Natarajan"
fake.name(script="native")        # "முருகன் நடராஜன்"

fake.set_language("bn")           # Switch to Bengali mid-session
fake.name(script="native")        # "সৌরভ গাঙ্গুলী"

Generate a consistent, complete identity in a single call. Every field belongs to the same person.

profile = fake.profile()

{
  "name": "Rajesh Krishnan",
  "name_native": "രാജേഷ് കൃഷ്ണൻ",
  "gender": "male",
  "dob": "15/08/1990",
  "age": 35,
  "language": "ml",
  "aadhaar": "3847 2918 4721",
  "pan": "ABCPK1234F",
  "phone": "+91 94471 82931",
  "email": "rajesh.krishnan@gmail.com",
  "address": "TC 14/2341, Pettah, TVM - 695024",
  "city": "Thiruvananthapuram",
  "state": "Kerala",
  "pincode": "695024",
  "bank_account": {"ifsc": "SBIN0001234", "account": "38291847291", "bank": "SBI"},
  "upi_id": "rajesh.krishnan@okicici",
  "employer": "Infosys",
  "job_title": "Software Engineer",
  "salary": "₹12.5 LPA",
  "college": "NIT Calicut",
  "degree": "B.Tech"
}

Pick specific fields only:

fake.profile(fields=["name", "aadhaar", "phone", "email"])

The killer feature for data scientists. Generate thousands of realistic Indian records instantly.

# 🔥 Generate 1000 records as pandas DataFrame
df = fake.to_dataframe(1000)
df.to_csv("indian_test_data.csv", index=False)

# Custom fields only
df = fake.to_dataframe(500, fields=["name", "phone", "city", "salary"])

# JSON output
json_str = fake.to_json(100)

# CSV string
csv_str = fake.to_csv(100)

# Raw list of dicts
records = fake.generate_batch(100)

👀 What to_dataframe(5) looks like:

All generated with correct formats and validated checksums. These pass real-world format validation.

# Aadhaar — Verhoeff checksum validated (not just random 12 digits)
fake.aadhaar()                     # "3847 2918 4721"
fake.aadhaar(formatted=False)      # "384729184721"

# PAN — entity-type aware
fake.pan()                         # "ABCPK1234F" (Person)
fake.pan(entity_type="C")          # "XYZCK5678G" (Company)

# GSTIN — correct state code + modular checksum
fake.gstin()                       # "32ABCPK1234F1Z5"
fake.gstin(state="MH")             # "27..." (Maharashtra)

# Others
fake.dl_number()                   # "KL-09-2020-0012345"
fake.voter_id()                    # "ABC1234567"
fake.passport()                    # "A1234567"

State-aware with valid pincodes, building number formats, and landmarks.

fake.address()             # "14/2341, MG Road, Pettah, TVM - 695024"
fake.full_address()        # "TC 14/2341, MG Road, Near Temple, Pettah, Kerala - 695024"
fake.city()                # "Thiruvananthapuram"
fake.district()            # "Ernakulam"
fake.village()             # "Punalur, Kollam District, Kerala"
fake.pincode()             # "695024" (valid for Kerala)
fake.landmark()            # "Near Government Hospital"

# 🔥 Messy address — simulates real-world Indian user input
fake.messy_address()
# "14/2341, Nr. Temple,Pettah, Trivandrum"
#  ↑ abbreviations  ↑ old city names  ↑ missing pincode

# INR with Indian comma system (lakhs/crores, NOT millions/billions)
fake.amount_inr()                      # "₹4,29,150.00"
fake.amount_inr(100000, 10000000)      # "₹45,82,391.20"

# Banking
fake.bank_account()  # {"ifsc": "SBIN0001234", "account": "38291847291", "bank": "SBI"}
fake.ifsc()                            # "HDFC0001234"
fake.upi_id()                          # "rajesh.k@okicici"
fake.bank_name()                       # "HDFC Bank"
fake.credit_card_indian()              # "4532 1234 5678 9012"

fake.company_indian()    # "Sharma Technologies Pvt. Ltd."
fake.company_type()      # "LLP"
fake.cin()               # "U12345MH2020PLC123456"
fake.gst_invoice()       # "INV/2024-25/001234"

fake.college()                  # "IIT Bombay"
fake.iit()                      # "IIT Madras"
fake.nit()                      # "NIT Trichy"
fake.iim()                      # "IIM Ahmedabad"
fake.medical_college()          # "AIIMS Delhi"
fake.university()               # "Anna University"
fake.degree()                   # "B.Tech"
fake.engineering_branch()       # "Computer Science"
fake.education_record()         # {"college": "BITS Pilani", "degree": "B.Tech", ...}

fake.job_title()                   # "Senior Software Engineer"
fake.employer()                    # "Razorpay"
fake.salary_lpa()                  # "₹18.5 LPA"
fake.salary_lpa(level="fresher")   # "₹5.2 LPA"
fake.salary_lpa(level="cxo")       # "₹180.0 LPA"
fake.salary_monthly()              # "₹1,54,167"
fake.job_record()                  # {"title": ..., "employer": ..., "salary_lpa": ...}

Salary bands: fresher → junior → mid → senior → lead → director → vp → cxo

fake.date_indian()         # "15/08/2024"  (DD/MM/YYYY, not MM/DD)
fake.date_of_birth()       # "23/11/1990"
fake.financial_year()      # "2024-25"     (Indian FY format)

India has massive internal migration. Decouple language from geography:

# Tamil name living in Delhi
fake = IndicFaker(language="ta", state="DL")
fake.name()       # "Murugan Natarajan"   ← Tamil name
fake.address()    # "H.No. 123, Dwarka, Delhi - 110075"  ← Delhi address
fake.gstin()      # "07..."               ← Delhi GST code

fake1 = IndicFaker(seed=42)
fake2 = IndicFaker(seed=42)

assert fake1.name() == fake2.name()       # Always True
assert fake1.aadhaar() == fake2.aadhaar() # Always True