pip install bharatutils

😩 The problem every Indian developer knows

# Without bharatutils — written for the 100th time, breaks on NaN
df["salary_display"] = df["salary"].apply(
    lambda x: f"₹{round(x/100000, 2)} L" if x >= 100000 else f"₹{x:,}"
)

# With bharatutils — one import, handles everything
from bharatutils import format_inr
df["salary_display"] = df["salary"].apply(format_inr)

Western libraries format millions and billions, validate US Social Security numbers, and know when Thanksgiving falls. bharatutils speaks lakh and crore, validates GSTIN with real checksums, reads addresses with landmarks instead of street numbers, and knows when Diwali is.

✨ Features

💰 Indian number formatting

from bharatutils import format_inr, to_lakh, to_crore

format_inr(1500000)        # '₹15.0 L'
format_inr(50000000)       # '₹5.0 Cr'
format_inr("15,00,000")    # '₹15.0 L'   — messy strings? handled
format_inr(-750000)        # '-₹7.5 L'   — negatives? handled
format_inr(float("nan"))   # 'N/A'       — pandas NaN? handled
indian_commas(15000000)    # '1,50,00,000' — full Indian grouping

Drop it straight into a pandas pipeline on 100,000 dirty rows. It won't flinch.

🧾 GST validation — with the real checksum

from bharatutils import validate_gstin_strict, parse_gstin

validate_gstin_strict("27AAAPZ2318J1ZI")   # True — verifies check digit
validate_gstin_strict("27AAAPZ2319J1ZI")   # False — catches the typo!

parse_gstin("27AAAPZ2318J1ZI")
# {'state': 'Maharashtra', 'pan': 'AAAPZ2318J', 'entity_number': '1', ...}

Most validators only check the format pattern. bharatutils implements the official mod-36 check-digit algorithm — a single wrong character fails validation, exactly as it should.

🪪 PAN validation + holder type decoding

from bharatutils import parse_pan

parse_pan("AAAPZ2318J")
# {'holder_type': 'Individual', 'name_initial': 'Z', 'is_individual': True}

The 4th character of every PAN hides its meaning: P person, C company, T trust, G government — decoded for you.

📍 Indian address parsing

from bharatutils import parse_address

parse_address("Flat 302, Shree Krishna Apts, Nr. SBI ATM, MG Road, Pune - 411001")
# {'pincode': '411001', 'state': 'Maharashtra', 'city': 'Pune'}

Landmarks, flat numbers, "Nr. SBI ATM" — none of it confuses the parser. Space-broken pincodes (700 016) get normalized. Phone numbers don't fool it. It always returns a dict, never crashes.

🪔 One calendar for all of Bharat

from bharatutils import get_festivals, next_festival, days_until

next_festival()          # {'name': 'Muharram', 'date': datetime.date(2026, 6, 26)}
days_until("Diwali")     # 150
get_festivals(2026)      # every festival of the year, sorted by date

Holi and Eid. Christmas and Guru Nanak Jayanti. Buddha Purnima and Diwali. One calendar, every celebration — the way India actually lives. Dates for 2023–2027, verified against official Government of India holiday lists.

📖 Quick reference

🗺️ Roadmap

• v0.1 — five core modules, published
• v0.1.2 — festival data 2023–2027, Buddha Purnima added
• v0.2 — exact pincode→district lookup, pandas accessor (df["col"].india.to_lakh())
• v0.3 — IFSC validation, vehicle registration parsing, regional festival calendars

Honest note: pincode→state mapping is currently prefix-based (~95% accurate). Exact lookup is the headline feature of v0.2.

🤝 Contributing

Found a bug? Wrong festival date? Missing your state's festivals? Open an issue — responses are fast. PRs welcome.

📄 License

MIT — use it anywhere, build anything, just keep the name on it.