The Ultimate High-Performance, Offline-First Indian Pincode Library.
Most developers rely on external APIs for pincode lookups. This is often slow, unreliable, and subject to rate limits. Indian Pincode solves this by embedding the entire dataset directly into your application with highly optimized indexing.
📦 Package Size Notice: This library is ~40MB (Node.js) and ~10MB (Python) due to the embedded comprehensive database of 19,000+ pincodes and 154,000+ post offices with geospatial data. This library is designed for applications that prioritize 100% uptime, offline capability, and don't want to rely on external APIs. If package size is a critical constraint, consider using an API-based solution instead.
| Feature | External API | Indian Pincode Library |
|---|---|---|
| Latency | 200ms - 1000ms (Network dependent) | < 1ms (In-memory/Local DB) |
| Reliability | Can go down, rate limits | 100% Uptime (It's in your code) |
| Privacy | Sends user location/query to 3rd party | Zero Data Leakage (All local) |
| Cost | Often paid or freemium | Free & Open Source |
| Offline | No | Yes |
We provide native, zero-dependency (where possible) libraries for the most popular backend languages.
Package: indian-pincode
- Backend: SQLite (Embedded, Fast, Robust)
- Installation:
pip install indian-pincode
import indian_pincode as pincode
# 1. Validate a Pincode
print(pincode.validate("110001"))
# Output: True
# 2. Get Details (State, District, Office)
details = pincode.lookup("110001")
print(details[0]['office_name'])
# Output: "Connaught Place SO"
print(details[0]['district'])
# Output: "NEW DELHI"
print(details[0]['state_name'])
# Output: "DELHI"
# 3. Geospatial Search (Find nearby post offices)
# Find offices within 5km of Connaught Place (28.63, 77.21)
nearby = pincode.find_nearby(28.63, 77.21, radius_km=5)
print(nearby[0]['pincode'])
# Output: "110001"Package: @devzoy/indian-pincode
- Backend: Pure JavaScript with Optimized JSON Chunks (Lazy Loaded)
- Installation:
npm install @devzoy/indian-pincode
const pincode = require('indian-pincode');
// 1. Validate
console.log(pincode.validate("560095"));
// Output: true
// 2. Lookup
pincode.lookup("560095").then(details => {
console.log(details[0].office);
// Output: "Koramangala VI Bk SO"
console.log(details[0].district);
// Output: "BANGALORE"
});
// 3. Find Nearby
pincode.findNearby(12.93, 77.62).then(res => {
console.log(res[0].pincode);
// Output: "560095"
});We source our data directly from processed official India Post records. Here are some examples of what you get:
Query: 110001
Result:
- District: NEW DELHI
- State: DELHI
- Offices: Connaught Place SO, Parliament House SO, etc.
Query: 500081
Result:
- District: HYDERABAD
- State: TELANGANA
- Offices: Madhapur SO, Cyberabad SO
Query: 700001
Result:
- District: KOLKATA
- State: WEST BENGAL
- Offices: Kolkata GPO, Lalbazar SO
We welcome contributions! Whether it's fixing a bug, adding a feature, or updating the data.
- Fork the repository.
- Clone your fork:
git clone https://github.com/YOUR_USERNAME/indian-pincode.git - Create a Branch:
git checkout -b feature/amazing-feature - Commit your changes:
git commit -m "Add amazing feature" - Push to the branch:
git push origin feature/amazing-feature - Open a Pull Request: Go to the original repository and click "New Pull Request".
If you find missing or incorrect pincode data, please open an Issue with the details, or submit a PR updating the raw data processing scripts.
This project is licensed under the MIT License - see the LICENSE file for details.
Data is processed from open government datasets provided by India Post (Department of Posts, Ministry of Communications, Government of India).