indian-pincode 1.0.4

High-performance Indian Pincode library with offline-first lookup, validation, and geospatial search

Indian Pincode

The Ultimate High-Performance, Offline-First Indian Pincode Library.

🚀 Why This is Better Than an API

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

📦 Libraries

We provide native, zero-dependency (where possible) libraries for the most popular backend languages.

🐍 Python

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"

🟢 Node.js

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"
});

🔍 Accuracy & Confidence

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

🛠 Contributing

We welcome contributions! Whether it's fixing a bug, adding a feature, or updating the data.

1. Fork the repository.
2. Clone your fork: git clone https://github.com/YOUR_USERNAME/indian-pincode.git
3. Create a Branch: git checkout -b feature/amazing-feature
4. Commit your changes: git commit -m "Add amazing feature"
5. Push to the branch: git push origin feature/amazing-feature
6. Open a Pull Request: Go to the original repository and click "New Pull Request".

Data Updates

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.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📊 Data Source

Data is processed from open government datasets provided by India Post (Department of Posts, Ministry of Communications, Government of India).