zipfinder 2.0.3

Offline postal-code / geocode lookup — O(1) & O(log N) indexed, TB/ZB scale ready

zipfinder

Complete offline postal-code / geocode database for Python. Works 100 % offline no internet connection required.

Features

• 1.8 million records — 121 countries, worldwide postal-code dataset
• O(1) lookups — hash-indexed, constant time regardless of dataset size
• O(log N) prefix search — bisect-based, no full-table scans ever
• Spatial radius search — geo-grid index, O(C + K·log K)
• TB / ZB scale — optional SQLite mode for datasets that exceed RAM
• Zero dependencies — Python standard library only
• Fully embedded — all data ships inside the package

Installation

pip install zipfinder

Quick Start

from zip_finder import lookup_zip, lookup_all_zips, search_zip, find_nearby_zips

# Exact lookup  O(1)
record = lookup_zip("94107", country="US")
print(record["city"])          # San Francisco
print(record["latitude"])      # 37.7647

# Lookup without country  returns first match across all countries
record = lookup_zip("94107")

# All countries sharing the same code  O(1)
all_matches = lookup_all_zips("94107")
for r in all_matches:
    print(r["country_code"], r["city"])

# Prefix search by zip or city  O(log N + K)
results = search_zip("Lon", country="GB", limit=5)
for r in results:
    print(r["postal_code"], r["city"])

# Nearby zip codes by coordinates  O(C + Klog K)
nearby = find_nearby_zips(37.7749, -122.4194, radius_km=10, limit=5)
for r in nearby:
    print(r["city"], r["distance_km"], "km")

API Reference

Function	Description	Time Complexity
lookup_zip(code, country=None)	Single zip lookup, returns one record or None	O(1)
lookup_all_zips(code, country=None)	All records for a zip across countries	O(1)
search_zip(query, country=None, limit=10)	Prefix search by zip or city name	O(log N + K)
find_nearby_zips(lat, lon, radius_km=10, limit=10)	Radius search by coordinates	O(C + Klog K)
get_db_stats()	Total records and country count	O(1)
list_countries()	Sorted list of all ISO-3166 country codes	O(1)
get_database(use_sqlite=False)	Access the raw database singleton

Record format

Every returned dict contains:

{
    "postal_code":   "94107",
    "city":          "San Francisco",
    "state":         "California",
    "state_code":    "CA",
    "country_code":  "US",
    "latitude":      37.7647,
    "longitude":     -122.4194,
    "accuracy":      4
}

Results from find_nearby_zips also include a "distance_km" field.

Advanced Usage

Database statistics

from zip_finder import get_db_stats, list_countries

stats = get_db_stats()
print(f"Records : {stats['total_records']:,}")   # 1,826,607
print(f"Countries: {stats['countries']}")        # 121

countries = list_countries()
print(countries[:5])   # ['AD', 'AE', 'AI', 'AL', 'AR']

TB / ZB scale SQLite mode

For datasets that exceed available RAM:

from zip_finder import get_database

db = get_database(use_sqlite=True)
record = db.lookup_zip("94107", country="US")

Function Name Reference

All function names and their descriptions:

Function	Description	Time Complexity
lookup_zip(code, country=None)	Single zip lookup, returns one record or None	O(1)
lookup_all_zips(code, country=None)	All records for a zip across countries	O(1)
search_zip(query, country=None, limit=10)	Prefix search by zip or city name	O(log N + K)
find_nearby_zips(lat, lon, radius_km=10, limit=10)	Radius search by coordinates	O(C + K\u00b7log K)
get_db_stats()	Total records and country count	O(1)
list_countries()	Sorted list of all ISO-3166 country codes	O(1)
get_database(use_sqlite=False)	Access the raw database singleton

Contributing

1. Fork https://github.com/karthikbd/zipfinder
2. Create a branch: git checkout -b feature/my-feature
3. Run tests: python -m pytest tests/ -v
4. Push and open a Pull Request

License

MIT Karthikeyan Balasundaram