High-precision postal codes, cities, and geographic datasets — ready to integrate into your product in minutes.
Built for production workloads — every record is verified and enriched.
All packages include commercial license, multiple formats, and 12 months of updates.
German postal codes, enriched
All 28,650 German 5-digit postal codes with latitude/longitude, federal state, district, municipality, area in km², and approximate population.
European cities, comprehensive
50,000+ cities across the European Union with population, timezone, elevation, country, region, and classification tier.
Global capitals, extended metadata
All 250 country capitals and major economic centers with extended metadata: ISO codes, languages, currency, dialing code, and timezone.
Tailored regional extracts
Custom extracts from OpenStreetMap for any region: POIs, road networks, administrative boundaries, and land use. Delivered in your format.
Standard formats that drop into your existing stack. No proprietary tools required.
import pandas as pd
# Load the PLZ Premium dataset
df = pd.read_csv('plz_premium.csv')
# Find all postal codes in Bavaria
bavaria = df[df['bundesland'] == 'Bayern']
print(f"PLZ in Bavaria: {len(bavaria)}")
# Closest PLZ to a given coordinate
from geopy.distance import geodesic
munich = (48.1351, 11.5820)
df['distance_km'] = df.apply(
lambda r: geodesic(munich, (r['lat'], r['lng'])).km,
axis=1
)
closest = df.loc[df['distance_km'].idxmin()]
print(f"Closest to Munich: PLZ {closest['plz']} ({closest['distance_km']:.1f} km)")Sample datasets, schema definitions, and helper tools live on GitHub — for everyone, forever.
1,000 sample German postal codes (CC-BY-4.0)
CLI to validate and enrich your own geo datasets
JSON Schemas for all our data packages
Pick a package from our shop or talk to us about a custom extract.