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A suite of APIs covering Maps, Places, Geocoding, Distance Matrix, and Directions from Google. Used in data engineering for geocoding address data, calculating logistics routes, enriching datasets with place attributes, and building geospatial pipelines using the googlemaps Python client.
The `googlemaps` Python client library handles authentication and rate limiting. Engineers use it for batch geocoding (address → coordinates), distance matrix calculations across delivery stops, and enriching datasets with nearby place categories.
Google Maps APIs power location-aware AI applications — from route optimization agents to geospatial RAG systems that answer 'Where are the nearest coffee shops to this event venue?' You can expose Places and Geocoding endpoints as MCP tools so LLM agents can reason about physical locations.
# pip install googlemaps
import googlemaps
gmaps = googlemaps.Client(key="YOUR_API_KEY")
result = gmaps.geocode("1600 Amphitheatre Pkwy, Mountain View, CA")
location = result[0]["geometry"]["location"]
print(location["lat"], location["lng"])Official dataset source
More datasets used by Python data engineers.
Access current weather conditions, hourly and 16-day forecasts, historical weather data, and air pollution metrics for any location on Earth. Widely used in Python data engineering pipelines for enriching datasets with weather context, building weather dashboards, and integrating climate signals into machine learning features via the pyowm library.
Access open-source global map data including roads, buildings, points of interest, land use, and administrative boundaries from OpenStreetMap. Used in geospatial data engineering pipelines for routing analysis, map enrichment, address geocoding, and building location-aware datasets with Python using the OSMnx library.
The Mapbox API provides mapping and location-based services for developers to integrate customizable maps, geocoding, routing and navigation features into their applications.