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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.
Python data engineers use the `requests` library or the `pyowm` wrapper to fetch current conditions, 5-day forecasts, and historical data by city name or coordinates. Responses are JSON and map cleanly into pandas DataFrames for time-series analysis.
OpenWeatherMap data feeds AI models that predict energy demand, optimize agricultural irrigation, and personalize travel recommendations. You can build a RAG pipeline that retrieves live weather context to answer natural-language queries like 'Is it a good day to run outdoors in Berlin?'
# pip install requests
import requests
resp = requests.get(
"https://api.openweathermap.org/data/2.5/weather",
params={"q": "London", "units": "metric", "appid": "YOUR_API_KEY"}
)
weather = resp.json()
print(weather["main"]["temp"], "°C")Official dataset source
More datasets used by Python data engineers.
The Mapbox API provides mapping and location-based services for developers to integrate customizable maps, geocoding, routing and navigation features into their applications.
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.
Retrieve real-time and historical air quality measurements including PM2.5, PM10, ozone, NO2, and CO from monitoring stations worldwide. Used in environmental data engineering pipelines for pollution trend analysis, public health analytics, geospatial mapping of air quality, and time-series ingestion in Python.