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Access NASA's extensive collection of space data including the Astronomy Picture of the Day, Mars rover photos, near-Earth object tracking, satellite imagery, and Earth observation datasets. Commonly used in scientific data pipelines, geospatial analysis workflows, and educational data engineering projects with Python.
Engineers call the NASA APIs using `requests` with a free API key, iterating through paginated endpoints for APOD, NeoWs, and DONKI. Results are stored in S3 or local parquet files for downstream ML pipelines.
NASA's satellite and planetary datasets are rich training material for computer vision models detecting weather patterns, land use changes, and geological features. You can wire the APOD endpoint into an MCP server so LLMs can retrieve today's astronomy image and generate educational summaries.
# pip install requests
import requests
resp = requests.get(
"https://api.nasa.gov/planetary/apod",
params={"api_key": "YOUR_API_KEY"}
)
apod = resp.json()
print(apod["title"], apod["url"])Official dataset source
More datasets used by Python data engineers.
The Swedish government monopoly liquor store API providing product catalogues, store locations, inventory, and pricing data. Useful for practising structured API ingestion, building retail analytics pipelines, and learning how to work with government-published commercial datasets in Python.
It provides REST access to FoodData Central (FDC). It is intended primarily to assist application developers wishing to incorporate nutrient data into their applications or websites.
Access free weather forecasts, alerts, observations, and radar data from the US National Weather Service. Used in data engineering pipelines for weather-enriched analytics, emergency alert monitoring, agricultural planning dashboards, and integrating official government weather data into Python applications.