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Access confirmed exoplanet data collected by NASA's Kepler, K2, and TESS missions, including orbital parameters, stellar properties, and discovery methods. Useful for scientific data pipelines, astronomy datasets, and practising complex query-based API ingestion in Python.
The NASA Exoplanet Archive supports ADQL (SQL-like) queries via its Table Access Protocol (TAP) service. Engineers use `astroquery.nasa_exoplanet_archive` or direct HTTP requests to retrieve planet parameters (mass, radius, orbital period) into pandas DataFrames.
NASA exoplanet data trains ML models for planet detection and habitability classification. RAG systems built on the exoplanet archive let LLMs answer detailed questions like 'How many super-Earths orbit in the habitable zones of their stars?' with verified catalog data.
# pip install requests pandas
import requests, pandas as pd
query = "select pl_name,pl_rade,pl_bmasse,pl_orbper from ps where pl_rade < 2 order by pl_rade"
resp = requests.get(
"https://exoplanetarchive.ipac.caltech.edu/TAP/sync",
params={"query": query, "format": "json"}
)
df = pd.DataFrame(resp.json())
print(df.head(10))Official dataset source
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