A lightweight REST API that returns random facts and trivia about cats. Useful for learning API integration, testing HTTP client libraries in Python, and building practice ETL pipelines before connecting to more complex data sources.
A single `requests.get()` call retrieves random cat facts as JSON. Engineers often use this API in tutorials to demonstrate basic API integration, response parsing, and storing results in a simple SQLite database or CSV file.
Cat Facts API is a lightweight sandbox for AI experiments — perfect for testing RAG retrieval pipelines, prompt chaining, or MCP server setups without dealing with authentication or rate limits. Build a cat facts chatbot as your first LLM application with zero friction.
# pip install requests
import requests
resp = requests.get("https://catfact.ninja/facts", params={"limit": 5})
for fact in resp.json()["data"]:
print("-", fact["fact"])Official dataset source
More datasets used by Python data engineers.
Access YouTube video metadata, channel statistics, playlist data, comments, captions, and trending content. Used in data pipelines for social media analytics, content trend monitoring, comment sentiment analysis, and building video performance dashboards using the Google API Python client.
Access music metadata, audio features (tempo, energy, danceability), playlist data, artist catalogues, and listening history from the Spotify platform. Used in data engineering for building music recommendation systems, audio feature datasets, and trend analysis pipelines with the spotipy Python library.
Provides structured data about Breaking Bad characters, episodes, quotes, and deaths. A clean, well-documented REST API commonly used to practise JSON ingestion, relational data modelling, and building small ETL pipelines in Python before working with larger production data sources.