Loading...
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.
The `spotipy` Python library wraps the Spotify Web API with OAuth handling. Engineers pull track audio features (danceability, energy, key) in bulk using `spotipy.Spotify.audio_features()` and store them in pandas DataFrames for clustering and recommendation experiments.
Spotify's audio features and playlist data power music-aware AI applications. You can build a RAG system that recommends playlists based on mood descriptions, or fine-tune a model to classify genres from audio feature vectors. The API also enables LLM-based music discovery chatbots.
# pip install spotipy
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(
client_id="YOUR_CLIENT_ID",
client_secret="YOUR_SECRET"
))
features = sp.audio_features(["4uLU6hMCjMI75M1A2tKUQC"])[0]
print("Danceability:", features["danceability"])Official dataset source
More datasets used by Python data engineers.
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.
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.
Retrieve tweets, user profiles, trends, and engagement metrics from the Twitter/X platform via its REST and streaming APIs. Useful for social media analytics pipelines, sentiment analysis, and building real-time data streams with Python using the Tweepy library.