Spotify Music Attributes & Artist Metrics

Columns

• name: The title of the song.
• artist: The name of the performing artist.
• spotify_id: The unique identifier for the track on Spotify.
• preview: A URL link to a preview or snippet of the song.
• img: A URL link to the cover art image associated with the track.
• danceability: A value describing how suitable a track is for dancing.
• energy: A perceptual measure of intensity and activity; energetic tracks feel fast, loud, and noisy.
• loudness: The overall loudness of a track in decibels (dB).
• speechiness: A value detecting the presence of spoken words in a track.
• acousticness: Describes whether a track uses only or primarily instruments that produce sound through acoustic means.
• instrumentalness: Predicts whether a track contains no vocals.
• liveness: Detects the presence of an audience in the recording.
• valence: Describes the musical positiveness of a track.
• acousticness_artist: Aggregate acousticness metric associated with the artist.
• danceability_artist: Aggregate danceability metric associated with the artist.
• energy_artist: Aggregate energy metric associated with the artist.
• instrumentalness_artist: Aggregate instrumentalness metric associated with the artist.
• liveness_artist: Aggregate liveness metric associated with the artist.
• speechiness_artist: Aggregate speechiness metric associated with the artist.
• valence_artist: Aggregate valence metric associated with the artist.

Distribution

• Format: .csv
• Size: 20.43 MB
• Rows: 65,800 records (approximate valid count)
• Columns: 20
• Structure: Tabular data containing unique values for songs and artists, with 100% valid entries for key metrics like danceability and energy.

Usage

• Music Analysis: Deep dive into audio features to understand trends across decades or genres.
• Recommendation Systems: Build models to suggest songs based on valence, energy, or danceability.
• Data Visualization: Create visual representations of the musical landscape, correlating loudness with energy or other variables.
• Academic Research: Utilise the dataset for studies regarding musical structure, pop culture trends, or audio processing.

Coverage

• Language: English songs.
• Scope: Spans diverse genres and decades.
• Content: Contains over 56,000 unique song names and 13,000 unique artists.

License

Who Can Use It

• Music Aficionados: Those wishing to explore the data behind the songs they love.
• Data Enthusiasts: Individuals looking for a rich, clean dataset for practice or portfolio projects.
• Researchers: Academics studying musicology, data science, or audio features.

Dataset Name Suggestions

1. 65K English Songs: Spotify Audio Features
2. Spotify Music Attributes & Artist Metrics
3. English Tracks Audio Analysis Database
4. The English Music Audio Feature Collection

Attributes