GildasLeDrogoff/spotify-huge-track-analysis-dataset

Spotify Track Analysis Dataset

General Description

This dataset provides a large-scale, research-oriented analytical representation of Spotify music data.

It is centered on tracks as musical recordings (track_id), while preserving explicit artist attribution as defined by Spotify’s native credit model.

Each row corresponds to a track–artist association, identified by:

• a Spotify track identifier (track_id)
• a credited artist name (artist_name)

A single track may appear on multiple rows when it is associated with multiple artists (collaborations, featurings, compilations, collective works). This duplication is structural and intentional.

All musical characteristics, album metadata, and popularity metrics are strictly invariant for a given track_id. No intra-track variation exists between rows sharing the same track_id.

For size reduction and analytical relevance purposes, only tracks with strictly positive popularity (track_popularity > 0) have been retained. Tracks with no measurable exposure (zero popularity) were excluded upstream.

Granularity and Data Model

The dataset strictly follows the Spotify attribution model and must be interpreted across two distinct levels.

Conceptual Level (Analytical)

• track_id uniquely identifies a musical recording.
• All numerical and descriptive variables are defined and stable at this level.

Physical Level (Dataset Rows)

• Each row corresponds to a unique (track_id, artist_name) pair.
• A track credited to N artists appears on N distinct rows.
• There is a bijective correspondence between:
• the total number of rows
• the number of distinct (track_id, artist_name) tuples

This design choice preserves the full set of artist credits without compromising analytical consistency at the track level.

Dataset Characteristics

• 📂 File: spotify-huge-audio-features.parquet
• 📏 Size: 4096.96 MB
• 🧮 Total number of rows: 56,277,664
• 📊 Columns: 27
• 📦 Row groups: 57
• 🔧 Parquet version: 2.6
• 🏷️ Created by: ClickHouse version 26.1.2

The dataset is designed for batch-oriented analytical engines (DuckDB, Spark, Polars, Arrow, ClickHouse). It is not suitable for transactional or real-time workloads.

Data Schema

Column name	Parquet type	Column name	Parquet type
track_id	BYTE_ARRAY	artist_name	BYTE_ARRAY
track_name	BYTE_ARRAY	album_name	BYTE_ARRAY
album_release_date	INT32	duration_ms	INT32
explicit	INT32	track_number	INT32
disc_number	INT32	track_popularity	INT32
album_popularity	INT32	track_vs_album_popularity	DOUBLE
artist_popularity	INT32	artist_followers	INT64
album_vs_artist_popularity	DOUBLE	tempo	DOUBLE
key	INT32	mode	INT32
danceability	DOUBLE	energy	DOUBLE
loudness	DOUBLE	speechiness	DOUBLE
acousticness	DOUBLE	instrumentalness	DOUBLE
liveness	DOUBLE	valence	DOUBLE
energy_danceability_score	DOUBLE

Field Details

Identifiers and Labels

• Spotify track identifier (track_id)
• Track name
• Album name
• Credited artist name

Popularity and Audience

• Track popularity
• Album popularity
• Artist popularity
• Artist follower count

Popularity metrics are defined by Spotify and represent a single snapshot at the time of dataset construction.

Comparative Popularity Indicators

• Relative popularity of the track compared to the album
• Relative popularity of the album compared to the artist

These indicators enable inter-artist and inter-catalog comparisons, independent of differences in notoriety scale.

Temporal Information

• Album release date

Structural Track Metadata

• Duration (milliseconds)
• Explicit content indicator
• Track number
• Disc number

Musical Attributes

• Tempo (BPM)
• Musical key
• Mode (major / minor)

Spotify Audio Features

• Danceability
• Energy
• Loudness
• Speechiness
• Acousticness
• Instrumentalness
• Liveness
• Valence

Composite Feature

• energy_danceability_score Deterministic score combining energy and danceability, provided for ranking, segmentation, and exploratory analysis.

Intended Use Cases

This dataset is intended for offline analytical workflows, including:

• Large-scale exploratory analysis of musical characteristics
• Popularity-aware clustering and segmentation
• Recommendation modeling based on audio features
• Statistical analyses linking popularity and acoustic attributes
• Comparative studies across artists, albums, and release periods
• Benchmarking of large-scale data processing pipelines

All numerical variables are stable at the track_id level, ensuring analytical consistency and reproducibility.

Limitations

• Popularity metrics are time-dependent and reflect a single snapshot.
• Tracks with “zero” popularity according to Spotify are intentionally excluded.
• Multiple rows may correspond to the same track due to multi-artist credits.