electricsheepafrica/analysis_ready_tracks

Spotify-Africa Analysis-Ready Tracks

Dataset Description

Clean subset of 155 tracks from top 30 African artists, perfect for tutorials, demos, and quick analysis with high metadata quality.

Dataset Details

• Language: English
• License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
• Source: Spotify Web API
• Collection Period: 2025
• Geographic Coverage: Africa (5 regions: West, East, Southern, Central, North)

Dataset Structure

Data Fields

This dataset includes comprehensive metadata about African music tracks and artists:

Core Metadata:

• Track identifiers (track_id, track_name, ISRC)
• Artist information (artist_id, artist_name, genres, popularity)
• Album details (album_id, album_name, album_type, release_date)
• Technical specs (duration_ms, explicit, preview_url)

Enrichment Features:

• Regional metadata: country, region inference
• Temporal classifications: release_era, release_decade, track_age_years
• Popularity metrics: popularity_tier, market_scope, region_popularity_percentile
• Collaboration detection: has_collab, collab_count
• Genre consolidation: primary_genre, genre_tags (15+ standardized African genres)
• Streaming analytics: streaming_potential (0-100 score), market_penetration
• Duration categorization: duration_category (Short/Standard/Long/Extended)
• Track features: track_name_length, has_parentheses, track_position

Data Splits

This dataset contains a single split:

• train: All available data

File Formats

• CSV: Human-readable format for general use
• Parquet: Compressed, type-safe format for efficient loading

Usage

Loading the Dataset

from datasets import load_dataset

# Load dataset
dataset = load_dataset("electricsheepafrica/analysis_ready_tracks")
df = dataset['train'].to_pandas()

print(f"Loaded {len(df):,} rows")
print(df.head())

Direct File Access

import pandas as pd

# Load from CSV
df = pd.read_csv("hf://datasets/electricsheepafrica/analysis_ready_tracks/*.csv")

# Load from Parquet (faster)
df = pd.read_parquet("hf://datasets/electricsheepafrica/analysis_ready_tracks/*.parquet")

Dataset Statistics

• Data Quality: 92% metadata completeness
• Deduplication: All tracks deduplicated across sources
• Validation: All Spotify IDs validated
• Coverage: Spans 67 years of African music (1958-2025)

Research Applications

This dataset is ideal for:

• Music Information Retrieval: Genre classification, similarity detection
• Machine Learning: Popularity prediction, streaming success modeling
• Network Analysis: Artist collaboration patterns
• Cultural Studies: African music evolution and globalization
• Market Research: Regional preferences, distribution strategies

Standardized Genres

The dataset includes 15+ consolidated African music genres:

• Afrobeats - Contemporary West African pop fusion
• Amapiano - South African house/jazz hybrid
• Afropop - Pan-African popular music
• Afro House - African electronic dance music
• Highlife - West African guitar-based music
• Bongo Flava - Tanzanian hip-hop/R&B fusion
• Gqom - South African electronic/house
• Kwaito - South African township music
• Gengetone - Kenyan hip-hop fusion
• Afro-Fusion - Contemporary genre-blending
• Azonto - Ghanaian dance music
• Soukous - Central African dance music
• Mbalax - Senegalese pop music
• Afro Drill - African drill rap
• Alte - Alternative Afrobeats

Citation

If you use this dataset in your research, please cite:

@dataset{spotify_africa_analysis_ready_tracks_2025,
  title={Spotify-Africa Analysis-Ready Tracks: African Music Metadata from Spotify},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/electricsheepafrica/analysis_ready_tracks}}
}

License

This dataset is released under CC BY 4.0 (Creative Commons Attribution 4.0 International).

You are free to:

• Share — copy and redistribute the material
• Adapt — remix, transform, and build upon the material
• Use commercially — for any purpose

Under the following terms:

• Attribution — You must give appropriate credit and indicate if changes were made

Collection

This dataset is part of the Spotify-Africa Music Research Collection: https://huggingface.co/collections/electricsheepafrica/spotify-africa-music-research-69038be619ca34d864018cda

Related Datasets

Explore other datasets in the collection:

• master_tracks - Primary unified dataset (recommended)
• tracks - Main enriched tracks
• artist_summary - Artist-level aggregations
• region_summary - Regional statistics
• analysis_ready_tracks - Clean subset for tutorials
• scaled_tracks - Large-scale collection
• popular_tracks - Top hits

Ethical Considerations

• Data Source: All data collected from public Spotify Web API
• Privacy: No user data or listening history included
• Representation: Strives for geographic and genre diversity
• Bias: May reflect Spotify's platform availability and market penetration in Africa
• Cultural Sensitivity: African music genres standardized with respect to local naming

Contact

• Organization: Electric Sheep Africa
• Repository: https://huggingface.co/datasets/electricsheepafrica/analysis_ready_tracks
• Collection: https://huggingface.co/collections/electricsheepafrica/spotify-africa-music-research-69038be619ca34d864018cda

For questions or collaborations, please use the repository discussions or issues.

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