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Datasets:
electricsheepafrica
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analysis_ready_tracks

Tasks:
Feature Extraction
Tabular Classification
Modalities:
Tabular
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parquet
Languages:
English
Size:
< 1K
Tags:
music
african-music
clean
curated
tutorial
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metadata
license: cc-by-4.0
task_categories:
  - feature-extraction
  - tabular-classification
language:
  - en
tags:
  - music
  - african-music
  - clean
  - curated
  - tutorial
size_categories:
  - n<1K
pretty_name: Spotify-Africa 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

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

Explore African Music. Celebrate Diversity. Amplify Voices. 🌍🎵