---
license: cc-by-4.0
task_categories:
- feature-extraction
- tabular-classification
language:
- en
tags:
- music
- african-music
- popular
- hits
- top-tracks
size_categories:
- n<1K
pretty_name: Spotify-Africa Popular Tracks
---

# Spotify-Africa Popular Tracks

## Dataset Description

Top 100 hit tracks representing the most popular and influential African music on Spotify with high popularity scores.

### 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

```python
from datasets import load_dataset

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

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

### Direct File Access

```python
import pandas as pd

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

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

```bibtex
@dataset{spotify_africa_popular_tracks_2025,
  title={Spotify-Africa Popular Tracks: African Music Metadata from Spotify},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/electricsheepafrica/popular_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/popular_tracks
- **Collection:** https://huggingface.co/collections/electricsheepafrica/spotify-africa-music-research-69038be619ca34d864018cda

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

---

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