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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- feature-extraction
+language:
+- en
+tags:
+- music
+- african-music
+- artists
+- curated
+- top-artists
+size_categories:
+- n<1K
+pretty_name: Spotify-Africa Analysis-Ready Artists
+---
+
+# Spotify-Africa Analysis-Ready Artists
+
+## Dataset Description
+
+Top 30 African artist metadata with complete information, ideal for artist-focused research and network analysis.
+
+### 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/analysis_ready_artists")
+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/analysis_ready_artists/*.csv")
+
+# Load from Parquet (faster)
+df = pd.read_parquet("hf://datasets/electricsheepafrica/analysis_ready_artists/*.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_analysis_ready_artists_2025,
+  title={Spotify-Africa Analysis-Ready Artists: African Music Metadata from Spotify},
+  author={Electric Sheep Africa},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/electricsheepafrica/analysis_ready_artists}}
+}
+```
+
+## 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_artists
+- **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.** 🌍🎵