Global Metal: Exploratory Data Analysis of 183,000+ Metal Bands

An exploratory data analysis of the Encyclopaedia Metallum (Metal Archives) dataset, examining global trends in heavy metal music across countries, subgenres, and decades.

Questions Explored

• Which countries produce the most metal bands, both total and per capita?
• How have subgenres evolved across the global metal scene?
• What does Southeast Asia's (and Malaysia's) metal scene look like?
• How do band survival rates differ between Malaysia and the global average?

Dataset

• Source: Encyclopaedia Metallum via Kaggle
• Records: 183,394 bands across 157 countries
• Features: Band name, country, genre, status

Tools

Python, pandas, NumPy, matplotlib, seaborn, Jupyter Notebook

Key Findings

Global Distribution

• The United States leads with 41,385 bands, followed by Germany (13,840) and Brazil (8,408).
• Nordic countries punch far above their weight — Finland has 5,469 bands for a population of just 5.5 million.

Genre Landscape

• Death Metal (29,906) and Black Metal (28,300) dominate globally, followed by Thrash Metal (25,643).
• Malaysia mirrors this trend with Death Metal (202) and Black Metal (155) leading, but Thrash Metal (127) holds a proportionally stronger presence.

Southeast Asia

• Indonesia dominates the region with 2,650 bands — more than triple Malaysia's 775.
• Singapore (349) stands out relative to its small population.

Band Status

• 55.2% of bands globally are active, while 30.1% have split up.
• Malaysian bands have a higher active rate (~60%) compared to the global average, suggesting a younger, growing scene.
• Malaysia also shows a higher "Unknown" rate (17% vs 8.5%), likely reflecting underground bands that are harder to track.

Project Structure

metal-eda/
├── data/              # Dataset (not tracked in git)
├── notebooks/         # Jupyter notebook with full analysis
│   └── 01_eda.ipynb
├── outputs/           # Generated charts
├── src/               # Scripts (future use)
├── .gitignore
├── README.md
└── requirements.txt

How to Run

1. Clone the repository: git clone https://github.com/YOUR_USERNAME/metal-eda.git
2. Create a virtual environment: python -m venv venv
3. Install dependencies: pip install -r requirements.txt
4. Download the dataset from Kaggle and place CSV files in data/
5. Open notebooks/01_eda.ipynb in Jupyter Notebook