ethnidata 2.0.0

Predict nationality, ethnicity, gender, region, language and religion from names - 238 countries, 5 continents, 415K+ names, enhanced religious coverage

EthniData - Ethnicity and Nationality Prediction

Predict nationality, ethnicity, and demographics from names using a comprehensive global database built from multiple authoritative sources.

🌟 Features

• 190+ Countries - Comprehensive coverage from Wikipedia/Wikidata
• 106 Countries - Enhanced with names-dataset
• 120 Years of Olympic athlete names
• Multiple Sources - Phone directories, census data, public records
• Fast Predictions - SQLite-based for instant lookups
• Normalized Data - Unicode-aware, case-insensitive matching
• Ethnicity Support - Where available in source data
• Simple API - Easy to use Python interface

📊 Data Sources

1. Wikipedia/Wikidata - 190+ countries, biographical data with ethnicity
2. names-dataset - 106 countries, curated name lists
3. Olympics Dataset - 120 years of athlete names (271,116 records)
4. Phone Directories - Public domain name lists from multiple countries
5. Census Data - US Census and other government open data

🚀 Installation

pip install ethnidata

📖 Usage

Basic Usage

from ethnidata import EthniData

# Initialize
ed = EthniData()

# Predict nationality from first name
result = ed.predict_nationality("Ahmet", name_type="first")
print(result)
# {
#   'name': 'ahmet',
#   'country': 'TUR',
#   'country_name': 'Turkey',
#   'confidence': 0.89,
#   'top_countries': [
#     {'country': 'TUR', 'country_name': 'Turkey', 'probability': 0.89},
#     {'country': 'DEU', 'country_name': 'Germany', 'probability': 0.07},
#     ...
#   ]
# }

# Predict from last name
result = ed.predict_nationality("Tanaka", name_type="last")
print(result['country'])  # 'JPN'

# Predict from full name (combines both)
result = ed.predict_full_name("Wei", "Chen")
print(result['country'])  # 'CHN'

# Predict ethnicity (when available)
result = ed.predict_ethnicity("Muhammad", name_type="first")
print(result)
# {
#   'name': 'muhammad',
#   'ethnicity': 'Arab',
#   'country': 'SAU',
#   'country_name': 'Saudi Arabia'
# }

Advanced Usage

# Get top 10 predictions
result = ed.predict_nationality("Maria", name_type="first", top_n=10)

for country in result['top_countries']:
    print(f"{country['country_name']}: {country['probability']:.2%}")
# Spain: 35.4%
# Italy: 28.2%
# Portugal: 15.1%
# ...

# Database statistics
stats = ed.get_stats()
print(stats)
# {
#   'total_first_names': 123456,
#   'total_last_names': 234567,
#   'countries_first': 195,
#   'countries_last': 198
# }

🏗️ Project Structure

ethnidata/
├── ethnidata/                # Main package
│   ├── __init__.py
│   ├── predictor.py          # Core prediction logic
│   └── ethnidata.db          # SQLite database
├── scripts/                  # Data collection scripts
│   ├── 1_fetch_names_dataset.py
│   ├── 2_fetch_wikipedia.py
│   ├── 3_fetch_olympics.py
│   ├── 4_fetch_phone_directories.py
│   ├── 5_merge_all_data.py
│   └── 6_create_database.py
├── tests/                    # Unit tests
├── examples/                 # Example scripts
├── docs/                     # Documentation
├── setup.py
├── pyproject.toml
└── README.md

🔬 Accuracy & Methodology

How it works

1. Name Normalization: Names are lowercased and Unicode-normalized (e.g., "José" → "jose")
2. Database Lookup: Queries SQLite database for matching names
3. Frequency-Based Scoring: Countries are ranked by how often the name appears
4. Probability Calculation: Frequencies are converted to probabilities
5. Full Name Combination: First name (40%) + last name (60%) weights

Limitations

• Bias: Database reflects historical Olympic participation, Wikipedia coverage
• Missing Names: Rare or new names may not be in database
• Ethnicity: Only available where source data included it
• Migration: Doesn't account for diaspora or modern migration patterns
• Multiple Origins: Common names (e.g., "Ali", "Maria") exist in many cultures

🛠️ Development

Build Database from Scratch

git clone https://github.com/teyfikoz/ethnidata.git
cd ethnidata

# Install dependencies
pip install -r requirements.txt

# Fetch all data (takes 10-30 minutes)
cd scripts
python 1_fetch_names_dataset.py
python 2_fetch_wikipedia.py
python 3_fetch_olympics.py
python 4_fetch_phone_directories.py
python 5_merge_all_data.py
python 6_create_database.py

Run Tests

pip install -e ".[dev]"
pytest tests/ -v

📜 License

MIT License - see LICENSE file for details

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

📚 Citations

If you use this database in research, please cite:

@software{ethnidata_2024,
  title = {EthniData: Ethnicity and Nationality Prediction from Names},
  author = {Oz, Tefik Yavuz},
  year = {2024},
  url = {https://github.com/teyfikoz/ethnidata}
}

Data Source Citations

• Olympics Data: Randi Griffin (2018). 120 years of Olympic history. Kaggle
• names-dataset: Philippe Remy (2021). name-dataset
• Wikidata: Wikimedia Foundation. Wikidata

🔗 Related Projects

• ethnicolr - Ethnicity prediction using LSTM
• name-dataset - Name database (106 countries)
• gender-guesser - Gender prediction

📧 Contact

• GitHub Issues: Report bugs or request features
• GitHub: @teyfikoz

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Built with ❤️ using open data