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First and Last Names Database

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To download the raw CSV data for your analysis, browse here.

This Python library provides information about names:

It can give you an answer to some of those questions:

Composition

730K first names and 983K last names, extracted from the Facebook massive dump (533M users).

Installation

Available on PyPI:

pip install names-dataset

Usage

NOTE: The library requires 3.2GB of RAM to load the full dataset in memory. Make sure you have enough RAM to avoid MemoryError.

Once it's installed, run those commands to familiarize yourself with the library:

from names_dataset import NameDataset, NameWrapper

# The library takes time to initialize because the database is massive. A tip is to include its initialization in your app's startup process.
nd = NameDataset()

print(NameWrapper(nd.search('Philippe')).describe)
# Male, France

print(NameWrapper(nd.search('Zoe')).describe)
# Female, United Kingdom

print(nd.search('Walter'))
# {'first_name': {'country': {'Argentina': 0.062, 'Austria': 0.037, 'Bolivia, Plurinational State of': 0.042, 'Colombia': 0.096, 'Germany': 0.044, 'Italy': 0.295, 'Peru': 0.185, 'United States': 0.159, 'Uruguay': 0.036, 'South Africa': 0.043}, 'gender': {'Female': 0.007, 'Male': 0.993}, 'rank': {'Argentina': 37, 'Austria': 34, 'Bolivia, Plurinational State of': 67, 'Colombia': 250, 'Germany': 214, 'Italy': 193, 'Peru': 27, 'United States': 317, 'Uruguay': 44, 'South Africa': 388}}, 'last_name': {'country': {'Austria': 0.036, 'Brazil': 0.039, 'Switzerland': 0.032, 'Germany': 0.299, 'France': 0.121, 'United Kingdom': 0.048, 'Italy': 0.09, 'Nigeria': 0.078, 'United States': 0.172, 'South Africa': 0.085}, 'gender': {}, 'rank': {'Austria': 106, 'Brazil': 805, 'Switzerland': 140, 'Germany': 39, 'France': 625, 'United Kingdom': 1823, 'Italy': 3564, 'Nigeria': 926, 'United States': 1210, 'South Africa': 1169}}}

print(nd.search('White'))
# {'first_name': {'country': {'United Arab Emirates': 0.044, 'Egypt': 0.294, 'France': 0.061, 'Hong Kong': 0.05, 'Iraq': 0.094, 'Italy': 0.117, 'Malaysia': 0.133, 'Saudi Arabia': 0.089, 'Taiwan, Province of China': 0.044, 'United States': 0.072}, 'gender': {'Female': 0.519, 'Male': 0.481}, 'rank': {'Taiwan, Province of China': 6940, 'United Arab Emirates': None, 'Egypt': None, 'France': None, 'Hong Kong': None, 'Iraq': None, 'Italy': None, 'Malaysia': None, 'Saudi Arabia': None, 'United States': None}}, 'last_name': {'country': {'Canada': 0.035, 'France': 0.016, 'United Kingdom': 0.296, 'Ireland': 0.028, 'Iraq': 0.016, 'Italy': 0.02, 'Jamaica': 0.017, 'Nigeria': 0.031, 'United States': 0.5, 'South Africa': 0.04}, 'gender': {}, 'rank': {'Canada': 46, 'France': 1041, 'United Kingdom': 18, 'Ireland': 66, 'Iraq': 1307, 'Italy': 2778, 'Jamaica': 35, 'Nigeria': 425, 'United States': 47, 'South Africa': 416}}}

print(nd.search('محمد'))
# {'first_name': {'country': {'Algeria': 0.018, 'Egypt': 0.441, 'Iraq': 0.12, 'Jordan': 0.027, 'Libya': 0.035, 'Saudi Arabia': 0.154, 'Sudan': 0.07, 'Syrian Arab Republic': 0.062, 'Turkey': 0.022, 'Yemen': 0.051}, 'gender': {'Female': 0.035, 'Male': 0.965}, 'rank': {'Algeria': 4, 'Egypt': 1, 'Iraq': 2, 'Jordan': 1, 'Libya': 1, 'Saudi Arabia': 1, 'Sudan': 1, 'Syrian Arab Republic': 1, 'Turkey': 18, 'Yemen': 1}}, 'last_name': {'country': {'Egypt': 0.453, 'Iraq': 0.096, 'Jordan': 0.015, 'Libya': 0.043, 'Palestine, State of': 0.016, 'Saudi Arabia': 0.118, 'Sudan': 0.146, 'Syrian Arab Republic': 0.058, 'Turkey': 0.017, 'Yemen': 0.037}, 'gender': {}, 'rank': {'Egypt': 2, 'Iraq': 3, 'Jordan': 1, 'Libya': 1, 'Palestine, State of': 1, 'Saudi Arabia': 3, 'Sudan': 1, 'Syrian Arab Republic': 2, 'Turkey': 44, 'Yemen': 1}}}

print(nd.get_top_names(n=10, gender='Male', country_alpha2='US'))
# {'US': {'M': ['Jose', 'David', 'Michael', 'John', 'Juan', 'Carlos', 'Luis', 'Chris', 'Alex', 'Daniel']}}

print(nd.get_top_names(n=5, country_alpha2='ES'))
# {'ES': {'M': ['Jose', 'Antonio', 'Juan', 'Manuel', 'David'], 'F': ['Maria', 'Ana', 'Carmen', 'Laura', 'Isabel']}}

print(nd.get_country_codes(alpha_2=True))
# ['AE', 'AF', 'AL', 'AO', 'AR', 'AT', 'AZ', 'BD', 'BE', 'BF', 'BG', 'BH', 'BI', 'BN', 'BO', 'BR', 'BW', 'CA', 'CH', 'CL', 'CM', 'CN', 'CO', 'CR', 'CY', 'CZ', 'DE', 'DJ', 'DK', 'DZ', 'EC', 'EE', 'EG', 'ES', 'ET', 'FI', 'FJ', 'FR', 'GB', 'GE', 'GH', 'GR', 'GT', 'HK', 'HN', 'HR', 'HT', 'HU', 'ID', 'IE', 'IL', 'IN', 'IQ', 'IR', 'IS', 'IT', 'JM', 'JO', 'JP', 'KH', 'KR', 'KW', 'KZ', 'LB', 'LT', 'LU', 'LY', 'MA', 'MD', 'MO', 'MT', 'MU', 'MV', 'MX', 'MY', 'NA', 'NG', 'NL', 'NO', 'OM', 'PA', 'PE', 'PH', 'PL', 'PR', 'PS', 'PT', 'QA', 'RS', 'RU', 'SA', 'SD', 'SE', 'SG', 'SI', 'SV', 'SY', 'TM', 'TN', 'TR', 'TW', 'US', 'UY', 'YE', 'ZA']

nd.first_names
# Dictionary of all the first names with their attributes.

nd.last_names
# Dictionary of all the last names with their attributes.

API

The search call provides information about:

The get_top_names call gives the most popular names:

The get_country_codes returns the supported country codes (or full pycountry objects).

Full dataset

The dataset is available here name_dataset.zip (3.3GB).

<img width="284" alt="image" src="https://user-images.githubusercontent.com/4516927/220814570-85340302-4c49-4648-b1c8-dedebd0e570b.png">

License

Countries

Afghanistan, Albania, Algeria, Angola, Argentina, Austria, Azerbaijan, Bahrain, Bangladesh, Belgium, Bolivia, Plurinational State of, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Chile, China, Colombia, Costa Rica, Croatia, Cyprus, Czechia, Denmark, Djibouti, Ecuador, Egypt, El Salvador, Estonia, Ethiopia, Fiji, Finland, France, Georgia, Germany, Ghana, Greece, Guatemala, Haiti, Honduras, Hong Kong, Hungary, Iceland, India, Indonesia, Iran, Islamic Republic of, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Korea, Republic of, Kuwait, Lebanon, Libya, Lithuania, Luxembourg, Macao, Malaysia, Maldives, Malta, Mauritius, Mexico, Moldova, Republic of, Morocco, Namibia, Netherlands, Nigeria, Norway, Oman, Palestine, State of, Panama, Peru, Philippines, Poland, Portugal, Puerto Rico, Qatar, Russian Federation, Saudi Arabia, Serbia, Singapore, Slovenia, South Africa, Spain, Sudan, Sweden, Switzerland, Syrian Arab Republic, Taiwan, Province of China, Tunisia, Turkey, Turkmenistan, United Arab Emirates, United Kingdom, United States, Uruguay, Yemen.

🇲🇹🇪🇬🇧🇴🇳🇦🇹🇳🇷🇸🇯🇲🇦🇷🇯🇵🇰🇿🇸🇦🇺🇸🇦🇪🇭🇺🇭🇰🇶🇦🇸🇬🇩🇪🇾🇪🇲🇾🇭🇹🇵🇷🇨🇳🇦🇴🇹🇼🇸🇩🇧🇭🇧🇪🇪🇹🇪🇪🇨🇴🇬🇷🇧🇷🇷🇺🇱🇾🇸🇻🇰🇼🇰🇷🇦🇱🇸🇾🇧🇫🇨🇿🇨🇦🇴🇲🇩🇰🇨🇱🇧🇩🇧🇼🇫🇯🇮🇶🇮🇪🇿🇦🇨🇷🇯🇴🇰🇭🇵🇪🇺🇾🇮🇷🇲🇩🇫🇷🇲🇴🇳🇱🇬🇭🇨🇾🇩🇿🇮🇹🇬🇧🇧🇮🇮🇳🇫🇮🇦🇫🇵🇭🇦🇿🇬🇪🇨🇲🇮🇱🇪🇸🇱🇹🇩🇯🇬🇹🇱🇺🇵🇸🇹🇷🇵🇱🇮🇸🇳🇬🇵🇦🇭🇷🇸🇮🇭🇳🇦🇹🇲🇺🇸🇪🇲🇦🇨🇭🇧🇳🇲🇻🇳🇴🇪🇨🇮🇩🇧🇬🇵🇹🇲🇽🇱🇧🇹🇲

NOTE: It is unfortunately not possible to support more countries because the missing ones were not included in the original dataset.

Citation

@misc{NameDataset2021,
  author = {Philippe Remy},
  title = {Name Dataset},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/philipperemy/name-dataset}},
}