First and Last Names Database

This Python library provides information about names:

• Popularity (rank)
• Country (105 countries are supported)
• Gender

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

• Who is Zoe? Likely a Female, United Kindgom.
• Knows Philippe? Likely a Male, France. And with the spelling Philipp? Male, Germany.
• How about Nikki? Likely a Female, United States.

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 V3 lib takes time to init (the database is massive). Tip: Put it into the init of your app.
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 to all the first names with their attributes.

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

API

The search call provides information about:

• country: The probability of the name belonging to a country. Only the top 10 countries matching the name are returned.

• gender: The probability of the person to be a Male or Female.

• rank: The rank of the name in his country. 1 means the most popular name.

• NOTE: first_name/last_name: the gender does not apply to last_name.

The get_top_names call gives the most popular names:

• n: The number of names to return matching some criteria. Default is 100.
• gender: Filters on Male or Female. Default is None.
• use_first_names: Filters on the first names or last names. Default is True.
• country_alpha2: Filters on the country (e.g. GB is the United Kingdom). Default is None.

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

• alpha_2: Only returns the country codes: 2-char code. Default is False.

Full dataset

The full (curated) dataset containing first, last names along with gender and countries has been uploaded here full.tar.bz2 (2.3G).

The format of this dataset is described here: philipperemy#17 (comment).

License

• This version was generated from the massive Facebook Leak (533M accounts).
• Lists of names are not copyrightable, generally speaking, but if you want to be completely sure you should talk to a lawyer.

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}},
}