names-dataset 3.0.3

The python library to handle names

First and Last Names Database

This Python library provides information about names:

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

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

Installation

Available on PyPI:

pip install names-dataset

Usage

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

from names_dataset import NameDataset

# The V3 lib takes time to init (the database is massive). Tip: Put it into the init of your app.
nd = NameDataset()

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']

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).

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

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

Citation

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