This Python library provides detailed insights about names, including:
- Popularity (ranking by country)
- Gender prediction
- Country-specific statistics (105 countries supported)
- Fuzzy search (e.g., search for "ISABLE" returns "ISABEL")
- Autocomplete search (e.g., search for names starting with "ISA")
It can give you an answer to some of those questions:
- Who is
Zoe? Likely aFemale, United Kindgom. - Knows
Philippe? Likely aMale, France. And with the spellingPhilipp?Male, Germany. - How about
Nikki? Likely aFemale, United States.
📥 To download the raw CSV data for your analysis, browse here.
730K first names and 983K last names, extracted from the Facebook massive dump (533M users).
Available on PyPI:
pip install names-datasetMemoryError.
Once installed, you can run the following commands to get familiar 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']
print(nd.auto_complete('isa', n=3)) # very fast, can be used in a loop in realtime.
# [{'name': 'Isabel', 'rank': 144}, {'name': 'Isaac', 'rank': 266}, {'name': 'Isa', 'rank': 450}]
print(nd.fuzzy_search('isablel', n=3)) # slow to compute.
# [{'name': 'Isabel', 'rank': 144}, {'name': 'Isabela', 'rank': 1228}, {'name': 'Isabele', 'rank': 2386}]
nd.first_names
# Dictionary of all the first names with their attributes.
nd.last_names
# Dictionary of all the last names with their attributes.🔍 search(name: str)
Searches for a name and returns metadata for both first and last names (if available).
The result is:
country: The probability that the name belongs to a given country. Only the top 10 matching countries are returned.gender: The probability of the person being male or female.rank: The popularity rank of the name in its country (1 = most popular).
📌 Note: Gender data only applies to first names.
🏆 get_top_names(...)
Retrieves the most popular names across supported countries.
Parameters:
n- Number of names to return (per group).gender- 'Male' or 'Female' (only valid for first names).use_first_names- Choose between first names and last names.country_alpha2- 2-letter ISO country code (e.g., 'US', 'JP').
🌍 get_country_codes(alpha_2: bool = False)
Returns a list of supported countries found in the dataset. Country codes are ISO 3166-1 alpha-2 format (e.g., US, FR, JP).
Parameters:
alpha_2- If True, returns 2-letter ISO codes only.
✨ auto_complete(...)
Returns top name suggestions that begin with the specified prefix.
Parameters:
name— Prefix string (e.g., 'Al').n— Max number of results.use_first_names— Use first names if True, else last names.country_alpha2— Filter by country.gender— 'Male' or 'Female' (first names only).
🧠 fuzzy_search(...)
Performs fuzzy matching to suggest similar names.
Parameters:
name— Search term (e.g., 'Jonh').n— Number of close matches to return.use_first_names— Use first names if True, else last names.country_alpha2— Filter by country.gender— 'Male' or 'Female' (first names only).
The dataset is available here name_dataset.zip (3.3GB).
- The data contains 491,655,925 records from 106 countries.
- The uncompressed version takes around 10GB on the disk.
- Each country is in a separate CSV file.
- A CSV file contains rows of this format: first_name,last_name,gender,country_code.
- Each record is a real person.
- For Ruby see names_dataset.
- 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.
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.
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NOTE: It is unfortunately not possible to support more countries because the missing ones were not included in the original dataset.
@misc{NameDataset2021,
author = {Philippe Remy},
title = {Name Dataset},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/philipperemy/name-dataset}},
}