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The Python library for names.
| Date | Stars |
|---|---|
| 2026-07-24 | 1010 |
| 2026-07-25 | 1010 |
| 2026-07-28 | 1010 |
| 2026-07-30 | 1010 |
| 2026-08-06 | 1010 |
Today
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Momentum
0.0
growth rate 0.00%/day
# First and Last Name Database
[](https://pepy.tech/project/names-dataset)
[](https://pepy.tech/project/names-dataset/month)
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 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`.
📥 To download the raw CSV data for your analysis, browse [here](#full-dataset).
## Composition
730K first names and 983K last names, extracted from the [Facebook massive dump (533M users)](https://www.theguardian.com/technology/2021/apr/03/500-million-facebook-users-website-hackers).
## Installation
Available on *[PyPI](https://pypi.org/project/names-dataset/)*:
```bash
pip install names-dataset
```
## Usage
⚠️ Note: This library requires approximately 3.2 GB of RAM to load the full dataset into memory. Make sure your system has enough available memory to avoid `MemoryError`.
Once installed, you can run the following commands to get familiar with the library:
```python
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, 'Excerpt of 10,745 characters
Read on GitHub
Philippe Rémy · Imperial College London
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a1d13079f42ed798, topic:dataset, name:dataset, readme:dataset
matched fp:a1d13079f42ed798, topic:named-entity-recognition