Mimesis: The Fake Data Generator
Documentation: https://mimesis.name/
Mimesis (/mɪˈmiːsɪs) is a robust data generator for Python that can produce a wide range of fake data in various languages.
The key features are:
- Multilingual: Supports 46 different locales.
- Extensibility: Supports custom data providers and custom field handlers.
- Ease of use: Features a simple design and clear documentation for straightforward data generation.
- Performance: Widely recognized as the fastest data generator among Python solutions.
- Data variety: Includes various data providers designed for different use cases.
- Schema-based generators: Offers schema-based data generators to effortlessly produce data of any complexity.
- Intuitive: Great editor support. Fully typed, thus autocompletion almost everywhere.
To install mimesis, use pip:
~ pip install mimesis
To work with Mimesis on Python versions 3.8 and 3.9, the final compatible version is Mimesis 11.1.0. Install this specific version to ensure compatibility.
You can find the complete documentation on the Read the Docs.
It is divided into several sections:
- About Mimesis
- Quickstart
- Locales
- Data Providers
- Structured Data Generation
- Random and Seed
- Integration with Pytest
- Integration with factory_boy
- API Reference
- Changelog
You can improve it by sending pull requests to this repository.
The library is exceptionally user-friendly, and it only requires you to import a Data Provider object that corresponds to the desired data type.
For instance, the Person provider can be imported to access personal information, including name, surname, email, and other related fields:
from mimesis import Person
from mimesis.locales import Locale
person = Person(Locale.EN)
person.full_name()
# Output: 'Brande Sears'
person.email(domains=['example.com'])
# Output: '[email protected]'
person.email(domains=['mimesis.name'], unique=True)
# Output: '[email protected]'
person.telephone(mask='1-4##-8##-5##3')
# Output: '1-436-896-5213'Mimesis is licensed under the MIT License. See LICENSE for more information.
