Mimesis is a Python fake data generator

What Mimesis is and what it generates

Mimesis is a Python library for generating fake but realistic data in multiple languages and locales. The README describes it as a fake data generator that can produce names, addresses, dates, phone numbers, emails, financial data, and many other value types. Fake data here means values that look plausible but are not tied to real people, which makes them safe to use in places where real user data would be a privacy risk. The library aims to keep the generated values realistic so that test systems behave close to production.

Common uses for fake data

According to the README, Mimesis is commonly used to populate test databases, mock API responses, generate JSON or XML fixtures, create sample datasets, and anonymize production data. Test databases need volume and variety to surface bugs that only appear with certain inputs. Mock API responses let front-end work proceed before a back end is ready. Fixtures in JSON or XML give repeatable inputs for tests. Anonymizing production data means replacing real values with generated ones so the data can be shared for development without exposing people. Because the data is fake, it can be committed to source control or handed to contractors without the concerns that come with real records.

The key features of Mimesis

The README lists several key features. Multilingual support covers 47 different locales, so generated names and addresses match local conventions. Extensibility lets you add custom data providers and custom field handlers when the built-in ones are not enough. Ease of use comes from a simple design and clear documentation. Performance is called out as widely recognized among the fastest data generators for Python. Data variety means many providers exist for different use cases. Intuitive use comes from full typing, which gives autocompletion in editors, and the simple, consistent API makes it easy to generate data for development and testing.

Schema-based and relational data generation

Two features help with structured data. Schema-based generators let you describe the shape of the data you want and produce values of any complexity from that schema. Relational data support generates related datasets that include foreign keys and nested schemas, so a set of tables can be filled with consistent, linked records. Foreign keys keep child rows pointing at the right parent rows, and nested schemas let one record contain others, matching how real data is shaped. These features move beyond single random values toward whole datasets that respect relationships, which matters when a test must mimic a real schema with joins.

Installation and documentation

Installing Mimesis is done with pip, the standard Python package installer. The README shows the command as pip install mimesis. Documentation is split into sections that cover about, quickstart, locales, data providers, structured and relational data generation, random and seed control, factory_boy integration, and the API reference. The project is licensed under the MIT License, and the README notes support for Python 3.10 through 3.14 plus PyPy. The factory_boy integration helps teams that already use that library for test fixtures.

A short usage example

The README demonstrates usage by importing a data provider that matches the data type you need. For personal information, the Person provider exposes name, surname, email, and related fields. The example below shows how the API reads: it creates a Person for the English locale and then calls methods to produce a full name, an email at a chosen domain, and a telephone number from a mask.

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'

The output shows generated values such as a full name and an email built from a domain you supply. The unique flag asks for a value not seen before, and the telephone mask uses # as a placeholder for digits. This small example reflects the consistent, typed API the README describes.

License and supported Python versions

The MIT license means Mimesis can be used, modified, and redistributed freely as long as the license notice is kept, which suits both open and commercial projects. The README states the library targets Python 3.10 and newer, including 3.11, 3.12, 3.13, 3.14, and PyPy, so teams on current Python runtimes can adopt it. The combination of a typed API, many locales, and schema and relational generation makes Mimesis a broad option for anyone who needs realistic fake data in Python.

Editorial conclusion

Mimesis is a Python library for generating fake but realistic data across many locales, from names and addresses to financial values. It serves test databases, API mocks, fixtures, sample datasets, and production data anonymization, and its key features include 47 locales, custom providers, schema-based and relational generation, and a fully typed API. Installed with pip and released under the MIT license, it is a practical choice for Python teams that need believable data without touching real information.

Frequently asked questions

Official sources

1. License: MIT
2. lk-geimfari/mimesis on GitHub
3. Project website
4. README
5. Releases