About Mimesis

What Mimesis is?

Mimesis provides a perfect solution for generating data. It effectively populates databases, creates intricate JSON/XML files, and anonymizes productive service data. If you require these functions, Mimesis is the ideal tool for you.

What Mimesis is Not?

Mimesis was not created as an object factory intended for use with a specific database or ORM (e.g., Django ORM, SQLAlchemy, etc.). However, this does not mean that it cannot be used with an ORM. In fact, it can be easily integrated with an ORM using third-party libraries like mimesis-factory or others.

What is the fake data?

Fake data refers to data that is not useful or sensitive, but is used to occupy a space where real data is typically located. This type of data can act as a placeholder for both testing and operational purposes. In testing, it can also serve as stubs or placeholders.

What does name mean?

Mimesis (/maɪˈmiːsəs/; Ancient Greek: μίμησις (mīmēsis), from μιμεῖσθαι (mīmeisthai), “to imitate”, from μῖμος (mimos), “imitator, actor”) is a term of critical and philosophical significance, rooted in Ancient Greek, with various connotations such as imitation, representation, mimicry, receptivity, nonsensuous similarity, resemblance, expression, and the presentation of the self.

Why octopus?

Basically, because octopuses are cool guys, but also because of the fantastic mimicry abilities of some families of octopuses. Have you ever hear about Thaumoctopus mimicus? Just read about that guy, because he is a really badass one.

Features

The key features are:

• Easy: User-friendly data generator, with a simple design and clear documentation for easy and swift data generation.

• Multilingual: Mimesis generates data in a vast range of languages, making it a multilingual tool that caters to numerous language requirements.

• Performance: Mimesis has excellent performance and is widely regarded as the fastest data generator among all Python solutions available.

• Data variety: Mimesis supports a broad range of data providers, including names, addresses, phone numbers, email addresses, dates, times, and more, enabling users to generate data for various purposes.

• Country-specific data providers: Mimesis supports country-specific data providers for generating country-specific data.

• Extensibility: Mimesis is extensible, allowing users to create and integrate their own data providers with the library, thus enabling them to generate custom datasets that meet their unique data generation requirements.

• Generic data provider: Mimesis provides a generic data provider that offers easy access to all the available data providers within the library from a single object, enabling the creation of customized data generation workflows with a simplified and streamlined approach.

• Zero hard dependencies: Mimesis has zero hard dependencies on external modules and does not require the installation of any libraries other than the Python standard library, making it easy to install and use.

• Schema-based generators: Mimesis provides schema-based data generators, offering an effortless way to produce data by the schema of any complexity. This feature enables users to generate customized data that follows a predefined structure or schema, making it especially helpful when creating test data for applications.

Advantages

Compared to other similar libraries, such as Faker, this library offers several advantages, including:

• Performance: It is notably faster than other similar libraries.

• Completeness: It offers a wide range of detailed providers that generate various types of data.

• Simplicity: It solely relies on the Python standard library and does not require any additional modules or dependencies.

Performance

Below you can see the result of performance comparison of Mimesis and Faker:

Generating 10k full names

Library	Method name	Iterations	Uniqueness	Runtime (in seconds)
Mimesis	full_name()	10 000	9988 (99.88%)	0.137
Faker	Faker.name()	10 000	9363 (93.63%)	1.758

Generating 100k full names

Library	Method name	Iterations	Uniqueness	Runtime (in seconds)
Mimesis	full_name()	100 000	98 265 (98.27%)	1.344
Faker	Faker.name()	100 000	71 067 (71.07%)	17.375

Generating 1 million full names

Library	Method name	Iterations	Uniqueness	Runtime (in seconds)
Mimesis	full_name()	1 000 000	847 645 (84.76%)	13.685
Faker	Faker.name()	1 000 000	330 166 (33.02%)	185.945