mimesis-data

mimesis-data

Overview

Mimesis is a Python test-data generator that's "widely recognized as the fastest data generator among Python solutions" with full type hints for editor autocompletion (mimesis-readme).

The library supports 46 locales (mimesis-readme) and exposes both per-provider methods (Person.full_name()) and a schema-based generator for typed-dict shapes.

When to use

• The project is Python and the team values mimesis's speed (~10-100x faster than Faker on bulk generation, per the upstream benchmarks).
• Type hints matter - mimesis exposes typed return values that show up in IDE autocomplete.
• The project needs strong locale coverage - mimesis ships 46 locales; Faker ships 70+ but many are Faker-thin (only a few providers populated).
• Schema-based generation is a fit - mimesis's Schema / Field pattern produces typed dicts directly.

If the team is already standardized on Faker, switching is rarely worth it - see faker-data. If the team needs factory orchestration with referential integrity, pair mimesis with factory_boy (or use factory-bot-data in Ruby projects).

Install

pip install mimesis

(Per mimesis-readme.)

Authoring

Per-provider usage

from mimesis import Person, Address, Internet, Datetime
from mimesis.locales import Locale

person = Person(Locale.EN)
person.full_name()                 # 'Brande Sears'
person.email(domains=['example.com'])   # '[email protected]'
person.gender()
person.title()

address = Address(Locale.EN)
address.full_address()             # '123 Main St, Springfield, IL 62701'
address.city()
address.country()

internet = Internet()
internet.url()
internet.ip_v4()
internet.user_agent()

dt = Datetime()
dt.datetime()
dt.date()
dt.formatted_datetime()

(Adapted from mimesis-readme.)

Generic - one entry point per locale

from mimesis import Generic
from mimesis.locales import Locale

g = Generic(Locale.EN)
g.person.full_name()
g.address.city()
g.internet.email()

Generic aggregates every provider under one instance - preferred when a fixture needs values from multiple providers; avoids constructing one provider per type.

Schema-based - typed-dict generation

from mimesis import Field, Schema, Locale

field = Field(Locale.EN)

# Build one row's worth of data
def schema():
    return {
        "id": field("uuid"),
        "name": field("person.full_name"),
        "email": field("person.email"),
        "created_at": field("datetime.datetime"),
        "address": {
            "city": field("address.city"),
            "zip": field("address.postal_code"),
        },
    }

# Generate a list of rows
generator = Schema(schema=schema, iterations=1000)
data = generator.create()    # → list of 1000 dicts

(Adapted from mimesis-readme schema documentation.)

The schema/field pattern is mimesis's distinguishing feature - it produces typed-dict shapes without per-field method calls, which makes it convenient for bulk fixture generation (e.g. seeding a test DB with 10k rows).

Locale support

from mimesis import Person
from mimesis.locales import Locale

Person(Locale.EN).full_name()    # 'Brande Sears'
Person(Locale.JA).full_name()    # '広橋 美月'
Person(Locale.RU).full_name()    # 'Анастасия Иванова'
Person(Locale.DE).full_name()    # 'Klaus Müller'

Per mimesis-readme, 46 locales are supported. Full list at mimesis.name/latest/locales.html.

Seeding for determinism

from mimesis import Generic
from mimesis.locales import Locale

g = Generic(Locale.EN, seed=12345)
g.person.full_name()    # deterministic based on seed

Pass seed= at provider construction; subsequent calls are deterministic. Same as faker-data, seed in tests so failures reproduce locally.

Pairing with factory_boy

Mimesis can be the value engine for factory_boy:

from factory import Factory, LazyFunction
from mimesis import Person, Locale
from myapp.models import User

person = Person(Locale.EN, seed=42)

class UserFactory(Factory):
    class Meta:
        model = User

    name  = LazyFunction(person.full_name)
    email = LazyFunction(lambda: person.email())

LazyFunction ensures each factory instantiation re-calls the mimesis method - getting a new value per fixture, not a single shared one.

Anti-patterns

Anti-pattern	Why it fails	Fix
Constructing one provider per attribute	Person() per field is N times the constructor cost; slows bulk generation.	Use Generic once; access providers as attributes.
Hardcoding seed= literally to a value the test depends on	Brittle: a mimesis update changes the PRNG sequence; the test fails next upgrade.	Pin mimesis version; OR assert patterns (matches a regex), not literal values.
Using mimesis for security payloads	Mimesis generates realistic-looking data; SQL injection / XSS won't appear.	Use malicious-payload-bank.
Schema with 100k iterations in pytest	Memory-bound; slow.	Generate to disk (Schema.to_csv, Schema.to_json) and seed the DB outside the test.
Mixing mimesis + Faker in the same project	Two PRNGs to seed; two doc surfaces; two upgrade cadences.	Pick one; if migrating, do it in a single PR.

Limitations

• Smaller community than Faker. Fewer Stack Overflow answers, fewer third-party providers.
• PRNG sequence varies across major versions. Pin the version in CI for deterministic tests across runs.
• No native factory orchestration. You still need factory_boy (or hand-rolled equivalents) for referential integrity.

References

• mimesis-readme - install, providers, Generic, Schema/Field, 46 locales.
• mimesis docs - https://mimesis.name/
• faker-data - Python Faker alternative.
• synthetic-data-toolkit - dispatcher selecting between mimesis / Faker / FactoryBot / Bogus.