Minimalistic, standalone alternative fake data generator with no dependencies.
Project description
Minimalistic, standalone alternative fake data generator with no dependencies.
Overview
fake.py is a standalone, portable library designed for generating various random data types for testing.
It offers a simplified, dependency-free alternative for creating random texts, (person) names, URLs, dates, file names, IPs, primitive Python data types (such as uuid, str, int, float, bool) and byte content for multiple file formats including PDF, DOCX, PNG, SVG, BMP, and GIF.
The package also supports file creation on the filesystem and includes factories (dynamic fixtures) compatible with Django, TortoiseORM, and Pydantic.
Features
Generation of random texts, (person) names, emails, URLs, dates, IPs, and primitive Python data types.
Support for various file formats (PDF, DOCX, TXT, PNG, SVG, BMP, GIF) and file creation on the filesystem.
Basic factories for integration with Django, Pydantic, and TortoiseORM.
Prerequisites
Python 3.8+
Installation
pip
pip install fake.pyDownload and copy
fake.py is the sole, self-contained module of the package. It includes tests too. If it’s more convenient to you, you could simply download the fake.py module and include it in your repository.
Since tests are included, it won’t have a negative impact on your test coverage (you might need to apply tweaks to your coverage configuration).
Documentation
Documentation is available on Read the Docs.
For various ready to use code examples see the Recipes.
For tips on PDF creation see Creating PDF.
For tips on DOCX creation see Creating DOCX.
For tips on images creation see Creating images.
For guidelines on contributing check the Contributor guidelines.
Usage
Generate data
Person names
from fake import FAKER
FAKER.first_name()
FAKER.last_name()
FAKER.name()
FAKER.username()Random texts
from fake import FAKER
FAKER.slug()
FAKER.word()
FAKER.sentence()
FAKER.paragraph()
FAKER.text()Internet
from fake import FAKER
FAKER.email()
FAKER.url()
FAKER.ipv4()Filenames
from fake import FAKER
FAKER.filename()Primitive data types
from fake import FAKER
FAKER.pyint()
FAKER.pybool()
FAKER.pystr()
FAKER.pyfloat()Dates
from fake import FAKER
FAKER.date()
FAKER.date_time()Generate files
As bytes
from fake import FAKER
FAKER.pdf()
FAKER.docx()
FAKER.png()
FAKER.svg()
FAKER.bmp()
FAKER.gif()As files on the file system
from fake import FAKER
FAKER.pdf_file()
FAKER.docx_file()
FAKER.png_file()
FAKER.svg_file()
FAKER.bmp_file()
FAKER.gif_file()
FAKER.txt_file()Factories
This is how you could define a factory for Django’s built-in User model.
from django.conf import settings
from django.contrib.auth.models import User
from fake import (
FACTORY,
DjangoModelFactory,
FileSystemStorage,
SubFactory,
pre_save,
)
STORAGE = FileSystemStorage(root_path=settings.MEDIA_ROOT, rel_path="tmp")
class UserFactory(DjangoModelFactory):
username = FACTORY.username()
first_name = FACTORY.first_name()
last_name = FACTORY.last_name()
email = FACTORY.email()
last_login = FACTORY.date_time()
is_superuser = False
is_staff = False
is_active = FACTORY.pybool()
date_joined = FACTORY.date_time()
class Meta:
model = User
get_or_create = ("username",)
@pre_save
def __set_password(instance):
instance.set_password("test")And this is how you could use it:
user = UserFactory()
users = UserFactory.create_batch(5)Tests
Run the tests with unittest:
python -m unittestOr pytest:
pytestDifferences with Faker
fake.py is modeled after the famous Faker package. Its’ API is highly compatible, although drastically reduced. It’s not multilingual and does not support postal codes or that many RAW file formats. However, you could easily include it in your production setup without worrying about yet another dependency.
License
MIT
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