fake.py 0.3

Minimalistic, standalone alternative fake data generator with no dependencies.

Minimalistic, standalone alternative fake data generator with no dependencies.

Table of Contents

• Overview

• Features

• Prerequisites

• Installation

  • pip

  • Download and copy

• Documentation

• Usage

  • Generate data

  • Generate files

  • Factories

• Tests

• Differences with Faker

• License

• Author

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.py

Download 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 unittest

Or pytest:

pytest

Differences 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

Author

Artur Barseghyan <[e-mail]>