Factory Boy: A Test Fixtures Replacement for Python


factory_boy is a fixtures replacement based on thoughtbot's factory_bot.

As a fixtures replacement tool, it aims to replace static, hard to maintain fixtures with easy-to-use factories for complex objects.

Instead of building an exhaustive test setup with every possible combination of corner cases, factory_boy allows you to use objects customized for the current test, while only declaring the test-specific fields:

class FooTests(unittest.TestCase):

    def test_with_factory_boy(self):
        # We need a 200€, paid order, shipping to australia, for a VIP customer
        order = OrderFactory(
        # Run the tests here

    def test_without_factory_boy(self):
        address = Address(
            street="42 fubar street",
        customer = Customer(
        # etc.

factory_boy is designed to work well with various ORMs (Django, MongoDB, SQLAlchemy), and can easily be extended for other libraries.

Its main features include:

  • Straightforward declarative syntax
  • Chaining factory calls while retaining the global context
  • Support for multiple build strategies (saved/unsaved instances, stubbed objects)
  • Multiple factories per class support, including inheritance



PyPI: https://pypi.org/project/factory-boy/

$ pip install factory_boy

Source: https://github.com/FactoryBoy/factory_boy/

$ git clone git://github.com/FactoryBoy/factory_boy/
$ python setup.py install



This section provides a quick summary of factory_boy features. A more detailed listing is available in the full documentation.

Defining factories

Factories declare a set of attributes used to instantiate a Python object. The class of the object must be defined in the model field of a class Meta: attribute:

import factory
from . import models

class UserFactory(factory.Factory):
    class Meta:
        model = models.User

    first_name = 'John'
    last_name = 'Doe'
    admin = False

# Another, different, factory for the same object
class AdminFactory(factory.Factory):
    class Meta:
        model = models.User

    first_name = 'Admin'
    last_name = 'User'
    admin = True

ORM integration

factory_boy integration with Object Relational Mapping (ORM) tools is provided through specific factory.Factory subclasses:

  • Django, with factory.django.DjangoModelFactory
  • Mogo, with factory.mogo.MogoFactory
  • MongoEngine, with factory.mongoengine.MongoEngineFactory
  • SQLAlchemy, with factory.alchemy.SQLAlchemyModelFactory

More details can be found in the ORM section.

Using factories

factory_boy supports several different instantiation strategies: build, create, and stub:

# Returns a User instance that's not saved
user = UserFactory.build()

# Returns a saved User instance.
# UserFactory must subclass an ORM base class, such as DjangoModelFactory.
user = UserFactory.create()

# Returns a stub object (just a bunch of attributes)
obj = UserFactory.stub()

You can use the Factory class as a shortcut for the default instantiation strategy:

# Same as UserFactory.create()
user = UserFactory()

No matter which strategy is used, it's possible to override the defined attributes by passing keyword arguments:

# Build a User instance and override first_name
>>> user = UserFactory.build(first_name='Joe')
>>> user.first_name

It is also possible to create a bunch of objects in a single call:

>>> users = UserFactory.build_batch(10, first_name="Joe")
>>> len(users)
>>> [user.first_name for user in users]
["Joe", "Joe", "Joe", "Joe", "Joe", "Joe", "Joe", "Joe", "Joe", "Joe"]

Realistic, random values

Demos look better with random yet realistic values; and those realistic values can also help discover bugs. For this, factory_boy relies on the excellent faker library:

class RandomUserFactory(factory.Factory):
    class Meta:
        model = models.User

    first_name = factory.Faker('first_name')
    last_name = factory.Faker('last_name')
>>> RandomUserFactory()
<User: Lucy Murray>

Reproducible random values

The use of fully randomized data in tests is quickly a problem for reproducing broken builds. To that purpose, factory_boy provides helpers to handle the random seeds it uses, located in the factory.random module:

import factory.random

def setup_test_environment():
    # Other setup here

Lazy Attributes

Most factory attributes can be added using static values that are evaluated when the factory is defined, but some attributes (such as fields whose value is computed from other elements) will need values assigned each time an instance is generated.

These "lazy" attributes can be added as follows:

class UserFactory(factory.Factory):
    class Meta:
        model = models.User

    first_name = 'Joe'
    last_name = 'Blow'
    email = factory.LazyAttribute(lambda a: '{}.{}@example.com'.format(a.first_name, a.last_name).lower())
    date_joined = factory.LazyFunction(datetime.now)
>>> UserFactory().email


LazyAttribute calls the function with the object being constructed as an argument, when LazyFunction does not send any argument.


Unique values in a specific format (for example, e-mail addresses) can be generated using sequences. Sequences are defined by using Sequence or the decorator sequence:

class UserFactory(factory.Factory):
    class Meta:
        model = models.User

    email = factory.Sequence(lambda n: 'person{}@example.com'.format(n))

>>> UserFactory().email
>>> UserFactory().email


Some objects have a complex field, that should itself be defined from a dedicated factories. This is handled by the SubFactory helper:

class PostFactory(factory.Factory):
    class Meta:
        model = models.Post

    author = factory.SubFactory(UserFactory)

The associated object's strategy will be used:

# Builds and saves a User and a Post
>>> post = PostFactory()
>>> post.id is None  # Post has been 'saved'
>>> post.author.id is None  # post.author has been saved

# Builds but does not save a User, and then builds but does not save a Post
>>> post = PostFactory.build()
>>> post.id is None
>>> post.author.id is None

Support Policy

factory_boy supports active Python versions as well as PyPy3.

Debugging factory_boy

Debugging factory_boy can be rather complex due to the long chains of calls. Detailed logging is available through the factory logger.

A helper, factory.debug(), is available to ease debugging:

with factory.debug():
    obj = TestModel2Factory()

import logging
logger = logging.getLogger('factory')

This will yield messages similar to those (artificial indentation):

BaseFactory: Preparing tests.test_using.TestModel2Factory(extra={})
  LazyStub: Computing values for tests.test_using.TestModel2Factory(two=<OrderedDeclarationWrapper for <factory.declarations.SubFactory object at 0x1e15610>>)
    SubFactory: Instantiating tests.test_using.TestModelFactory(__containers=(<LazyStub for tests.test_using.TestModel2Factory>,), one=4), create=True
    BaseFactory: Preparing tests.test_using.TestModelFactory(extra={'__containers': (<LazyStub for tests.test_using.TestModel2Factory>,), 'one': 4})
      LazyStub: Computing values for tests.test_using.TestModelFactory(one=4)
      LazyStub: Computed values, got tests.test_using.TestModelFactory(one=4)
    BaseFactory: Generating tests.test_using.TestModelFactory(one=4)
  LazyStub: Computed values, got tests.test_using.TestModel2Factory(two=<tests.test_using.TestModel object at 0x1e15410>)
BaseFactory: Generating tests.test_using.TestModel2Factory(two=<tests.test_using.TestModel object at 0x1e15410>)


factory_boy is distributed under the MIT License.

Issues should be opened through GitHub Issues; whenever possible, a pull request should be included. Questions and suggestions are welcome on the mailing-list.

Development dependencies can be installed in a virtualenv with:

$ pip install --editable '.[dev]'

All pull requests should pass the test suite, which can be launched simply with:

$ make testall

In order to test coverage, please use:

$ make coverage

To test with a specific framework version, you may use a tox target:

# list all tox environments
$ tox --listenvs

# run tests inside a specific environment
$ tox -e py310-djangomain-alchemy-mongoengine

Valid options are:

  • DJANGO for Django
  • MONGOENGINE for mongoengine
  • ALCHEMY for SQLAlchemy

To avoid running mongoengine tests (e.g no MongoDB server installed), run:

$ make SKIP_MONGOENGINE=1 test


For users interesting in packaging FactoryBoy into downstream distribution channels (e.g. .deb, .rpm, .ebuild), the following tips might be helpful:


The package's run-time dependencies are listed in setup.cfg. The dependencies useful for building and testing the library are covered by the dev and doc extras.

Moreover, all development / testing tasks are driven through make(1).


In order to run the build steps (currently only for docs), run:

python setup.py egg_info
make doc


When testing for the active Python environment, run the following:

make test


You must make sure that the factory module is importable, as it is imported from the testing code.

Author: FactoryBoy
Source Code: https://github.com/FactoryBoy/factory_boy
License: MIT License


What is GEEK

Buddha Community

Factory Boy: A Test Fixtures Replacement for Python
Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind


How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.


In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Ray  Patel

Ray Patel


Advance Web Penetration Testing Tool For Python

Features 🎭

Admin Panel Finder

Admin Scanner

Dork Generator

Advance Dork Finder

Extract Links

No Redirect

Hash Crack (Online-Database)

Hash Crack (Wordlist)

Whois Lookup

Tcp Port Scan

Geo IP Lookup

Reserve Analysts Search

Csrf Vernavility Checker


WordPress Username Finder

#testing #advance web penetration testing tool for python #python #advance web penetration #testing tool for python #web