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Faker is a Python package that generates fake data for you.

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Faker is a Python package that generates fake data for you. Whether you need to bootstrap your database, create good-looking XML documents, fill-in your persistence to stress test it, or anonymize data taken from a production service, Faker is for you.

Faker is heavily inspired by PHP's Faker, Perl's Data::Faker, and by ruby's Faker.

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For more details, see the extended docs.

Basic Usage

Install with pip:

pip install fake-factory

Use faker.Factory.create() to create and initialize a faker generator, which can generate data by accessing properties named after the type of data you want.

    from faker import Factory
    fake = Factory.create()

    # OR
    from faker import Faker
    fake = Faker()

    fake.name()
    # 'Lucy Cechtelar'

    fake.address()
    # "426 Jordy Lodge
    #  Cartwrightshire, SC 88120-6700"

    fake.text()
    # Sint velit eveniet. Rerum atque repellat voluptatem quia rerum. Numquam excepturi
    # beatae sint laudantium consequatur. Magni occaecati itaque sint et sit tempore. Nesciunt
    # amet quidem. Iusto deleniti cum autem ad quia aperiam.
    # A consectetur quos aliquam. In iste aliquid et aut similique suscipit. Consequatur qui
    # quaerat iste minus hic expedita. Consequuntur error magni et laboriosam. Aut aspernatur
    # voluptatem sit aliquam. Dolores voluptatum est.
    # Aut molestias et maxime. Fugit autem facilis quos vero. Eius quibusdam possimus est.
    # Ea quaerat et quisquam. Deleniti sunt quam. Adipisci consequatur id in occaecati.
    # Et sint et. Ut ducimus quod nemo ab voluptatum.

Each call to method fake.name() yields a different (random) result. This is because faker forwards faker.Generator.method_name() calls to faker.Generator.format(method_name).

for i in range(0,10):
  print fake.name()

    # Adaline Reichel
    # Dr. Santa Prosacco DVM
    # Noemy Vandervort V
    # Lexi O'Conner
    # Gracie Weber
    # Roscoe Johns
    # Emmett Lebsack
    # Keegan Thiel
    # Wellington Koelpin II
    # Ms. Karley Kiehn V

Providers

Each of the generator properties (like name, address, and lorem) are called "fake". A faker generator has many of them, packaged in "providers". Here is a list of the bundled formatters in the default locale.

Localization

faker.Factory can take a locale as an argument, to return localized data. If no localized provider is found, the factory fallbacks to the default locale (en_EN).

from faker import Factory
fake = Factory.create('it_IT')
for i in range(0,10):
    print fake.name()

> Elda Palumbo
> Pacifico Giordano
> Sig. Avide Guerra
> Yago Amato
> Eustachio Messina
> Dott. Violante Lombardo
> Sig. Alighieri Monti
> Costanzo Costa
> Nazzareno Barbieri
> Max Coppola

You can check available Faker locales in the source code, under the providers package. The localization of Faker is an ongoing process, for which we need your help. Don't hesitate to create localized providers to your own locale and submit a PR!

Included localized providers:

Using from shell

In a python environment with faker installed you can use it with:

python -m faker [option] [*args]

[option]:

  • formatter name as text, address: display result of fake

[*args]: pass value to formatter (actually only strings)

$ python -m faker address
968 Bahringer Garden Apt. 722
Kristinaland, NJ 09890

How to create a Provider

from faker import Faker
fake = Faker()

# first, import a similar Provider or use the default one
from faker.providers import BaseProvider

# create new provider class
class MyProvider(BaseProvider):
    def foo(self):
        return 'bar'

# then add new provider to faker instance
fake.add_provider(MyProvider)

# now you can use:
fake.foo()
> 'bar'

How to use with factory-boy

import factory
from faker import Factory as FakerFactory
from myapp.models import Book

faker = FakerFactory.create()


class Book(factory.Factory):
    FACTORY_FOR = Book

    title = factory.LazyAttribute(lambda x: faker.sentence(nb_words=4))
    author_name = factory.LazyAttribute(lambda x: faker.name())

Seeding the Generator

You may want to get always the same generated data - for instance when using Faker for unit testing purposes. The generator offers a seed() method, which seeds the random number generator. Calling the same script twice with the same seed produces the same results.

from faker import Faker
fake = Faker()
fake.seed(4321)

print fake.name()   # Margaret Boehm

Tests

Run tests:

$ python setup.py test

or

$ python -m unittest -v faker.tests

Write documentation for providers:

$ python -m faker > docs.txt

License

Faker is released under the MIT Licence. See the bundled LICENSE file for details.

Credits

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