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 Faker, Perl Faker, and by Ruby Faker.
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Starting from version 4.0.0, Faker dropped support for Python 2 and from version 5.0.0
only supports Python 3.7 and above. If you still need Python 2 compatibility, please install version 3.0.1 in the
meantime, and please consider updating your codebase to support Python 3 so you can enjoy the
latest features Faker has to offer. Please see the extended docs for more details, especially
if you are upgrading from version 2.0.4 and below as there might be breaking changes.
This package was also previously called fake-factory which was already deprecated by the end
of 2016, and much has changed since then, so please ensure that your project and its dependencies
do not depend on the old package.
Install with pip:
pip install FakerUse faker.Faker() 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 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 _ in range(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'Faker also has its own pytest plugin which provides a faker fixture you can use in your
tests. Please check out the pytest fixture docs to learn more.
Each of the generator properties (like name, address, and
lorem) are called "fake". A faker generator has many of them,
packaged in "providers".
from faker import Faker
from faker.providers import internet
fake = Faker()
fake.add_provider(internet)
print(fake.ipv4_private())Check the extended docs for a list of bundled providers and a list of community providers.
faker.Faker can take a locale as an argument, to return localized
data. If no localized provider is found, the factory falls back to the
default LCID string for US english, ie: en_US.
from faker import Faker
fake = Faker('it_IT')
for _ in range(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'faker.Faker also supports multiple locales. New in v3.0.0.
from faker import Faker
fake = Faker(['it_IT', 'en_US', 'ja_JP'])
for _ in range(10):
print(fake.name())
# 鈴木 陽一
# Leslie Moreno
# Emma Williams
# 渡辺 裕美子
# Marcantonio Galuppi
# Martha Davis
# Kristen Turner
# 中津川 春香
# Ashley Castillo
# 山田 桃子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. Please don't hesitate to create a localized provider for your own locale and submit a Pull Request (PR).
The Faker constructor takes a performance-related argument called
use_weighting. It specifies whether to attempt to have the frequency
of values match real-world frequencies (e.g. the English name Gary would
be much more frequent than the name Lorimer). If use_weighting is False,
then all items have an equal chance of being selected, and the selection
process is much faster. The default is True.
When installed, you can invoke faker from the command-line:
faker [-h] [--version] [-o output]
[-l {bg_BG,cs_CZ,...,zh_CN,zh_TW}]
[-r REPEAT] [-s SEP]
[-i {package.containing.custom_provider otherpkg.containing.custom_provider}]
[fake] [fake argument [fake argument ...]]Where:
faker: is the script when installed in your environment, in development you could usepython -m fakerinstead-h,--help: shows a help message--version: shows the program's version number-o FILENAME: redirects the output to the specified filename-l {bg_BG,cs_CZ,...,zh_CN,zh_TW}: allows use of a localized provider-r REPEAT: will generate a specified number of outputs-s SEP: will generate the specified separator after each generated output-i {my.custom_provider other.custom_provider}list of additional custom providers to use. Note that is the import path of the package containing your Provider class, not the custom Provider class itself.fake: is the name of the fake to generate an output for, such asname,address, ortext[fake argument ...]: optional arguments to pass to the fake (e.g. the profile fake takes an optional list of comma separated field names as the first argument)
Examples:
$ faker address
968 Bahringer Garden Apt. 722
Kristinaland, NJ 09890
$ faker -l de_DE address
Samira-Niemeier-Allee 56
94812 Biedenkopf
$ faker profile ssn,birthdate
{'ssn': '628-10-1085', 'birthdate': '2008-03-29'}
$ faker -r=3 -s=";" name
Willam Kertzmann;
Josiah Maggio;
Gayla Schmitt;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) -> str:
return 'bar'
# then add new provider to faker instance
fake.add_provider(MyProvider)
# now you can use:
fake.foo()
# 'bar'Dynamic providers can read elements from an external source.
from faker import Faker
from faker.providers import DynamicProvider
medical_professions_provider = DynamicProvider(
provider_name="medical_profession",
elements=["dr.", "doctor", "nurse", "surgeon", "clerk"],
)
fake = Faker()
# then add new provider to faker instance
fake.add_provider(medical_professions_provider)
# now you can use:
fake.medical_profession()
# 'dr.'You can provide your own sets of words if you don't want to use the default lorem ipsum one. The following example shows how to do it with a list of words picked from cakeipsum :
from faker import Faker
fake = Faker()
my_word_list = [
'danish','cheesecake','sugar',
'Lollipop','wafer','Gummies',
'sesame','Jelly','beans',
'pie','bar','Ice','oat' ]
fake.sentence()
# 'Expedita at beatae voluptatibus nulla omnis.'
fake.sentence(ext_word_list=my_word_list)
# 'Oat beans oat Lollipop bar cheesecake.'Factory Boy already ships with integration with Faker. Simply use the
factory.Faker method of factory_boy:
import factory
from myapp.models import Book
class BookFactory(factory.Factory):
class Meta:
model = Book
title = factory.Faker('sentence', nb_words=4)
author_name = factory.Faker('name')The .random property on the generator returns the instance of
random.Random used to generate the values:
from faker import Faker
fake = Faker()
fake.random
fake.random.getstate()By default all generators share the same instance of random.Random, which
can be accessed with from faker.generator import random. Using this may
be useful for plugins that want to affect all faker instances.
Through use of the .unique property on the generator, you can guarantee
that any generated values are unique for this specific instance.
from faker import Faker
fake = Faker()
names = [fake.unique.first_name() for i in range(500)]
assert len(set(names)) == len(names)Calling fake.unique.clear() clears the already seen values.
Note, to avoid infinite loops, after a number of attempts to find a unique
value, Faker will throw a UniquenessException. Beware of the birthday
paradox, collisions
are more likely than you'd think.
from faker import Faker
fake = Faker()
for i in range(3):
# Raises a UniquenessException
fake.unique.boolean()In addition, only hashable arguments and return values can be used
with .unique.
When using Faker for unit testing, you will often want to generate the same
data set. For convenience, the generator also provide a seed() method,
which seeds the shared random number generator. Calling the same methods with
the same version of faker and seed produces the same results.
from faker import Faker
fake = Faker()
Faker.seed(4321)
print(fake.name())
# 'Margaret Boehm'Each generator can also be switched to its own instance of random.Random,
separate to the shared one, by using the seed_instance() method, which acts
the same way. For example:
from faker import Faker
fake = Faker()
fake.seed_instance(4321)
print(fake.name())
# 'Margaret Boehm'Please note that as we keep updating datasets, results are not guaranteed to be
consistent across patch versions. If you hardcode results in your test, make sure
you pinned the version of Faker down to the patch number.
If you are using pytest, you can seed the faker fixture by defining a faker_seed
fixture. Please check out the pytest fixture docs to learn more.
Run tests:
$ toxWrite documentation for providers:
$ python -m faker > docs.txtPlease see CONTRIBUTING.
Faker is released under the MIT License. See the bundled LICENSE file for details.