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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For more details, see the extended docs, especially if you are upgrading from version 2.0.4 and below as there might be breaking changes.

Basic Usage

Install with pip:

pip install Faker

Note: this package was previously called fake-factory.

Use 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'

Pytest fixtures

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.

Providers

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.

Localization

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 en_US locale.

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).

Included localized providers:

ar_EG - Arabic (Egypt)
ar_PS - Arabic (Palestine)
ar_SA - Arabic (Saudi Arabia)
bg_BG - Bulgarian
bs_BA - Bosnian
cs_CZ - Czech
de_DE - German
dk_DK - Danish
el_GR - Greek
en_AU - English (Australia)
en_CA - English (Canada)
en_GB - English (Great Britain)
en_IN - English (India)
en_NZ - English (New Zealand)
en_US - English (United States)
es_ES - Spanish (Spain)
es_MX - Spanish (Mexico)
et_EE - Estonian
fa_IR - Persian (Iran)
fi_FI - Finnish
fr_FR - French
hi_IN - Hindi
hr_HR - Croatian
hu_HU - Hungarian
hy_AM - Armenian
it_IT - Italian
ja_JP - Japanese
ka_GE - Georgian (Georgia)
ko_KR - Korean
lt_LT - Lithuanian
lv_LV - Latvian
ne_NP - Nepali
nl_NL - Dutch (Netherlands)
no_NO - Norwegian
pl_PL - Polish
pt_BR - Portuguese (Brazil)
pt_PT - Portuguese (Portugal)
ro_RO - Romanian
ru_RU - Russian
sl_SI - Slovene
sv_SE - Swedish
tr_TR - Turkish
uk_UA - Ukrainian
zh_CN - Chinese (China)
zh_TW - Chinese (Taiwan)

Command line usage

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 use python -m faker instead
-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 as name, address, or text
[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': u'628-10-1085', 'birthdate': '2008-03-29'}

$ faker -r=3 -s=";" name
Willam Kertzmann;
Josiah Maggio;
Gayla Schmitt;

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 customize the Lorem Provider

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.'

How to use with Factory Boy

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')

Accessing the random instance

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.

Seeding the Generator

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.

Tests

Run tests:

$ tox

Write documentation for providers:

$ python -m faker > docs.txt

Contribute

Please see CONTRIBUTING.

License

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

xwine / faker