faker

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

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Faker

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Quick Overview

Faker is a Python library that generates fake data for various purposes such as testing, development, and data anonymization. It provides a wide range of data types including names, addresses, phone numbers, and more, supporting multiple locales and languages.

Pros

Extensive variety of data types and categories
Support for multiple languages and locales
Easy to use and integrate into existing projects
Customizable and extendable

Cons

Some locales may have limited data compared to others
Generated data may not always be realistic in certain contexts
Performance can be slower for large-scale data generation
Occasional inconsistencies in data formatting across different providers

Code Examples

Generate a random name:

from faker import Faker

fake = Faker()
print(fake.name())
# Output: John Doe

Generate a list of 5 email addresses:

fake = Faker()
emails = [fake.email() for _ in range(5)]
print(emails)
# Output: ['example@example.com', 'user123@domain.net', ...]

Generate localized data (e.g., French):

fake = Faker('fr_FR')
print(fake.address())
# Output: 3, rue de la Paix
#         75002 PARIS

Getting Started

To get started with Faker, first install it using pip:

pip install Faker

Then, you can use it in your Python code:

from faker import Faker

fake = Faker()

# Generate some fake data
print(f"Name: {fake.name()}")
print(f"Email: {fake.email()}")
print(f"Address: {fake.address()}")
print(f"Phone: {fake.phone_number()}")
print(f"Text: {fake.text()}")

This will output various types of fake data. You can customize the output by specifying a locale or using different provider methods. Refer to the Faker documentation for more detailed usage instructions and available data providers.

Competitor Comparisons

Faker

26,791

Faker is a PHP library that generates fake data for you

Pros of Faker

More extensive documentation and examples
Larger community and more frequent updates
Wider range of locales and data providers

Cons of Faker

PHP-specific, limiting cross-language compatibility
Heavier resource usage for large-scale data generation
Some inconsistencies in method naming conventions

Code Comparison

faker (Python):

from faker import Faker

fake = Faker()
print(fake.name())
print(fake.address())
print(fake.text())

Faker (PHP):

<?php
require_once 'vendor/autoload.php';

$faker = Faker\Factory::create();
echo $faker->name() . "\n";
echo $faker->address() . "\n";
echo $faker->text() . "\n";

Both libraries provide similar functionality for generating fake data, but with language-specific implementations. faker is more versatile across different programming languages, while Faker offers a more comprehensive set of features within the PHP ecosystem. The choice between the two depends on the specific project requirements, language preferences, and the need for cross-language compatibility.

java-faker

4,697

Brings the popular ruby faker gem to Java

Pros of java-faker

Native Java implementation, offering better performance for Java projects
Extensive localization support with numerous locales available
Comprehensive API documentation and examples

Cons of java-faker

Limited to Java ecosystem, less versatile than faker
Smaller community and fewer contributors compared to faker
Less frequent updates and releases

Code Comparison

java-faker:

Faker faker = new Faker();
String name = faker.name().fullName();
String streetAddress = faker.address().streetAddress();

faker:

from faker import Faker
fake = Faker()
name = fake.name()
street_address = fake.street_address()

Summary

java-faker is a robust library for generating fake data in Java applications, offering excellent performance and localization support. However, it's limited to the Java ecosystem and has a smaller community compared to faker.

faker, on the other hand, is a more versatile and widely-used library, supporting multiple programming languages and offering a larger variety of data generators. It benefits from a larger community and more frequent updates but may have slightly lower performance in Java environments compared to java-faker.

Choose java-faker for Java-specific projects requiring high performance and extensive localization, while faker is better suited for multi-language projects or when a wider range of data generators is needed.

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README

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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Compatibility

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.

Basic Usage

Install with pip:

.. code:: bash

pip install Faker

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.

.. code:: python

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).

.. code:: python

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".

.. code:: python

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 LCID string for US english, ie: en_US.

.. code:: python

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.

.. code:: python

from faker import Faker
fake = Faker(['it_IT', 'en_US', 'ja_JP'])
for _ in range(10):
    print(fake.name())

# é´æ¨ é½ä¸
# Leslie Moreno
# Emma Williams
# æ¸¡è¾º è£ç¾å
# Marcantonio Galuppi
# Martha Davis
# Kristen Turner
# ä¸æ´¥å· æ¥é¦
# Ashley Castillo
# å±±ç° æ¡å

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).

Optimizations

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.

Command line usage

When installed, you can invoke faker from the command-line:

.. code:: console

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:

.. code:: console

$ 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;

How to create a Provider

.. code:: python

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'

How to create a Dynamic Provider

Dynamic providers can read elements from an external source.

.. code:: python

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.'

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 <http://www.cupcakeipsum.com/>__ :

.. code:: python

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:

.. code:: python

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:

.. code:: python

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.

Unique values

Through use of the .unique property on the generator, you can guarantee that any generated values are unique for this specific instance.

.. code:: python

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 <https://en.wikipedia.org/wiki/Birthday_problem>_, collisions are more likely than you'd think.

.. code:: python

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.

Seeding the Generator

When using Faker for unit testing, you will often want to generate the same data set. For convenience, the generator also provides a seed() method, which seeds the shared random number generator. A Seed produces the same result when the same methods with the same version of faker are called.

.. code:: python

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

print(fake.name())
# 'Margaret Boehm'

Each generator can also be switched to use its own instance of random.Random, separated from the shared one, by using the seed_instance() method, which acts the same way. For example:

.. code:: python

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:

.. code:: bash

$ tox

Write documentation for the providers of the default locale:

.. code:: bash

$ python -m faker > docs.txt

Write documentation for the providers of a specific locale:

.. code:: bash

$ python -m faker --lang=de_DE > docs_de.txt

Contribute

Please see CONTRIBUTING_.

License

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

Credits

FZaninotto_ / PHP Faker_
Distribute_
Buildout_
modern-package-template_

.. _FZaninotto: https://github.com/fzaninotto .. _PHP Faker: https://github.com/fzaninotto/Faker .. _Perl Faker: http://search.cpan.org/~jasonk/Data-Faker-0.07/ .. _Ruby Faker: https://github.com/stympy/faker .. _Distribute: https://pypi.org/project/distribute/ .. _Buildout: http://www.buildout.org/ .. _modern-package-template: https://pypi.org/project/modern-package-template/ .. _extended docs: https://faker.readthedocs.io/en/stable/ .. _bundled providers: https://faker.readthedocs.io/en/stable/providers.html .. _community providers: https://faker.readthedocs.io/en/stable/communityproviders.html .. _pytest fixture docs: https://faker.readthedocs.io/en/master/pytest-fixtures.html .. _LICENSE: https://github.com/joke2k/faker/blob/master/LICENSE.txt .. _CONTRIBUTING: https://github.com/joke2k/faker/blob/master/CONTRIBUTING.rst .. _Factory Boy: https://github.com/FactoryBoy/factory_boy

.. |pypi| image:: https://img.shields.io/pypi/v/Faker.svg?style=flat-square&label=version :target: https://pypi.org/project/Faker/ :alt: Latest version released on PyPI

.. |coverage| image:: https://img.shields.io/coveralls/joke2k/faker/master.svg?style=flat-square :target: https://coveralls.io/r/joke2k/faker?branch=master :alt: Test coverage

.. |build| image:: https://github.com/joke2k/faker/actions/workflows/ci.yml/badge.svg :target: https://github.com/joke2k/faker/actions/workflows/ci.yml :alt: Build status of the master branch

.. |license| image:: https://img.shields.io/badge/license-MIT-blue.svg?style=flat-square :target: https://raw.githubusercontent.com/joke2k/faker/master/LICENSE.txt :alt: Package license

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Faker

26,791

Faker is a PHP library that generates fake data for you

java-faker

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Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.

Try Visual Copilot

faker

Top Related Projects

Faker

java-faker

Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Faker

Pros of Faker

Cons of Faker

Code Comparison

java-faker

Pros of java-faker

Cons of java-faker

Code Comparison

Summary

Convert designs to code with AI

README

Compatibility

Basic Usage

Pytest fixtures

Providers

Localization

Optimizations

Command line usage

How to create a Provider

How to create a Dynamic Provider

How to customize the Lorem Provider

How to use with Factory Boy

Accessing the random instance

Unique values

Seeding the Generator

Tests

Contribute

License

Credits

Top Related Projects

Faker

java-faker

Convert designs to code with AI

NPM DownloadsLast 30 Days

Accessing the `random` instance