/faker

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

Primary LanguagePythonMIT LicenseMIT

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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Latest version released on PyPI Build status of the master branch Test coverage Package license


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:

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.

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).

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".

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.

faker.Faker also supports multiple locales. New in v3.0.0.

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:

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': '628-10-1085', 'birthdate': '2008-03-29'}

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

How to create a Provider

How to create a Dynamic Provider

Dynamic providers can read elements from an external source.

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 :

How to use with Factory Boy

Factory Boy already ships with integration with Faker. Simply use the factory.Faker method of factory_boy:

Accessing the random instance

The .random property on the generator returns the instance of random.Random used to generate the values:

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.

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.

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 provide a seed() method, which seeds the shared random number generator. Seed produces the same result when the same methods with the same version of faker are called.

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:

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:

Write documentation for the providers of the default locale:

Write documentation for the providers of a specific locale:

Contribute

Please see CONTRIBUTING.

License

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

Credits