faker

faker

Python模拟数据生成库

Faker是一个Python库,用于生成各类模拟数据。它可创建姓名、地址、文本等多种格式的虚拟数据,支持多语言本地化,提供命令行接口。Faker适用于填充测试数据库、创建示例文档和压力测试等场景。该库易用且可扩展,支持自定义提供程序,是开发测试中的实用工具。

FakerPython包假数据生成本地化命令行使用Github开源项目

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_.


::

_|_|_|_|          _|
_|        _|_|_|  _|  _|      _|_|    _|  _|_|
_|_|_|  _|    _|  _|_|      _|_|_|_|  _|_|
_|      _|    _|  _|  _|    _|        _|
_|        _|_|_|  _|    _|    _|_|_|  _|

|pypi| |build| |coverage| |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:

.. 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())

# 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).

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

编辑推荐精选

扣子-AI办公

扣子-AI办公

职场AI,就用扣子

AI办公助手,复杂任务高效处理。办公效率低?扣子空间AI助手支持播客生成、PPT制作、网页开发及报告写作,覆盖科研、商业、舆情等领域的专家Agent 7x24小时响应,生活工作无缝切换,提升50%效率!

堆友

堆友

多风格AI绘画神器

堆友平台由阿里巴巴设计团队创建,作为一款AI驱动的设计工具,专为设计师提供一站式增长服务。功能覆盖海量3D素材、AI绘画、实时渲染以及专业抠图,显著提升设计品质和效率。平台不仅提供工具,还是一个促进创意交流和个人发展的空间,界面友好,适合所有级别的设计师和创意工作者。

图像生成AI工具AI反应堆AI工具箱AI绘画GOAI艺术字堆友相机AI图像热门
码上飞

码上飞

零代码AI应用开发平台

零代码AI应用开发平台,用户只需一句话简单描述需求,AI能自动生成小程序、APP或H5网页应用,无需编写代码。

Vora

Vora

免费创建高清无水印Sora视频

Vora是一个免费创建高清无水印Sora视频的AI工具

Refly.AI

Refly.AI

最适合小白的AI自动化工作流平台

无需编码,轻松生成可复用、可变现的AI自动化工作流

酷表ChatExcel

酷表ChatExcel

大模型驱动的Excel数据处理工具

基于大模型交互的表格处理系统,允许用户通过对话方式完成数据整理和可视化分析。系统采用机器学习算法解析用户指令,自动执行排序、公式计算和数据透视等操作,支持多种文件格式导入导出。数据处理响应速度保持在0.8秒以内,支持超过100万行数据的即时分析。

AI工具酷表ChatExcelAI智能客服AI营销产品使用教程
TRAE编程

TRAE编程

AI辅助编程,代码自动修复

Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。

AI工具TraeAI IDE协作生产力转型热门
AIWritePaper论文写作

AIWritePaper论文写作

AI论文写作指导平台

AIWritePaper论文写作是一站式AI论文写作辅助工具,简化了选题、文献检索至论文撰写的整个过程。通过简单设定,平台可快速生成高质量论文大纲和全文,配合图表、参考文献等一应俱全,同时提供开题报告和答辩PPT等增值服务,保障数据安全,有效提升写作效率和论文质量。

AI辅助写作AI工具AI论文工具论文写作智能生成大纲数据安全AI助手热门
博思AIPPT

博思AIPPT

AI一键生成PPT,就用博思AIPPT!

博思AIPPT,新一代的AI生成PPT平台,支持智能生成PPT、AI美化PPT、文本&链接生成PPT、导入Word/PDF/Markdown文档生成PPT等,内置海量精美PPT模板,涵盖商务、教育、科技等不同风格,同时针对每个页面提供多种版式,一键自适应切换,完美适配各种办公场景。

AI办公办公工具AI工具博思AIPPTAI生成PPT智能排版海量精品模板AI创作热门
潮际好麦

潮际好麦

AI赋能电商视觉革命,一站式智能商拍平台

潮际好麦深耕服装行业,是国内AI试衣效果最好的软件。使用先进AIGC能力为电商卖家批量提供优质的、低成本的商拍图。合作品牌有Shein、Lazada、安踏、百丽等65个国内外头部品牌,以及国内10万+淘宝、天猫、京东等主流平台的品牌商家,为卖家节省将近85%的出图成本,提升约3倍出图效率,让品牌能够快速上架。

下拉加载更多