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Machine Learning and Deep Learning\u003c/span\u003e","测试","\u003e 1k ★","🐍 Python","File Manipulation","testing","Libraries","Testing [🔝](#readme)","Fake Data","Performance Testing","Testing Tools","Awesome Python","Uncategorized","Feature Extraction","Awesome Privacy Engineering [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"],"sub_categories":["Testing","测试","\u003cspan id=\"head42\"\u003e3.5.1. Machine Learning\u003c/span\u003e","Useful Python Tools for Data Analysis","HTTP Clients","Synthetic Data Generation","Uncategorized","Text/NLP","Synthetic Data"],"readme":"\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/lk-geimfari/mimesis\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/lk-geimfari/mimesis/master/.github/images/logo.png\" width=\"300\" alt=\"Mimesis\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003cem\u003eMimesis: The Fake Data Generator\u003c/em\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://github.com/lk-geimfari/mimesis/actions/workflows/test.yml?query=branch%3Amaster\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://github.com/lk-geimfari/mimesis/actions/workflows/test.yml/badge.svg?branch=master\" alt=\"Test\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://mimesis.name/en/latest/\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://readthedocs.org/projects/mimesis/badge/?version=latest\" alt=\"Coverage\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/mimesis/\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/pypi/v/mimesis?color=bright-green\" alt=\"Package version\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/mimesis/\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/pypi/dm/mimesis\" alt=\"Package version\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/mimesis/\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13%20%7C%203.14%20%7C%20pypy-brightgreen\" alt=\"Supported Python versions\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\n---\n\n**Documentation**: \u003ca href=\"https://mimesis.name/\" target=\"_blank\"\u003ehttps://mimesis.name/\u003c/a\u003e\n\n---\n\nMimesis ([/mɪˈmiːsɪs](https://mimesis.name/master/about.html#what-does-name-mean)) is a Python library for generating fake but realistic data in multiple languages and locales. It can generate names, addresses, dates, phone numbers, emails, financial data, and many other types of values.\n\nIt is commonly used to populate test databases, mock API responses, generate JSON or XML fixtures, create sample datasets, and anonymize production data. Mimesis provides a simple, consistent API that makes it easy to generate realistic data for development and testing.\n\nThe key features are:\n\n- **Multilingual**: Supports 47 different locales.\n- **Extensibility**: Supports custom data providers and custom field handlers.\n- **Ease of use**: Features a simple design and clear documentation for straightforward data generation.\n- **Performance**: Widely recognized as the fastest data generator among Python solutions.\n- **Data variety**: Includes various data providers designed for different use cases.\n- **Schema-based generators**: Offers schema-based data generators to effortlessly produce data of any complexity.\n- **Relational data**: Supports generating related datasets with foreign keys and nested schemas.\n- **Intuitive**: Great editor support. Fully typed, so you get autocompletion almost everywhere.\n\n## Installation\n\nTo install Mimesis, use pip:\n\n```\n~ pip install mimesis\n```\n\n## Documentation\n\nYou can find the complete documentation on the [Read the Docs](https://mimesis.name/).\n\nIt is divided into several sections:\n\n-  [About Mimesis](https://mimesis.name/latest/about.html)\n-  [Quickstart](https://mimesis.name/latest/quickstart.html)\n-  [Locales](https://mimesis.name/latest/locales.html)\n-  [Data Providers](https://mimesis.name/latest/providers.html)\n-  [Structured Data Generation](https://mimesis.name/latest/schema.html)\n-  [Relational Data Generation](https://mimesis.name/latest/relational.html)\n-  [Random and Seed](https://mimesis.name/latest/random_and_seed.html)\n-  [Integration with factory_boy](https://mimesis.name/latest/factory_plugin.html)\n-  [API Reference](https://mimesis.name/latest/api.html)\n-  [Changelog](https://mimesis.name/latest/index.html#changelog)\n\nYou can improve it by sending pull requests to this repository.\n\n## Usage\n\nImport a data provider that corresponds to the data type you need.\n\nFor example, the [Person](https://mimesis.name/latest/api.html#person) provider gives access to personal information,\nincluding name, surname, email, and other related fields:\n\n```python\nfrom mimesis import Person\nfrom mimesis.locales import Locale\n\nperson = Person(Locale.EN)\n\nperson.full_name()\n# Output: 'Brande Sears'\n\nperson.email(domains=['example.com'])\n# Output: 'roccelline1878@example.com'\n\nperson.email(domains=['mimesis.name'], unique=True)\n# Output: 'f272a05d39ec46fdac5be4ac7be45f3f@mimesis.name'\n\nperson.telephone(mask='1-4##-8##-5##3')\n# Output: '1-436-896-5213'\n```\n\n## License\n\nMimesis is licensed under the MIT License. See [LICENSE](https://github.com/lk-geimfari/mimesis/blob/master/LICENSE) for more information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flk-geimfari%2Fmimesis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flk-geimfari%2Fmimesis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flk-geimfari%2Fmimesis/lists"}