Metadata-Version: 1.1
Name: mimesis
Version: 4.0.0
Summary: Mimesis: fake data generator.
Home-page: https://github.com/lk-geimfari/mimesis
Author: Likid Geimfari (Isaak Uchakaev)
Author-email: likid.geimfari@gmail.com
License: MIT License
Description: Mimesis - Fake Data Generator
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        .. image:: https://raw.githubusercontent.com/lk-geimfari/mimesis/master/media/readme-logo.png
             :target: https://github.com/lk-geimfari/mimesis
        
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        Description
        -----------
        
        .. image:: https://travis-ci.org/lk-geimfari/mimesis.svg?branch=master
             :target: https://travis-ci.org/lk-geimfari/mimesis
             :alt: Travis CI
        
        .. image:: https://readthedocs.org/projects/mimesis/badge/?version=latest
             :target: https://mimesis.name/
             :alt: Documentation Status
        
        .. image:: https://codecov.io/gh/lk-geimfari/mimesis/branch/master/graph/badge.svg
             :target: https://codecov.io/gh/lk-geimfari/mimesis
             :alt: Code Coverage
        
        .. image:: https://badge.fury.io/py/mimesis.svg
             :target: https://badge.fury.io/py/mimesis
             :alt: Package version
        
        .. image:: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-brightgreen.svg
             :target: https://badge.fury.io/py/mimesis
             :alt: Python version
        
        
        
        Mimesis is a package for Python, which helps generate big volumes of fake data for a variety of purposes in a variety of languages. The fake data could be used to populate a testing database, create beautiful JSON and XML files, anonymize data taken from production and etc.
        
        
        Installation
        ------------
        
        To install mimesis, simply use pip:
        
        .. code:: text
        
            [env] ~ ⟩ pip install mimesis
        
        Getting started
        ---------------
        
        This library is really easy to use and everything you need is just import an object which
        represents a type of data you need (we call such object *Provider*).
        
        In example below we import provider `Person <https://mimesis.name/api.html#person>`_,
        which represents data related to personal information, such as name, surname, email and etc:
        
        .. code:: python
        
            >>> from mimesis import Person
            >>> person = Person('en')
        
            >>> person.full_name()
            'Antonetta Garrison'
        
            >>> person.occupation()
            'Backend Developer'
            
            >>> person.telephone()
            '1-408-855-5063'
        
        
        More about the other providers you can read in our `documentation`_.
        
        .. _documentation: https://mimesis.name/getting_started.html#providers
        
        
        Locales
        -------
        
        Mimesis currently includes support for 33 different `locales`_. You can
        specify a locale when creating providers and they will return data that
        is appropriate for the language or country associated with that locale.
        
        Let's take a look how it works:
        
        .. code:: python
        
            >>> from mimesis import Person
            >>> from mimesis.enums import Gender
        
            >>> de = Person('de')
            >>> en = Person('en')
        
            >>> de.full_name(gender=Gender.FEMALE)
            'Sabrina Gutermuth'
        
            >>> en.full_name(gender=Gender.MALE)
            'Layne Gallagher'
        
        .. _locales: https://mimesis.name/getting_started.html#locales
        
        Providers
        ---------
        
        Mimesis support over twenty different data providers available,
        which can produce data related to people, food, computer hardware,
        transportation, addresses, and more.
        
        See `API Reference <https://mimesis.name/api.html>`_ for more info.
        
        
        Generating structured data
        --------------------------
        
        You can generate dictionaries which can be easily converted to any
        format you want (JSON/XML/YAML etc.)  with any structure you want.
        
        Just use object ``Field()`` as shown below:
        
        .. code:: python
        
            >>> from mimesis.schema import Field, Schema
            >>> from mimesis.enums import Gender
            >>> _ = Field('en')
            >>> description = (
            ...     lambda: {
            ...         'id': _('uuid'),
            ...         'name': _('text.word'),
            ...         'version': _('version', pre_release=True),
            ...         'timestamp': _('timestamp', posix=False),
            ...         'owner': {
            ...             'email': _('person.email', key=str.lower),
            ...             'token': _('token_hex'),
            ...             'creator': _('full_name', gender=Gender.FEMALE),
            ...         },
            ...     }
            ... )
            >>> schema = Schema(schema=description)
            >>> schema.create(iterations=1)
        
        Output:
        
        .. code:: text
        
            [
              {
                'id': '7a41f446-57a8-ec17-b9ad-367742251679',
                'name': 'desert',
                'version': '7.3.7-alpha.6',
                'timestamp': '2026-06-06T14:00:52Z',
                'owner': {
                  'email': 'damaged1829@gmail.com',
                  'token': 'acfd799af9b46e5560a51dabace593033171ec81e997905acfc602c93a741735',
                  'creator': 'Keena Hendricks'
                }
              }
            ]
        
        
        See `Schema and Fields <https://mimesis.name/getting_started.html#schema-and-fields>`_ for more info.
        
        
        
        Documentation
        -------------
        
        You can find the complete documentation on the `Read the Docs`_.
        
        It is divided into several sections:
        
        -  `Foreword`_
        -  `Getting Started`_
        -  `Tips and Tricks`_
        -  `API Reference`_
        -  `Contributing`_
        -  `Changelog`_
        
        You can improve it by sending pull requests to this repository.
        
        .. _Read the Docs: https://mimesis.name
        .. _Foreword: https://mimesis.name/foreword.html
        .. _Getting Started: https://mimesis.name/getting_started.html
        .. _Tips and Tricks: https://mimesis.name/tips.html
        .. _API Reference: https://mimesis.name/api.html
        .. _Contributing: https://mimesis.name/contributing.html
        .. _Changelog: https://mimesis.name/changelog.html
        
        
        How to Contribute
        -----------------
        
        1. Take a look at `contributing guidelines`_.
        2. Check for open issues or open a fresh issue to start a discussion
           around a feature idea or a bug.
        3. Fork the repository on GitHub to start making your changes to the
           *your_branch* branch.
        4. Add yourself to the list of `contributors`_.
        5. Send a pull request and bug the maintainer until it gets merged and
           published.
        
        .. _contributing guidelines: https://github.com/lk-geimfari/mimesis/blob/master/CONTRIBUTING.rst
        .. _contributors: https://github.com/lk-geimfari/mimesis/blob/master/CONTRIBUTORS.rst
        
        
        License
        -------
        
        Mimesis is licensed under the MIT License. See `LICENSE`_ for more
        information.
        
        .. _LICENSE: https://github.com/lk-geimfari/mimesis/blob/master/LICENSE
        
Keywords: fake,mock,data,populate,database,testing,generate,mimesis,dummy
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Testing
