Skip to main content

Mimesis: mock data for developers.

Project description

https://raw.githubusercontent.com/lk-geimfari/mimesis/master/media/logo.png

Mimesis is a fast and easy to use library for Python, which helps generate mock data for a variety of purposes (see “Data providers”) in a variety of languages (see “Locales”). This data can be particularly useful during software development and testing. The library was written with the use of tools from the standard Python library, and therefore, it does not have any side dependencies.

Build Status Build status on Windows codecov PyPI version Python

Advantages

Mimesis offers a number of advantages over other similar libraries, such as Faker:

  • Performance. Mimesis is significantly faster than other similar libraries.

  • Completeness. Mimesis strives to provide many detailed providers that offer a variety of data generators.

  • Simplicity. Mimesis does not require any modules other than the Python standard library.

See here for an example of how we compare performance with other libraries.

Documentation

Mimesis is very simple to use, and the below examples should help you get started. Complete documentation for Mimesis is available here.

Installation

To install mimesis, simply:

  ~ pip install mimesis

Basic Usage

As we said above, this library is really easy to use:

>>> import mimesis
>>> person = mimesis.Personal(locale='en')

>>> person.full_name(gender='female')
'Antonetta Garrison'

>>> person.occupation()
'Backend Developer'

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:

>>> from mimesis import Personal

>>> de = Personal('de')
>>> ic = Personal('is')

>>> de.full_name()
'Sabrina Gutermuth'

>>> ic.full_name()
'Rósa Þórlindsdóttir'

Mimesis currently includes support for 31 different locales. See here.

When you only need to generate data for a single locale, use the Generic() provider, and you can access all providers of Mimesis from one object.

>>> import mimesis
>>> g = mimesis.Generic('es')

>>> g.datetime.month()
'Agosto'

>>> g.food.fruit()
'Limón'

Data providers

Mimesis support over twenty different data providers available, which can produce data related to food, people, computer hardware, transportation, addresses, and more. See details for more information.

Provider

Description

1

Address

Address data (street name, street suffix etc.)

2

Business

Business data (company, company_type, copyright etc.)

3

Code

Codes (ISBN, EAN, IMEI etc.).

4

ClothingSizes

Clothing sizes (international sizes, european etc.)

5

Datetime

Datetime (day_of_week, month, year etc.)

6

Development

Data for developers (version, programming language etc.)

7

File

File data (extension etc.)

8

Food

Information on food (vegetables, fruits, measurements etc.)

9

Games

Games data (game, score, pegi_rating etc.)

10

Personal

Personal data (name, surname, age, email etc.)

11

Text

Text data (sentence, title etc.)

12

Transport

Dummy data about transport (truck model, car etc.)

13

Science

Scientific data (scientist, math_formula etc.)

14

Structured

Structured data (html, css etc.)

15

Internet

Internet data (facebook, twitter etc.)

16

Hardware

The data about the hardware (resolution, cpu, graphics etc.)

17

Numbers

Numerical data (floats, primes, digit etc.)

18

Path

Provides methods and property for generate paths.

19

UnitSytem

Provides names of unit systems in international format

20

Generic

All at once

21

Cryptographic

Cryptographic data

Custom Providers

You also can add custom provider to Generic(), using add_provider() method:

>>> import mimesis
>>> generic = mimesis.Generic('en')

>>> class SomeProvider(object):
...     class Meta:
...         name = "some_provider"
...
...     def hello(self):
...         return "Hello!"

>>> class Another(object):
...     def bye(self):
...         return "Bye!"

>>> generic.add_provider(SomeProvider)
>>> generic.add_provider(Another)

>>> generic.some_provider.hi()
'Hello!'

>>> generic.another.bye()
'Bye!'

or multiple custom providers using method add_providers():

>>> generic.add_providers(SomeProvider, Another)

Builtins specific data providers

Some countries have data types specific to that country. For example social security numbers (SSN) in the United States of America (en), and cadastro de pessoas físicas (CPF) in Brazil (pt-br). If you would like to use these country-specific providers, then you must import them explicitly:

>>> from mimesis import Generic
>>> from mimesis.builtins import BrazilSpecProvider

>>> generic = Generic('pt-br')
>>> generic.add_provider(BrazilSpecProvider)
>>> generic.brazil_provider.cpf()
'696.441.186-00'

Integration with Web Application Frameworks

You can use Mimesis during development and testing of applications built on a variety of frameworks. Here is an example of integration with a Flask application:

class Patient(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    full_name = db.Column(db.String(100))
    blood_type = db.Column(db.String(64))

    def __init__(self, **kwargs):
        super(Patient, self).__init__(**kwargs)

    @staticmethod
    def populate(count=500, locale=None):
        import mimesis

        person =  mimesis.Personal(locale=locale)

        for _ in range(count):
            patient = Patient(
                full_name=person.full_name('female'),
                blood_type=person.blood_type(),
            )

            db.session.add(patient)
            try:
                db.session.commit()
            except IntegrityError:
                db.session.rollback()

Just run shell mode and do following:

>>> Patient().populate(count=1000, locale='en')

Generate data by schema

Mimesis support generating data by schema:

>>> from mimesis.schema import Schema
>>> schema = Schema('en')

>>> schema.load(schema={
...     "id": "cryptographic.uuid",
...     "name": "text.word",
...     "version": "development.version",
...     "owner": {
...         "email": "personal.email",
...         "token": "cryptographic.token",
...         "creator": "personal.full_name"
...     }
... }).create(iterations=2)

>>> # or you can load data from json file:
>>> schema.load(path='schema.json').create(iterations=2)

Result:

[
  {
    "id": "790cce21-5f75-2652-2ee2-f9d90a26c43d",
    "name": "container",
    "owner": {
      "email": "anjelica8481@outlook.com",
      "token": "0bf924125640c46aad2a860f40ec4b7f33a516c497957abd70375c548ed56978",
      "creator": "Ileen Ellis"
    },
    "version": "4.11.6"
  },
  ...
]

Decorators

If your locale belongs to the family of Cyrillic languages, but you need latinized locale-specific data, then you can use special decorator which help you romanize your data. At this moment it’s works only for Russian and Ukrainian:

>>> from mimesis.decorators import romanized

>>> @romanized('ru')
... def russian_name():
...     return 'Вероника Денисова'

>>> russian_name()
'Veronika Denisova'

Disclaimer

The authors assume no responsibility for how you use this library data generated by it. This library is designed only for developers with good intentions. Do not use the data generated with Mimesis for illegal purposes.

Contributing

Your contributions are always welcome! Please take a look at the contribution guidelines first. Here you can look at list of our contributors.

License

Mimesis is licensed under the MIT License. See LICENSE for more information.

Author

Likid Geimfari (likid.geimfari@gmail.com)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mimesis-0.0.6.tar.gz (2.4 MB view details)

Uploaded Source

File details

Details for the file mimesis-0.0.6.tar.gz.

File metadata

  • Download URL: mimesis-0.0.6.tar.gz
  • Upload date:
  • Size: 2.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for mimesis-0.0.6.tar.gz
Algorithm Hash digest
SHA256 09115e538e3a9134b4d00ebbb02624ac30ad88161b0a98be4481bdb84ba0de11
MD5 6c4ddc2722b0b06a0fbc66282b6ac595
BLAKE2b-256 3670571fcafcd00b3fb364d0a68174e131c4bbb33544592f8d628ea10f35d412

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page