elizabeth 0.3.23 2017-03-19 ✔ PY3

elizabeth on PyPI  

Elizabeth: dummy data for you.


AuthorLikid Geimfari
LicenseMIT License
Keywords db fake data testing generate elizabeth dummy


Elizabeth is a fast and easy to use Python library for generating dummy data for a variety of purposes. This data can be particularly useful during software development and testing. For example, it could be used to populate a testing database for a web application with user information such as email addresses, usernames, first names, last names, etc.

Elizabeth uses a JSON-based datastore and does not require any modules that are not in the Python standard library. There are over nineteen different data providers available, which can produce data related to food, people, computer hardware, transportation, addresses, and more.

Documentation

Complete documentation for Elizabeth is available here: http://elizabeth.readthedocs.io/

Installation

To install Elizabeth, simply:

➜  ~ pip install elizabeth

Basic Usage:

>>> from elizabeth import Personal
>>> pr = Personal('en')

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

>>> pr.email(gender='male)
'oren5936@live.com'

>>> pr.occupation()
'Programmer'

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. Elizabeth currently includes support for 27 different locales.

Using locales:

>>> from elizabeth import Personal

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

>>> en.full_name()
'Carolin Brady'

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

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

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

>>> from elizabeth import Generic
>>> g = Generic('es')

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

>>> g.code.imei()
'353918052107063'

>>> g.food.fruit()
'Limón'

Advantages

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

  • Performance. Elizabeth is significantly faster than other similar libraries.
  • Completeness. Elizabeth strives to provide many detailed providers that offer a variety of data generators.
  • Simplicity. Elizabeth does not require any modules other than the Python standard library.

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

Integration with Web Application Frameworks

You can use Elizabeth 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)
    email = db.Column(db.String(120), unique=True)
    phone_number = db.Column(db.String(25))
    full_name = db.Column(db.String(100))
    weight = db.Column(db.String(64))
    height = db.Column(db.String(64))
    blood_type = db.Column(db.String(64))
    age = db.Column(db.Integer)

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

    @staticmethod
    def _bootstrap(count=500, locale='en', gender):
        from elizabeth import Personal

        person = Personal(locale)

        for _ in range(count):
            patient = Patient(
                email=person.email(),
                phone_number=person.telephone(),
                full_name=person.full_name(gender=gender),
                age=person.age(minimum=18, maximum=45),
                weight=person.weight(),
                height=person.height(),
                blood_type=person.blood_type()
            )

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

Just run shell mode

(env) ➜ python3 manage.py shell

and do following:

>>> db
<SQLAlchemy engine='sqlite:///db_dev.sqlite'>

>>> Patient
<class 'app.models.Patient'>

>>> Patient()._bootstrap(count=1000, locale='en', gender='female')

Result: screenshot

Custom Providers

You also can add custom provider to Generic.

>>> class SomeProvider():
...
...     class Meta:
...         name = "some_provider"
...
...     @staticmethod
...     def one():
...         return 1

>>> class Another():
...
...     @staticmethod
...     def bye():
...         return "Bye!"

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

>>> generic.some_provider.one()
1

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

Builtins specific data providers

Some countries have data types specific to that country. For example social security numbers in the United States (en locale), and cadastro de pessoas físicas (CPF) in Brazil (pt-br locale).

If you would like to use these country-specific providers, then you must import them explicitly:

>>> from elizabeth import Generic
>>> from elizabeth.builtins import BrazilSpecProvider

>>> generic = Generic('pt-br')

>>> class BrazilProvider(BrazilSpecProvider):
...
...     class Meta:
...         name = "brazil_provider"
...

>>> generic.add_provider(BrazilProvider)
>>> generic.brazil_provider.cpf()
'696.441.186-00'

Decorators

If your locale is cyrillic, but you need latinized locale-specific data, then you can use special decorator. At this moment it’s work only for Russian:

>>> from elizabeth import Personal
>>> from elizabeth.decorators import romanized

>>> pr = Personal('ru')

>>> @romanized('ru')
... def get_name_ro():
...     return pr.full_name()
...

>>> def get_name_ru():
...     return pr.full_name()
...

>>> get_name_ru()
'Вида Панова'

>>> get_name_ro()
'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 Elizabeth for illegal purposes.