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Generate fake data for any purpose

Project description

dammy

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Generate fake/dummy data for any purpose

Table of contents

Introduction

dammy is a powerful and simple tool to generate fake data. You can use it to mock classes, populate databases and and much more. You can check the full documentation here.

Features

  • Generate anything within the set of prebuilt objects (Person names, country names, car manufacturers and models, random dates...)
  • Compose more complex data easily (Full profiles, complete databases, )
  • The possibility to expand the previous set with little to no code
  • Completely intuitive, you will learn to use it in less than 10 minutes
  • Export the generated data to SQL

Example

If you wanted to generate 1000 random people, just define what a person looks like and dammy will handle the rest

from dammy import EntityGenerator
from dammy.functions import cast
from dammy.stdlib import RandomName, RandomString, RandomDateTime, RandomInteger, CountryName

class Person(EntityGenerator):
    first_name = RandomName().upper()
    password = RandomString(5)
    birthday = RandomDateTime(start=datetime(1980, 1, 1), end=datetime(2000, 12, 31), date_format='%d/%m/%Y')
    favorite_number = RandomInteger(0, 10)
    age = cast((datetime.now() - birthday).days / 365.25, int)
    country = CountryName()

# Generate 1000 random people
for i in range(0, 1000):
    print(Person())

Which will output:

{'identifier': 1, 'uid': '9XCha', 'first_name': 'ZAYN', 'blood': 'A+', 'birthday': '24/01/1982', 'favorite_number': 5, 'age': 38, 'country': 'Denmark'}
{'identifier': 2, 'uid': 'bbYbw', 'first_name': 'MALIHA', 'blood': 'AB+', 'birthday': '01/12/1981', 'favorite_number': 1, 'age': 38, 'country': 'Syrian Arab Republic'}
{'identifier': 3, 'uid': 'aGF49', 'first_name': 'ANGEL', 'blood': 'AB+', 'birthday': '18/08/1992', 'favorite_number': 1, 'age': 27, 'country': 'Macedonia, the Former Yugoslav Republic of'}
{'identifier': 4, 'uid': 'Lcr0J', 'first_name': 'REUBEN', 'blood': '0-', 'birthday': '07/11/1997', 'favorite_number': 4, 'age': 22, 'country': 'Dominican Republic'}
{'identifier': 5, 'uid': 'P7mD4', 'first_name': 'MAMADOU', 'blood': 'A+', 'birthday': '02/01/1987', 'favorite_number': 7, 'age': 33, 'country': 'Palau'}
{'identifier': 6, 'uid': 'ykdeL', 'first_name': 'BATSHEVA', 'blood': 'A+', 'birthday': '11/01/1983', 'favorite_number': 5, 'age': 37, 'country': 'Seychelles'}
{'identifier': 7, 'uid': 'h9HjQ', 'first_name': 'JIMENA', 'blood': 'A-', 'birthday': '23/10/1985', 'favorite_number': 0, 'age': 34, 'country': 'China'}
{'identifier': 8, 'uid': 'rjt92', 'first_name': 'YERIK', 'blood': 'AB+', 'birthday': '29/10/1991', 'favorite_number': 5, 'age': 28, 'country': 'Libya'}
{'identifier': 9, 'uid': 'vL0DE', 'first_name': 'YISRAEL', 'blood': 'AB+', 'birthday': '25/03/1989', 'favorite_number': 8, 'age': 30, 'country': 'Spain'}
{'identifier': 10, 'uid': 'CsrhX', 'first_name': 'JOSHUA', 'blood': 'AB+', 'birthday': '20/09/1999', 'favorite_number': 1, 'age': 20, 'country': 'Svalbard and Jan Mayen'}
...

It also supports relationships between tables, so you can generate data to populate databases

from dammy import EntityGenerator
from dammy.db import AutoIncrement, PrimaryKey, ForeignKey
from dammy.stdlib import RandomName, RandomString, RandomDateTime, RandomInteger, CountryName

# Define what a person looks like
class Person(EntityGenerator):
    id_pk = PrimaryKey(id=AutoIncrement())
    first_name = RandomName().upper()
    password = RandomString(5)
    birthday = RandomDateTime(start=datetime(1980, 1, 1), end=datetime(2000, 12, 31), date_format='%d/%m/%Y')
    favorite_number = RandomInteger(0, 10)
    age = cast((datetime.now() - birthday).days / 365.25, int)
    country = CountryName()

# Define what a car looks like
class Car(EntityGenerator):
    id_pk = PrimaryKey(id=AutoIncrement())
    manufacturer_name = CarBrand()
    model = CarModel(car_brand=manufacturer_name)
    owner = ForeignKey(Person, 'identifier')

And the data can be exported to SQL

from dammy import DatasetGenerator

# Generate a dataset with 20000 cars and 94234 people
dataset = DatasetGenerator((Car, 20000), (Person, 94234)).generate()
dataset.get_sql(save_to='cars_with_owners.sql')

Installation

To install the latest stable release of dammy using pip

pip install dammy

You can also install the latest development release by cloning the repository and installing it with pip

git clone https://github.com/ibonn/dammy.git dammy
cd dammy
pip install -e .

Release history

  • 1.1.0

    • Iterators added
  • 1.0.0

    • Semantic versioning used from now on
    • Documentation fixed
    • Minor code changes (duplicated code removed...)
  • 0.1.3

    • Code refactored
    • All binary operations made possible between BaseGenerator objects
    • BaseDammy renamed to BaseGenerator
    • EntityGenerator renamed to OperationResult
    • DammyEntity renamed to EntityGenerator
    • Everything inherits from BaseGenerator
    • Removed DatabaseConstraint
    • Added UNIQUE constraint support
    • Datasets can now be exported to JSON
    • Entities can now be exported to JSON and CSV
    • dammy.stdlib expanded with new built-in generators
  • 0.1.2

    • Documentation improved
    • DatasetGenerator moved from main to db
    • Minor bugs fixed
  • 0.1.1

    • Can get attributes of entities
    • Can call methods on entities
    • Ability to perform operations added
    • Code improved
    • Docstrings added
  • 0.0.3

    • Fixed import bug in stdlib
  • 0.0.1

    • First release

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