Generate fake data using joke2k's faker and your own schema.
pip install faker-schemafrom faker_schema.faker_schema import FakerSchema
schema = {'employee_id': 'uuid4', 'employee_name': 'name', 'employee address': 'address',
'email_address': 'email'}
faker = FakerSchema()
data = faker.generate_fake(schema)
print(data)
# {'employee_id': '956f0cf3-a954-5bff-0aaf-ee0e1b7e1e1b', 'employee_name': 'Adam Wells',
# 'employee address': '189 Kyle Springs Suite 110\nNorth Robin, OR 73512',
# 'email_address': 'jmcgee@gmail.com'}This library is dependent on faker for available schema types. Faker provides a wide variety of data types via providers. For a list of available providers, checkout Providers and Community Providers
Once you know what types you want to generate your fake data, you can start defining your own schema
The expected schema is a dictionary, where the keys are field names and the values are the types of the fields. The schema dictionay can have nested dictionaries and lists too.
faker-schema currently provides two ways of loading your schema:
- JSON file
- JSON string
import json
from faker_schema.faker_schema import FakerSchema
from faker_schema.schema_loader import load_json_from_file, load_json_from_string
schema = load_json_from_file('path_to_json_file')
faker = FakerSchema()
data = faker.generate_fake(schema)
# OR
json_string = '{"employee_id"": "uuid4", "employee_name": "name"", "employee address":
"address", "email_address": "email"}'
schema = load_json_from_string(json_string)
faker = FakerSchema()
data = faker.generate_fake(schema)You can define your own way of loading a schema, convert it to a Python dictionary and pass it to the FakerSchema instance. The aim was to de-couple schema loading/generation from fake data generation. If you want to contribute more schema loading techniques, please open a GitHub issue or send a pull request.
The Faker library provides a list of different locales. You can choose your required locale from that list and provide it to the FakerSchema instance
from faker_schema.faker_schema import FakerSchema
schema = {'employee_id': 'uuid4', 'employee_name': 'name', 'employee address': 'address',
'email_address': 'email'}
faker = FakerSchema(locale='it_IT')
data = faker.generate_fake(schema)
print(data)
# {'employee_id': '47f8bb04-fc05-25c9-73cc-e8a22f29ee4e', 'employee_name': 'Caio Negri',
# 'employee address': 'Stretto Davis 34\nDamico lido, 54802 Vibo Valentia (TR)',
# 'email_address': 'nunzia19@libero.it'}from faker_schema.faker_schema import FakerSchema
schema = {'EmployeeInfo': {'ID': 'uuid4', 'Name': 'name', 'Contact': {'Email': 'email',
'Phone Number': 'phone_number'}, 'Location': {'Country Code': 'country_code',
'City': 'city', 'Country': 'country', 'Postal Code': 'postalcode',
'Address': 'street_address'}}}
faker = FakerSchema()
data = faker.generate_fake(schema)
# {'EmployeeInfo': {'ID': '0751f889-0d83-d05f-4eeb-16f575c6b4a3', 'Name': 'Stacey Williams',
# 'Contact': {'Email':'jpatterson@yahoo.com', 'Phone Number': '1-077-859-6393'},
# 'Location': {'Country Code': 'IE', 'City': 'Dyermouth', 'Country':
# 'United States Minor Outlying Islands', 'Postal Code': '84239',
# 'Address': '94806 Joseph Plaza Apt. 783'}}}from faker_schema.faker_schema import FakerSchema
schema = {'Employer': 'name', 'EmployeList': [{'Name': 'name'}, {'Name': 'name'},
{'Name': 'name'}]}
faker = FakerSchema()
data = faker.generate_fake(schema)
# {'Employer': 'Faith Knapp', 'EmployeList': [{'Name': 'Douglas Bailey'},
# {'Name': 'Karen Rivera'}, {'Name': 'Linda Vance MD'}]}from faker_schema.faker_schema import FakerSchema
schema = {'employee_id': 'uuid4', 'employee_name': 'name', 'employee address': 'address',
'email_address': 'email'}
faker = FakerSchema()
data = faker.generate_fake(schema, iterations=4)
print(data)
# [{'employee_id': 'e07a7964-9636-bca6-2a58-4a69ac126dc5', 'employee_name':
# 'Charlene Blankenship', 'employee address': '0431 Edward Mountains Suite 697\nPort Douglas,
# TX 96239-7277', 'email_address': 'ashley86@yahoo.com'}, {'employee_id':
# '42b02262-3e0c-cf40-8257-4a0af122dddb', 'employee_name': 'Cheryl Stevens',
# 'employee address': '48066 Eric Lake\nPhillipshire, MO 57224', 'email_address':
# 'lisa05@nash.info'}, {'employee_id': '41efbcc4-bb32-9260-b2b3-8fac29782e01',
# 'employee_name': 'Dennis Campbell', 'employee address':
# '52418 Diana Mills Suite 590\nEast Mackenzie, HI 16222', 'email_address':
# 'jennifer39@gmail.com'}, {'employee_id': '80bf12ff-2f3a-6db6-f3a6-14cb50076a46',
# 'employee_name': 'Jimmy Avery', 'employee address':
# '6867 Eddie Forest Apt. 735\nBranditon, IL 32717', 'email_address': 'ashley64@griffin.com'}]If you are using a community provider or you created your own provider, you can use those with faker-schema as well. I will use the provider, faker_web as an example.
After installing faker_web,
from faker import Faker
from faker_schema import FakerSchema
from faker_web import WebProvider
fake = Faker()
fake.add_provider(WebProvider)
faker = FakerSchema(faker=fake)
headers_schema = {'Content-Type': 'content_type', 'Server': 'server_token'}
fake_headers = faker.generate_fake(headers_schema)
print(fake_headers)
# {'Content-Type': 'application/json', 'Server': 'Apache/2.0.51 (Ubuntu)'} - Using make
make test- Using nose
nosetests - Using nose with coverage
nosetests --with-coverage --cover-package=faker_schema --cover-erase -v --cover-html- Using make
make flake8- Using flake8
flake8 --max-line-length 99 faker_schema/ tests/Usman Ehtesham Gul (ueg1990) -uehtesham90@gmail.com
If you want to add any new features, or improve existing one or if you find bugs, please open a GitHub issue or feel free to send a pull request. If you have any questions or need help/mentoring with contributions, feel free to contact me via email