Data Generation API¶

Random data generator for testing using the Faker library.

from rapidtest import Data

Data Class¶

Static class providing fake data generation methods for testing purposes.

Personal Information¶

generate_name¶

Data.generate_name() -> str

Generates a random full name.

Example:

name = Data.generate_name()
print(name)  # "John Smith"

generate_email¶

Data.generate_email() -> str

Generates a random email address.

Example:

email = Data.generate_email()
print(email)  # "john.smith@example.com"

generate_phone¶

Data.generate_phone() -> str

Generates a random phone number.

Example:

phone = Data.generate_phone()
print(phone)  # "+1-555-123-4567"

Authentication Data¶

generate_auth_user¶

Data.generate_auth_user() -> Dict[str, str]

Generates a complete user authentication object with username and password.

Returns:

{
    "username": "john_smith_123",
    "password": "SecureP@ssw0rd123"
}

Example:

user = Data.generate_auth_user()
print(user["username"])  # "john_smith_123"
print(user["password"])  # "SecureP@ssw0rd123"

generate_password¶

Data.generate_password() -> str

Generates a secure random password.

Example:

password = Data.generate_password()
print(password)  # "Kp9$mN2#xR4@"

Identification¶

generate_id¶

Data.generate_id() -> str

Generates a unique UUID.

Example:

user_id = Data.generate_id()
print(user_id)  # "f47ac10b-58cc-4372-a567-0e02b2c3d479"

Address Information¶

generate_address¶

Data.generate_address() -> str

Generates a complete postal address.

Example:

address = Data.generate_address()
print(address)  # "123 Main St\nAnytown, CA 12345"

generate_city¶

Data.generate_city() -> str

Generates a random city name.

generate_state¶

Data.generate_state() -> str

Generates a random state/province name.

generate_zipcode¶

Data.generate_zipcode() -> str

Generates a random postal code.

generate_country¶

Data.generate_country() -> str

Generates a random country name.

Text Content¶

generate_text¶

Data.generate_text() -> str

Generates random text (short paragraph).

Example:

text = Data.generate_text()
print(text)  # "Lorem ipsum dolor sit amet..."

generate_paragraph¶

Data.generate_paragraph() -> str

Generates a longer random paragraph.

Professional Information¶

generate_job¶

Data.generate_job() -> str

Generates a random job title.

Example:

job = Data.generate_job()
print(job)  # "Software Engineer"

Date and Time¶

generate_date¶

Data.generate_date() -> str

Generates a random date in ISO format (YYYY-MM-DD).

generate_datetime¶

Data.generate_datetime() -> str

Generates random date and time in ISO format.

generate_time¶

Data.generate_time() -> str

Generates a random time.

URL and Network¶

generate_url¶

Data.generate_url() -> str

Generates a random URL.

Example:

url = Data.generate_url()
print(url)  # "https://www.example.com/page"

generate_domain¶

Data.generate_domain() -> str

Generates a random domain name.

Example:

domain = Data.generate_domain()
print(domain)  # "example.com"

generate_ipv4¶

Data.generate_ipv4() -> str

Generates a random IPv4 address.

Example:

ip = Data.generate_ipv4()
print(ip)  # "192.168.1.1"

Company Information¶

generate_company¶

Data.generate_company() -> str

Generates a random company name.

generate_company_email¶

Data.generate_company_email() -> str

Generates a random company email address.

Example:

email = Data.generate_company_email()
print(email)  # "info@example.com"

Product Information¶

generate_product_name¶

Data.generate_product_name() -> str

Generates a random product name.

generate_price¶

Data.generate_price(min_price: float = 1.0, max_price: float = 1000.0) -> str

Generates a random price.

Parameters: - min_price (float): Minimum price value. Default is 1.0. - max_price (float): Maximum price value. Default is 1000.0.

Example:

price = Data.generate_price()
print(price)  # "99.99"

price = Data.generate_price(min_price=10.0, max_price=500.0)
print(price)  # "249.50"

Bulk Generation¶

generate_users¶

Data.generate_users(
    count: int = 1,
    fields: list[str] | None = None
) -> list[dict[str, str]]

Generates a list of users.

Parameters: - count (int): Number of users to generate. Default is 1. - fields (list[str] | None): Fields to include. Available: 'id', 'name', 'username', 'password', 'email', 'age', 'address', 'phone', 'city', 'state', 'country', 'company'. If None, generates all fields.

Example:

users = Data.generate_users(5)
print(users)  # [{'id': '...', 'name': '...', ...}, ...]

users = Data.generate_users(3, fields=['name', 'email'])
print(users)  # [{'name': '...', 'email': '...'}, ...]

generate_companies¶

Data.generate_companies(count: int = 1) -> list[dict[str, str]]

Generates a list of companies.

Parameters: - count (int): Number of companies to generate. Default is 1.

Example:

companies = Data.generate_companies(3)
print(companies)  # [{'name': '...', 'email': '...', ...}, ...]

generate_products¶

Data.generate_products(
    count: int = 1,
    include_price: bool = True
) -> list[dict[str, str]]

Generates a list of products.

Parameters: - count (int): Number of products to generate. Default is 1. - include_price (bool): Include random price. Default is True.

Example:

products = Data.generate_products(5)
print(products)  # [{'name': '...', 'price': '...'}, ...]

products = Data.generate_products(3, include_price=False)
print(products)  # [{'name': '...'}, ...]

Locale Management¶

set_locale¶

Data.set_locale(locale: str) -> None

Sets the locale for data generation.

Parameters: - locale (str): Locale code (e.g., 'es_ES', 'en_US', 'fr_FR', 'de_DE').

Example:

Data.set_locale("es_ES")
print(Data.generate_name())  # "Carlos García"

Data.set_locale("en_US")
print(Data.generate_name())  # "John Smith"

reset_locale¶

Data.reset_locale() -> None

Resets Faker to default locale (en_US).

Example:

Data.set_locale("es_ES")
Data.reset_locale()
print(Data.generate_name())  # "John Smith"

Complete Example¶

from rapidtest import HTTPTest, Data

# Initialize API tester
api = HTTPTest(url="http://localhost:8000")

# Generate fake user data
fake_user = {
    "id": Data.generate_id(),
    "username": Data.generate_name().replace(" ", "_").lower(),
    "email": Data.generate_email(),
    "password": Data.generate_password(),
    "phone": Data.generate_phone(),
    "address": Data.generate_address(),
    "job": Data.generate_job()
}

# Use fake data in API test
response = api.post(
    path="/users",
    json=fake_user,
    status=201
)

# Generate auth user for login tests
auth_user = Data.generate_auth_user()
api.post(
    path="/login",
    json=auth_user,
    status=200
)

# Generate multiple users for testing
users = Data.generate_users(10, fields=['name', 'email'])
for user in users:
    api.post(path="/users", json=user, status=201)

Benefits¶

Realistic Data: Uses the Faker library for realistic fake data
No Hardcoding: Avoid hardcoded test values that may become stale
Variety: Each test run uses different data, improving test coverage
Convenience: Simple static methods for quick data generation
Locale Support: Generate data in different languages and regions