Tip for clean tests in Python:

Structure your tests with the Arrange-Act-Assert pattern to improve readability and maintainability.

• Arrange: Set up the test. Initialize objects, prepare data, and configure any mocks or stubs.
• Act: Execute the code being tested. Call the specific function or method.
• Assert: Check the outcome. Verify that the result of the action is what you expected.

import pytest
from dataclasses import dataclass, field


# Code to be tested
@dataclass
class Product:
    name: str
    price: float


@dataclass
class ShoppingCart:
    items: list[Product] = field(default_factory=list)

    def add_item(self, product: Product):
        if product.price < 0:
            raise ValueError("Product price cannot be negative.")
        self.items.append(product)

    def get_total_price(self) -> float:
        return sum(item.price for item in self.items)


# Tests using the Arrange-Act-Assert pattern
def test_get_total_price_for_multiple_items():
    # Arrange
    product1 = Product(name="Mouse", price=25.50)
    product2 = Product(name="Keyboard", price=75.50)
    cart = ShoppingCart()
    cart.add_item(product1)
    cart.add_item(product2)

    # Act
    total_price = cart.get_total_price()

    # Assert
    assert total_price == 101.00


def test_get_total_price_for_empty_cart():
    # Arrange
    cart = ShoppingCart()

    # Act
    total_price = cart.get_total_price()

    # Assert
    assert total_price == 0.0


def test_add_item_with_negative_price_raises_value_error():
    # Arrange
    cart = ShoppingCart()
    product_with_negative_price = Product(name="Invalid Item", price=-50.0)

    # Act & Assert
    with pytest.raises(ValueError, match="Product price cannot be negative."):
        cart.add_item(product_with_negative_price)

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By: @DataScienceQ ✨