Mocking and Patching in Python Tests

1. Introduction

In Python testing, mocking and patching are techniques used to replace parts of your system under test and make assertions about how they have been used.

2. Key Concepts

• Mocking: Creating fake objects to simulate the behavior of real objects.
• Patching: Temporarily replacing or modifying the target object for the duration of a test.
• Unit Testing: Testing individual components of your software to ensure they work as intended.

3. Mocking

Mocking is the process of creating a mock object that simulates the behavior of a real object. This is particularly useful for testing components in isolation.

Python provides a built-in library called unittest.mock which is used for creating mock objects.

3.1 Creating a Mock Object

from unittest.mock import Mock

# Create a mock object
mock_object = Mock()

# Define the return value of a method
mock_object.method_name.return_value = 'Mocked Value'

# Call the method
result = mock_object.method_name()

print(result)  # Output: Mocked Value
                

4. Patching

Patching is a way to temporarily replace a target object with a mock. This allows you to control the behavior of that object during the test.

The patch decorator can be used to apply patching.

4.1 Using the Patch Decorator

from unittest.mock import patch

# Function to be tested
def fetch_data():
    # Simulates an API call
    return "Real Data"

# Test function
@patch('__main__.fetch_data', return_value='Mocked Data')
def test_fetch_data(mock_fetch):
    result = fetch_data()
    assert result == 'Mocked Data'

test_fetch_data()
                

5. Best Practices

• Use mocking and patching to isolate tests and avoid dependencies on external systems.
• Keep tests independent to ensure they can run in any order.
• Document the purpose of mocks and patches for future reference.
• Use assert_called_with to verify that mocks were used correctly.

6. FAQ