pytest-mock for enhanced mocking - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is pytest-mock and why is it useful?

pytest-mock is a plugin for pytest that simplifies mocking in tests by providing a mocker fixture. It helps replace parts of your system under test with mock objects easily and cleanly.

beginner

How do you use the mocker fixture in a pytest test function?

You add mocker as a parameter to your test function. Then use mocker.patch() to replace a function or object with a mock during the test.

intermediate

What is the difference between mocker.patch and unittest.mock.patch?

mocker.patch is a pytest-mock wrapper around unittest.mock.patch. It integrates better with pytest, automatically cleans up mocks after tests, and provides simpler syntax.

intermediate

How does pytest-mock help with test cleanup?

pytest-mock automatically undoes all patches after each test, so you don't need to manually stop or reset mocks. This prevents side effects between tests.

beginner

Give an example of mocking a function using pytest-mock.

Example: ```python def test_example(mocker): mock_func = mocker.patch('module.function_to_mock') mock_func.return_value = 42 result = module.function_to_mock() assert result == 42 ```

What is the main benefit of using pytest-mock over unittest.mock directly?

A Requires no imports

B Automatic cleanup of mocks after each test

C Supports only Python 2

D Faster test execution

pytest-mock automatically undoes patches after each test, preventing side effects and making tests cleaner.

How do you apply a mock patch using pytest-mock?

A Use mocker.mock('target')

B Call unittest.mock.patch directly without parameters

C Use mocker.patch('target') inside a test function with mocker parameter

D Add @patch decorator to the test function

pytest-mock provides the mocker fixture, and you use mocker.patch('target') inside your test function.

What does mocker.patch return?

A A mock object replacing the target

B The original function

C A boolean indicating success

D None

mocker.patch returns a mock object that replaces the target during the test.

Which of these is true about pytest-mock's mocker fixture?

A It is automatically available when pytest-mock is installed

B It must be imported manually in each test file

C It only works with unittest tests

D It disables all mocks after the test

When pytest-mock is installed, the mocker fixture is automatically available in pytest tests.

If you want to mock a method 'foo' in class 'Bar' in module 'baz', what is the correct patch target string?

A 'baz.foo.Bar'

B 'Bar.foo'

C 'foo.Bar.baz'

D 'baz.Bar.foo'

The patch target string uses the full import path: 'baz.Bar.foo'.

Explain how pytest-mock simplifies mocking in pytest tests.

Think about how mocker.patch works and test cleanup.

Describe the steps to mock a function using pytest-mock and verify its return value.

Consider a simple example mocking a function to return a fixed value.

Practice

1. What is the main purpose of using pytest-mock in tests?

easy

A. To replace parts of code with mock objects during testing

B. To speed up test execution by skipping tests

C. To automatically generate test data

D. To format test output for better readability

Solution

1. Step 1: Understand pytest-mock functionality

   pytest-mock is a plugin that helps replace parts of your code with mock objects during tests.

2. Step 2: Identify the main use case

   This replacement allows you to control and verify behavior without running actual code parts.

3. Final Answer:

   To replace parts of code with mock objects during testing -> Option A

4. Quick Check:

   pytest-mock = mocking code parts [OK]

Hint: Remember pytest-mock is for replacing code parts with mocks [OK]

Common Mistakes:

• Confusing mocking with test skipping
• Thinking pytest-mock generates test data
• Assuming pytest-mock formats output

2. Which of the following is the correct way to mock a function get_data in module my_module using pytest-mock?

easy

A. mocker.patch('my_module.get_data')

B. mocker.patch(get_data)

C. mocker.mock('my_module.get_data')

D. mocker.replace('my_module.get_data')

Solution

1. Step 1: Recall pytest-mock patch syntax

   The correct syntax uses a string with the full path: mocker.patch('module.function').

2. Step 2: Identify correct option

   mocker.patch('my_module.get_data') uses the correct string path format. Options A, B, and D use incorrect methods or arguments.

3. Final Answer:

   mocker.patch('my_module.get_data') -> Option A

4. Quick Check:

   Use string path in mocker.patch() [OK]

Hint: Use string 'module.function' in mocker.patch() [OK]

Common Mistakes:

• Passing function object instead of string path
• Using mocker.mock instead of patch
• Using mocker.replace which doesn't exist

3. Given the code below, what will be the output of the test?

def fetch_data():
    return 'real data'

def test_fetch(mocker):
    mocker.patch('__main__.fetch_data', return_value='mocked data')
    result = fetch_data()
    assert result == 'mocked data'
    print(result)

medium

A. real data

B. Test fails with AssertionError

C. mocked data

D. None

Solution

1. Step 1: Understand patch effect on fetch_data

   The patch replaces fetch_data to always return 'mocked data'.

2. Step 2: Analyze test assertions and print

   The assertion checks for 'mocked data' and passes. The print outputs 'mocked data'.

3. Final Answer:

   mocked data -> Option C

4. Quick Check:

   Patched function returns mocked data [OK]

Hint: Patched functions return mocked values, not real ones [OK]

Common Mistakes:

• Expecting original function output
• Confusing patch target string
• Assuming patch does not affect print

4. Identify the error in the following test code using pytest-mock:

def test_api_call(mocker):
    mocker.patch('api.call', return_value=200)
    response = api.call()
    assert response == 200

medium

A. The return_value should be a string, not an integer

B. assert statement syntax is wrong

C. mocker.patch should be called after the function call

D. The patch target string 'api.call' is incorrect or incomplete

Solution

1. Step 1: Check patch target correctness

   The patch target 'api.call' must be the full import path. If 'api' is not imported or incomplete, patch fails.

2. Step 2: Validate other parts

   Return value as integer is valid, patch before call is correct, and assert syntax is fine.

3. Final Answer:

   The patch target string 'api.call' is incorrect or incomplete -> Option D

4. Quick Check:

   Patch target must be full import path [OK]

Hint: Always use full import path in patch target string [OK]

Common Mistakes:

• Using partial or wrong patch target strings
• Thinking return_value must be string
• Calling patch after function call

5. You want to test a function process_data() that calls fetch_from_api(). Using pytest-mock, how do you mock fetch_from_api to return different values on consecutive calls: first 'data1', then 'data2'?

hard

A. mocker.patch('module.fetch_from_api', return_value=['data1', 'data2'])

B. mocker.patch('module.fetch_from_api', side_effect=['data1', 'data2'])

C. mocker.patch('module.fetch_from_api', side_effect='data1', 'data2')

D. mocker.patch('module.fetch_from_api', return_values=['data1', 'data2'])

Solution

1. Step 1: Understand side_effect usage

   To return different values on consecutive calls, use side_effect with a list of return values.

2. Step 2: Identify correct syntax

   mocker.patch('module.fetch_from_api', side_effect=['data1', 'data2']) correctly uses side_effect=['data1', 'data2']. Options B, C, and D use incorrect parameters or syntax.

3. Final Answer:

   mocker.patch('module.fetch_from_api', side_effect=['data1', 'data2']) -> Option B

4. Quick Check:

   Use side_effect list for multiple return values [OK]

Hint: Use side_effect list to mock multiple return values [OK]

Common Mistakes:

• Using return_value with a list instead of side_effect
• Incorrect side_effect syntax with multiple arguments
• Using non-existent return_values parameter