pytest-mock for enhanced mocking

Introduction

We use pytest-mock to easily replace parts of our code during tests. This helps us check how our code behaves without running everything for real.

When you want to test a function that calls an external service without actually calling it.

When you want to simulate a specific return value from a function to test different scenarios.

When you want to check if a function was called with the right arguments.

When you want to avoid slow or unreliable parts of your code during testing.

When you want to isolate the part of the code you are testing from others.

Syntax

PyTest

def test_example(mocker):
    mock_function = mocker.patch('module.function_to_mock')
    mock_function.return_value = 'mocked value'
    result = module.function_to_test()
    assert result == 'expected result'

The mocker fixture is provided by pytest-mock automatically.

Use mocker.patch() to replace the target function or object during the test.

Examples

This replaces math.sqrt to always return 3, no matter the input.

PyTest

import math

def test_mock_return_value(mocker):
    mocker.patch('math.sqrt', return_value=3)
    assert math.sqrt(16) == 3

This checks if func was called once with the argument 'hello'.

PyTest

def test_mock_called_with(mocker):
    mock_func = mocker.patch('module.func')
    module.call_func()
    mock_func.assert_called_once_with('hello')

This uses a side effect function to change the behavior of double during the test.

PyTest

def test_mock_side_effect(mocker):
    def side_effect(x):
        return x * 2
    mocker.patch('module.double', side_effect=side_effect)
    assert module.double(4) == 8

Sample Program

This test replaces math.sqrt to return 10 instead of 5. It checks if calculate() returns the mocked value.

PyTest

import math

def calculate():
    return math.sqrt(25)

def test_calculate(mocker):
    mocker.patch('math.sqrt', return_value=10)
    result = calculate()
    assert result == 10
    print('Test passed')

Important Notes

Always patch the function or object where it is used, not where it is defined.

pytest-mock works well with pytest and makes mocking simpler and cleaner.

Remember to keep mocks simple and only mock what is necessary for the test.

Summary

pytest-mock helps replace parts of code during tests easily.

Use mocker.patch() to mock functions or objects.

Check calls and return values to verify your code behavior.

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