Python Unittest Mock

Summary: in this tutorial, you’ll learn about the Python unittest Mock class and how to use it to mock other classes.

Introduction to Python unittest Mock class

Mocks simulate the behaviors of real objects. To test an object that depends on other objects in an isolated manner, you use mock objects to mock the real objects.

To mock objects, you use the unittest.mock module. The unittest.mock module provides the Mock class that allows you to mock other objects.

It also provides the MagicMock class that is a subclass of the Mock class. Besides the methods and properties of the Mock class, the MagicMock class has the implementations of all the dunder methods e.g., __str__ and __repr__.

See the following example:

from unittest.mock import Mock # create a new mock object mock = Mock() # mock the api function mock.api.return_value = { 'id': 1, 'message': 'hello' } # call the api function print(mock.api())Code language: Python (python)

Output:

{'id': 1, 'message': 'hello'}Code language: Python (python)

How it works.

First, import the Mock class from the unittest.mock module:

from unittest.mock import MockCode language: Python (python)

Second, create a new instance of the Mock class:

mock = Mock()Code language: Python (python)

Third, mock the api() function and assign its return value to a dictionary:

mock.api.return_value = { 'id': 1, 'message': 'hello' }Code language: Python (python)

Finally, call the api() from the mock object. It’ll return the assigned value:

print(mock.api())Code language: Python (python)

In this example, we have two mock objects: mock & mock.api.

Let’s add the print() statement to the program to see how it works:

from unittest.mock import Mock # create a new mock object mock = Mock() print(mock) # mock the api function mock.api.return_value = { 'id': 1, 'message': 'hello' } print(mock.api) # call the api print(mock.api())Code language: Python (python)

Output:

<Mock id='1830094470496'> <Mock name='mock.api' id='1830100086416'> {'id': 1, 'message': 'hello'}Code language: Python (python)

The output shows two Mock objects.

In short, if you assign a property that doesn’t exist on the Mock object, Python will return a new mock object. Because of this dynamic, you can use the Mock class to mock any objects that you want.

When to use mock

These are cases that you may consider using mocks:

• System calls
• Networking
• I/O operation
• Clocks & time, timezones
• Or other cases whose results are unpredictable

Why using mocks

The following are benefits of mocks:

• Speed up the test
• Exclude external redundancies
• Make unpredictable results predictable

Python Unittest Mock example

Suppose you have a module called odometer.py:

from random import randint def speed(): return randint(40, 120) def alert(): s = speed() if s < 60 or s > 100: return True return FalseCode language: Python (python)

In the sensor.py module:

• The speed() returns the current speed of a vehicle. It returns a random value between 40 and 120. In the real world, the function would read the data from the odometer.
• The alert() function returns true if the current speed is lower than 60 km/ and higher than 120 km/h. The alert() function uses the speed() function to get the current speed.

It’ll be difficult to test the alert() function because the value returned by the speed() function is varied. To resolve it, you can use Mock class.

The following creates a test_odometer.py test module that tests the alert() function:

test_alert_normal (test_odometer.TestOdometer) ... ok ---------------------------------------------------------------------- Ran 1 test in 0.000s OKCode language: Python (python)

How it works.

First, assign a Mock object to the odometer.speed function:

odometer.speed = Mock()Code language: Python (python)

Second, set the return value of the speed() function to 70:

odometer.speed.return_value = 70Code language: Python (python)

Third, call the alert() function and test if it returns False. The alert() function will call the mock object instead of the actual speed() function.

The following picture illustrates how the test works with mock objects:

Run the test:

python -m unittest test_odometer.py -vCode language: Python (python)

Output:

test_alert_normal (test_odometer.TestOdometer) ... ok ---------------------------------------------------------------------- Ran 1 test in 0.000s OKCode language: Python (python)

The following adds the test cases that are over and under speed:

import unittest from unittest.mock import Mock import odometer class TestOdometer(unittest.TestCase): def test_alert_normal(self): odometer.speed = Mock() odometer.speed.return_value = 70 self.assertFalse(odometer.alert()) def test_alert_overspeed(self): odometer.speed = Mock() odometer.speed.return_value = 100 self.assertFalse(odometer.alert()) def test_alert_underspeed(self): odometer.speed = Mock() odometer.speed.return_value = 59 self.assertTrue(odometer.alert())Code language: Python (python)

Run the test:

python -m unittest test_odometer.py -vCode language: Python (python)

Output:

test_alert_normal (test_odometer.TestOdometer) ... ok test_alert_overspeed (test_odometer.TestOdometer) ... ok test_alert_underspeed (test_odometer.TestOdometer) ... ok ---------------------------------------------------------------------- Ran 3 tests in 0.001s OK