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 Mock
Code 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 False
Code 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
function returns true if the current speed is lower than 60 km/ and higher than 120 km/h. Thealert()
function uses thealert()
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
OK
Code 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 = 70
Code language: Python (python)
Third, call the
function and test if it returns False. The alert()
function will call the mock object instead of the actual alert()
speed()
function.
The following picture illustrates how the test works with mock objects:
Run the test:
python -m unittest test_odometer.py -v
Code language: Python (python)
Output:
test_alert_normal (test_odometer.TestOdometer) ... ok
----------------------------------------------------------------------
Ran 1 test in 0.000s
OK
Code 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 -v
Code 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
Code language: Python (python)
Summary
- Use the
Mock
class of theunittest.mock
class to mock other objects.