Home Backend Development Python Tutorial Mocking Python Classes

Mocking Python Classes

Aug 28, 2024 pm 06:32 PM

Mocking Python Classes

Lately, I had to write unit tests using Pytest for a Python module. The module contains a class where other classes are initialize within its constructor.

As usual I created a fixture for this class to make it easy to write a test for each class method. At this point I ran into some issues when I tried to mock the different classes initiated in the constructor. The mocking didn't work, and instances of these classes were still being created.

After some research and combining a few different solutions I found online, I want to share how I managed to mock the classes.

Solution

Here is an example of the class I tried to mock:

class ClassA:
    def __init__(self):
        self.class_b = ClassB()
        self.class_c = ClassC()
        self.count = 0
Copy after login

We want to set a value for every field of this class during tests. This value can be None or a class mock, but we don't want initiations of the classes ClassB and ClassC.

In our case, let's decide that self.class_b and self.class_c should be mocks:

@pytest.fixture
def mock_class_b():
    class_b = Mock(spec=ClassB)
    return class_b

@pytest.fixture
def mock_class_c():
    class_c = Mock(spec=ClassC)
    return class_c
Copy after login

So a fixture for this class that serves our goal looks like this:

@pytest.fixture
def class_a_mock(mock_class_b, mock_class_c):
    with patch.object(target=ClassA, attribute="__init__", return_value=None) as mock_init:
        class_a = ClassA()
        class_a.class_b = mock_class_b
        class_a.class_c = mock_class_c
        class_b.count = 0
        return class_a

Copy after login

The important part is how to use the patch.object function, which is from unittest.mock module in Python. It is used in testing to temporarily replace an attribute of a given object wit a mock or another value.

Arguments

  1. target=ClassA: the object (usually a class) whose attribute we want to patch.
  2. attribute="__init__": the name of the attribute we want to patch.
  3. return_value=None: replacing the __init__ method with a function that does nothing

In this way we can create mocked variables in our fixture.
Read more about patch.object

Testing Tips

I wrote this tutorial for this kind of cases where, for any reason, we cannot change the code of Class A. However, I generally recommend modifying the code if possible, not to change the logic, but to make it more testable.

Here are some examples of how to modify Class A to make it more testable:

Option 1: Pass instances of class B and class C as parameters.
This way, when we write the fixture, we can pass mocks instead of instances.

class ClassA:
    def __init__(self, class_b_instance, class_c_instance):
        self.class_b = class_b_instance
        self.class_c = class_c_instance
        self.count = 0
Copy after login

Option 2: Create a Boolean variable that indicates test mode.
This way we can decide which fields of class A will or will not get a value when it is initiated.

class ClassA:
    def __init__(self, test_mode=False):
        if not test_mode:
            self.class_b = ClassB()
            self.class_c = ClassC()
        self.count = 0
Copy after login

Option 3: Make class initiations in a separate method.
This approach gives us the choice to avoid calling set_class_variables in the test module.

class ClassA:
    def __init__(self):
        self.class_b = None
        self.class_c = None
        self.count = None

    def set_class_variables(self):
        self.class_b = ClassB()
        self.class_c = ClassC()
        self.count = 0
Copy after login

Hope this helps! :)

The above is the detailed content of Mocking Python Classes. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

Java Tutorial
1664
14
PHP Tutorial
1268
29
C# Tutorial
1242
24
Python vs. C  : Applications and Use Cases Compared Python vs. C : Applications and Use Cases Compared Apr 12, 2025 am 12:01 AM

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

Python: Games, GUIs, and More Python: Games, GUIs, and More Apr 13, 2025 am 12:14 AM

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

The 2-Hour Python Plan: A Realistic Approach The 2-Hour Python Plan: A Realistic Approach Apr 11, 2025 am 12:04 AM

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

How Much Python Can You Learn in 2 Hours? How Much Python Can You Learn in 2 Hours? Apr 09, 2025 pm 04:33 PM

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python and Time: Making the Most of Your Study Time Python and Time: Making the Most of Your Study Time Apr 14, 2025 am 12:02 AM

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python: Automation, Scripting, and Task Management Python: Automation, Scripting, and Task Management Apr 16, 2025 am 12:14 AM

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python: Exploring Its Primary Applications Python: Exploring Its Primary Applications Apr 10, 2025 am 09:41 AM

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

See all articles