What is Monkey Patching in Python: A Complete Tutorial With Examples
Tags: beginners, tutorial, python, productivity
Published: 2024-01-22 07:15:31


In the world of software testing, where the pursuit of efficient and reliable test automation is paramount, developers and testers often encounter challenges when working with existing codebases or third-party libraries. Modifying the behavior of these components without changing their source code can be daunting. For example, if you need to change a library function that you do not have access to the source code, you can do it using Monkey Patching.

Monkey Patching, a dynamic technique in Python, allows developers and quality assurance engineers (QAEs) to make runtime modifications to classes, objects, or even built-in modules. With its ability to address common pain points in [test automation](https://www.lambdatest.com/learning-hub/automation-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub), Monkey Patching in Python has become an invaluable tool in the arsenal of testers and developers.

One of the main problems that Monkey Patching solves in test automation is the need for flexibility and adaptability when working with external libraries or frameworks. When utilizing popular [automation testing tools](https://www.lambdatest.com/blog/automation-testing-tools/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) such as [Selenium](https://www.lambdatest.com/selenium?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) for web testing, QAEs often encounter situations requiring additional functionalities or modifications to suit their specific test scenarios.

Monkey Patching allows testers to augment existing functionality, add custom methods, or even modify the behavior of [Selenium WebDriver](https://www.lambdatest.com/learning-hub/webdriver) on the fly without having to modify the original source code. For example, if you need some special behavior when clicking on a button, you can use Monkey Patching to change *Click* function behavior to implement your desired behavior. This empowers testers to tailor automation workflows to their precise requirements, leading to more robust and efficient test suites.

In this Selenium [Python tutorial](https://www.lambdatest.com/learning-hub/python-tutorial?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub), we will deep dive into Monkey Patching in Python using Selenium, which can help test automation professionals overcome the limitations imposed by external dependencies and achieve greater control and customization, resulting in improved test coverage and higher confidence in the quality of their software applications.

Let’s move on! Stay tuned!
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## What is Monkey Patching?

Monkey Patching introduces a dynamic technique that developers and QAEs can employ to modify the behavior of existing classes, objects, or modules during runtime, without making any direct change to their source code. This approach enables a remarkable level of flexibility and adaptability, empowering them to extend the functionalities of pre-existing components, fix bugs, and customize behaviors to cater to specific requirements.

It involves the dynamic addition, modification, or replacement of attributes, methods, or functions within existing objects at runtime. This entails the ability to manipulate the behavior of an object or a module without directly accessing or modifying its source code.

The term “Monkey Patching” derives its name from the idea of making spontaneous changes to the code, allowing them to experiment with modifications on the fly without adhering to traditional inheritance or modifying the original codebase. For example, imagine that there is a library called *test_automation_metrics* that provides some metrics for automated tests. If you need to implement a new metric called *test_duration_by_endpoint*, you can do it without needing to change the library itself.
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## Advantages and Shortcomings of Monkey Patching

When it comes to Monkey Patching in Python, there are some advantages and shortcomings to consider. Here are some benefits:

* **Enhanced Functionality**

* **Bug Fixes and Missing Features**

* **Code Reusability**

Despite the benefits of using Monkey Patching, here are some of the shortcomings:

* **Unintended Side Effects**

* **Testing Complexity**

In summary, Monkey Patching offers advantages such as enhanced functionality, bug fixes, and code reusability. However, it also introduces challenges related to unintended side effects and testing complexity. By understanding and carefully considering these advantages and shortcomings, developers and QAEs can make good decisions about when and how to apply Monkey Patching effectively in their projects.

## Understanding Python Objects and Classes

Python, as an object-oriented programming language, revolves around the core principles of objects and classes. To fully comprehend the significance of Monkey Patching in Python, it is imperative to grasp these foundational concepts.

## Objects and Instances

In Python, an object denotes an instance of a class. Objects serve as the embodiment of data and behavior, allowing developers to model real-world entities or abstract concepts within their code. Each object is associated with a specific class, which serves as a blueprint or template outlining the attributes and methods the object possesses.

Creating an object from a class results in an instance. Multiple instances can be generated from a single class, each representing a distinct entity with its attribute values.

For instance, in the context of test automation, consider a class called “*TestSuite*.” By instantiating multiple test suite objects, developers can represent individual [test suites](https://www.lambdatest.com/learning-hub/test-suite?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub), each characterized by unique properties such as [test cases](https://www.lambdatest.com/learning-hub/test-case?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub), test data, and configurations.

These test suite objects serve as a means to organize and execute tests, allowing for efficient and systematic testing of software applications.

    class TestSuite:
        def __init__(self, name, test_cases, test_data, configuration):
            self.name = name
            self.test_cases = test_cases
            self.test_data = test_data
            self.configuration = configuration
       
        def run_tests(self):
            # Logic to execute the test cases in the test suite
            print("Running tests in", self.name)
            for test_case in self.test_cases:
                print("Running test case:", test_case)
                # Execute the test using the data and configuration
    
    
    # Example of creating test suite objects
    # Test Suite 1
    test_cases_1 = ["Test Case 1", "Test Case 2", "Test Case 3"]
    test_data_1 = {"input": "data1", "expected_output": "result1"}
    configuration_1 = {"timeout": 10, "retries": 3}
    
    
    suite_1 = TestSuite("Test Suite 1", test_cases_1, test_data_1, configuration_1)
    
    
    # Test Suite 2
    test_cases_2 = ["Test Case 4", "Test Case 5"]
    test_data_2 = {"input": "data2", "expected_output": "result2"}
    configuration_2 = {"timeout": 5, "retries": 2}
    
    
    suite_2 = TestSuite("Test Suite 2", test_cases_2, test_data_2, configuration_2)
    
    
    # Running the test suites
    suite_1.run_tests()
    suite_2.run_tests()

In the above code, the *TestSuite* class represents a test suite object. It has an *__init__* method that initializes the test suite with a name, a list of test cases, test data, and configuration. The class also includes a *run_tests* method that simulates the execution of the test cases within the test suite.

The example demonstrates the creation of two test suite objects, *suite_1* and *suite_2*, with different sets of test cases, test data, and configurations. The *run_tests* method is then called on each test suite object to execute the respective test cases.

![](https://cdn-images-1.medium.com/max/2200/0*e0R6poQYrEKVGxxp.png)
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## Classes and Inheritance

In Python, a class represents a blueprint defining the structure and behavior of objects. It encapsulates attributes (variables) and methods (functions) that outline the characteristics and actions associated with the objects instantiated from the class. Classes provide a means to organize and structure code by encapsulating related data and functionality.

Inheritance, a powerful feature of object-oriented programming, allows classes to inherit attributes and methods from other classes. It facilitates code reuse and establishes a hierarchical relationship among classes. In Python, a class can inherit from another class, known as the base or parent class. The class inherited from the parent class is referred to as the derived or child class. By inheriting the attributes and methods of the parent class, the derived class gains the ability to extend and specialize the behavior defined in the parent class.

Inheritance enables the creation of specialized classes that inherit and augment the behavior inherited from more general classes.

For instance, in the context of test automation, consider a base class named *TestSuite* and derived classes such as *RegressionTestSuite*, *SmokeTestSuite*, and *PerformanceTestSuite*. The base class, *TestSuite*, encapsulates the common attributes and methods required for executing test suites.

On the other hand, the derived classes inherit these common functionalities from the base class while introducing unique behaviors tailored to specific tests.

In this scenario, the *RegressionTestSuite *class can inherit the attributes and methods from the *TestSuite* base class, enabling it to execute [regression tests](https://www.lambdatest.com/learning-hub/regression-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub) that focus on verifying the overall system functionality after changes or updates. Similarly, the *SmokeTestSuite *class can inherit the base class and implement behaviors specifically designed for quick, high-level tests to identify critical issues. Additionally, the *PerformanceTestSuite* class can inherit the base class and introduce functionalities for conducting performance tests to evaluate the system’s response and stability under varying loads.

    class TestSuite:
        def __init__(self, name):
            self.name = name
    
    
        def run_tests(self):
            print("Running tests in", self.name)
    
    
    class RegressionTestSuite(TestSuite):
        def run_tests(self):
            print("Running regression tests in", self.name)
            # Additional regression test logic
    
    
    class SmokeTestSuite(TestSuite):
        def run_tests(self):
            print("Running smoke tests in", self.name)
            # Additional smoke test logic
    
    
    class PerformanceTestSuite(TestSuite):
        def run_tests(self):
            print("Running performance tests in", self.name)
            # Additional performance test logic
    
    
    # Example of creating objects and using functions
    regression_suite = RegressionTestSuite("Regression Test Suite")
    smoke_suite = SmokeTestSuite("Smoke Test Suite")
    performance_suite = PerformanceTestSuite("Performance Test Suite")
    
    
    regression_suite.run_tests()
    smoke_suite.run_tests()
    performance_suite.run_tests()

In the above code, the *TestSuite* class serves as the base class, while the *RegressionTestSuite*, *SmokeTestSuite*, and *PerformanceTestSuite* classes are derived classes inherited from the base class. Each derived class overrides the *run_tests* method to provide its specific implementation.

In the example, we create objects of each class: *regression_suite*, *smoke_suite*, and *performance_suite*. When you call the *run_tests* method on each object, the appropriate implementation from the corresponding derived class is executed. This demonstrates the use of inheritance, where the derived classes inherit the common *run_tests* method from the base class but provide their specialized behavior.

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## Method Resolution Order (MRO)

Method Resolution Order (MRO) in Python governs the sequence in which methods are resolved and invoked in the presence of inheritance. When a method is called on an object, Python searches for it by traversing through the object’s class and progressing up the inheritance hierarchy until it finds the method or reaches the topmost parent class.

Python determines the MRO using the C3 linearization algorithm, which establishes a consistent and predictable order for method resolution. Developers can access the MRO order by utilizing the *__mro__* attribute on a class or employing the *mro()* method.

Understanding the MRO plays a pivotal role in the application of Monkey Patching as it guides the placement of patched methods within the method resolution order. Monkey Patching allows you to add, modify, or replace methods at runtime dynamically. The MRO ensures that the appropriate method is invoked when an object calls a method.

    class BaseTest:
        def run_test(self):
            print("Running base test")
    class WebTest(BaseTest):
        def run_test(self):
            print("Running web test")
    class MobileTest(BaseTest):
        def run_test(self):
            print("Running mobile test")
    class HybridTest(WebTest, MobileTest):
        pass
    
    
    # Creating an object of HybridTest
    test = HybridTest()
    # Calling the run_test method
    test.run_test()
    # Accessing the MRO
    print(HybridTest.__mro__)

In the above code, we have a base class called *BaseTest* with a method *run_test()*. We then define two derived classes, *WebTest* and *MobileTest*, which override the *run_test()* method with their implementations. Finally, we create a third derived class, *HybridTest*, which inherits from both *WebTest* and *MobileTest* without implementing its version of *run_test()*.

When we create an object test of *HybridTest* and call the *run_test()* method on it, Python follows the MRO to determine which implementation of *run_test()* should be invoked. In this case, the MRO is [*HybridTest, WebTest, MobileTest, BaseTest, object*], and Python resolves the method in the order from left to right. Hence, the output will be:

![](https://cdn-images-1.medium.com/max/2212/0*ioVf1GrNWtxLgW-y.png)
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Additionally, you can access the MRO by accessing the *__mro__* attribute on the class, as demonstrated in the code. This allows developers to understand the order in which methods will be resolved when dealing with multiple inheritance scenarios in the context of test automation.

The output (<*class ‘__main__.HybridTest’*>, <*class ‘__main__.WebTest’*>, <*class ‘__main__.MobileTest’*>, <*class ‘__main__.BaseTest’*>, <*class ‘object’*>) represents the Method Resolution Order (MRO) for the *HybridTest* class.

In the MRO, the classes are listed in the order in which they will be searched for a method when it is called on an instance of *HybridTest*. Let’s break down the MRO:

 1. <*class ‘__main__.HybridTest’*>: This is the class itself, *HybridTest*, which is the starting point of the MRO. It is the first class to be searched for the method.

 2. <*class ‘__main__.WebTest’*>: This is the first parent class of *HybridTest* in the inheritance hierarchy. When a method is not found in *HybridTest*, Python will look for it in *WebTest* next.

 3. <*class ‘__main__.MobileTest’*>: This is the second parent class of *HybridTest*. If the method is not found in *WebTest*, Python will continue the search in *MobileTest*.

 4. <*class ‘__main__.BaseTest’*>: This is the next parent class in the hierarchy. If the method is not found in *MobileTest*, Python will look for it in *BaseTest*.

 5. <*class ‘object’*>: This is the ultimate parent class in Python and is always at the end of the MRO. If the method is not found in any previous classes, Python will search for it in the object class.

So, when you call a method on an instance of *HybridTest*, Python will first look for it in *HybridTest*. If it is not found, it will search in *WebTest*, then *MobileTest*, *BaseTest*, and finally object if necessary.

By gaining a profound understanding of Python’s object-oriented concepts, including objects, instances, classes, inheritance, and method resolution order, developers and QAEs can establish a solid foundation for effectively employing Monkey Patching techniques. These concepts provide the necessary groundwork for modifying and extending the behavior of Python objects, enabling developers to adapt and enhance existing code to suit specific requirements.
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## Essential Concepts of Monkey Patching in Python

In this section, we will delve into the essential concepts of Monkey Patching in Python, understanding its underlying mechanics, best practices, and its implications on code maintainability and reliability.

## Modifying Existing Classes and Objects

One of the primary use cases of Monkey Patching in Python is modifying existing classes and objects to suit specific testing needs. Let’s consider an example where you have a base test suite class called *TestSuite* that provides a common set of methods for running tests. However, we want to extend this class to add custom functionality specific to regression tests.

In the above example, we have a base *TestSuite* class with a *run_tests()* method that prints a generic message. To modify this behavior for regression tests, we define a derived class called *RegressionTestSuite* that inherits from *TestSuite* and overrides the *run_tests()* method. Within the overridden method, we add the specific regression test logic.

    class TestSuite:
        def run_tests(self):
            print("Running tests in the base TestSuite")
    
    
    # Derived class: RegressionTestSuite
    class RegressionTestSuite(TestSuite):
        def run_tests(self):
            print("Running regression tests")

By using Monkey Patching in Python, you can make these modifications at runtime without altering the source code. This allows you to tailor the behavior of the *TestSuite* class for regression testing while maintaining the flexibility to use the original *TestSuite* implementation for other types of tests.

## Extending Functionality

Another aspect of Monkey Patching in Python is the ability to extend the functionality of existing classes and objects. This allows you to dynamically add new methods or attributes to support our testing requirements better. Let’s continue with our previous example and add a new method to the *RegressionTestSuite* class.

    def new_regression_test_method(self):
        print("Executing new regression test method")
    RegressionTestSuite.new_method = new_regression_test_method

In the above code, we define a new function called *new_regression_test_method()* that represents the additional functionality we want to introduce to the *RegressionTestSuite* class. We then assign this function to the new_method attribute of the *RegressionTestSuite* class. This effectively extends the functionality of the class by adding a new method.

Now, when we instantiate a *RegressionTestSuite* object, we can call this new method alongside the existing methods inherited from the base class.

    regression_suite = RegressionTestSuite()
    regression_suite.run_tests()  # Existing overridden method
    regression_suite.new_method()  # Newly added method

By leveraging Monkey Patching, you can seamlessly introduce new functionality to existing classes, enabling you to tailor our test suites to specific requirements without modifying the original class definition. This flexibility enhances code reuse and promotes a more modular and maintainable test automation framework.
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## Monkey Patching Techniques

Monkey Patching, as we have discussed, offers developers and QAEs the ability to modify or extend classes and objects at runtime. In this section, we will delve into specific techniques of Monkey Patching in Python and explore their applications in test automation. We will cover attribute overriding, method overriding, and patching built-in classes and objects.

## Attribute Overriding

Attribute overriding involves dynamically modifying the attributes of a class or object. This technique allows you to change the values of existing attributes or add new attributes to customize the behavior of the class or object.

In the context of test automation, attribute overriding can be beneficial when you want to modify the behavior of test cases or test suites dynamically. Let’s consider an example where you have a base test case class called *BaseTestCase* with a common attribute named *timeout*. However, we want to override this attribute for specific test cases that require a longer *timeout*, for example, when rendering a huge amount of data on the screen.

    class BaseTestCase:
        timeout = 10  # Common timeout value for test cases
    
    
    # Derived class: CustomTestCase
    class CustomTestCase(BaseTestCase):
        pass
    
    
    # Monkey patching: Attribute overriding
    CustomTestCase.timeout = 20  # Override timeout for specific test cases

In this example, we derive a class *CustomTestCase* from *BaseTestCase*. By utilizing Monkey Patching, we override the timeout attribute of *CustomTestCase* to assign a different value. This allows us to customize the timeout value for specific test cases without modifying the original class.

## Method Overriding

Method overriding involves modifying the behavior of a method in a class or object. This technique enables you to replace the implementation of a method with customized logic.

In the realm of test automation, method overriding can be useful when you want to modify or extend the functionality of test cases or test suites. Let’s consider a scenario with a base test case class called *BaseTestCase* with a common method named *run_test()*. However, we want to override this method for specific test cases that require additional setup or teardown steps.

    from selenium import webdriver
    
    
    # Base test case class
    class BaseTestCase:
        def run_test(self):
            # Instantiate WebDriver
            driver = webdriver.Chrome()
    
    
            # Common test execution logic
            driver.get("https://www.example.com")
    
    
            # Close the browser
            driver.quit()
    
    
    
    
    # Derived class: CustomTestCase
    class CustomTestCase(BaseTestCase):
        def run_test(self):
            # Additional setup steps
            driver = webdriver.Chrome()
            driver.maximize_window()
    
    
            # Call base class method
            super().run_test()
    
    
            # Additional teardown steps
            driver.close()

In this example, the *CustomTestCase* class inherits from *BaseTestCase*. By utilizing method overriding through Monkey Patching, we override the *run_test()* method of *CustomTestCase* to introduce additional setup and teardown steps. This allows us to customize the behavior of specific test cases while preserving the original logic defined in the base class.
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## Patching Built-in Classes and Objects

Monkey Patching in Python is not limited to user-defined classes and objects; it can also be applied to modify the behavior of built-in classes and objects in Python. This capability provides immense flexibility and allows you to extend the functionality of existing built-in classes or objects to suit our testing needs.

Consider a scenario where you are [automating web testing](https://www.lambdatest.com/automated-web-testing-platform?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) using Python and Selenium. You may encounter situations where you want to customize or enhance the behavior of Selenium classes or objects. Monkey Patching allows you to achieve this without modifying the original Selenium source code.

For example, let’s say you want to add a custom method named highlight_element() to the Selenium WebElement class, which highlights the web element on the page for better visibility during [test execution](https://www.lambdatest.com/learning-hub/test-execution?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub). You can achieve this through Monkey Patching:

    from selenium.webdriver.remote.webelement import WebElement
    
    
    # Monkey patching: Adding a custom method to the WebElement class
    def highlight_element(self):
        self._execute_script("arguments[0].style.border='2px solid red';", self)
    
    
    WebElement.highlight_element = highlight_element

In this example, we import the WebElement class from the
selenium.webdriver.remote.webelement module. By utilizing Monkey Patching, we add a custom method named highlight_element() to the WebElement class, which modifies the element’s border style to highlight it on the web page.

This technique of Monkey Patching built-in classes or objects allows us to extend the functionality of existing libraries or frameworks to fulfill specific testing requirements without the need for direct modification or forking of the source code.

In conclusion, Monkey Patching in Python provides powerful techniques for modifying or extending classes and objects dynamically at runtime. In the context of test automation, it offers flexibility and customization options to tailor the behavior of test cases, test suites, or even built-in classes and objects.

By understanding and leveraging the concepts of attribute overriding, method overriding, and patching built-in classes and objects, developers, and testers can effectively harness the potential of Monkey Patching in Python to enhance their test automation frameworks and achieve more efficient and customized testing.

## Real-World Examples of Monkey Patching

Monkey Patching in Python is a versatile technique with numerous practical applications in various domains. In this section, you will explore the real-world use cases of Monkey Patching, focusing on its applications in the context of test automation, patching third-party libraries, extending the Python Standard Library, and implementing custom Monkey Patching examples.

## Monkey Patching for Test Automation

Monkey Patching plays a significant role in test automation, offering valuable opportunities to customize and enhance [automation testing frameworks](https://www.lambdatest.com/blog/automation-testing-frameworks/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) and tools. Let’s delve into some examples:

## Test Data Customization

Monkey Patching in Python allows you to modify data generation functions or classes within testing frameworks. This customization ensures that the generated test data meets specific constraints or conditions for testing different scenarios.

For instance, let’s consider a scenario where a testing framework generates random email addresses for test cases.

    import random
    import string
    
    
    def generate_email():
        username_length = random.randint(5, 10)
        username = ''.join(random.choices(string.ascii_lowercase, k=username_length))
        domain = random.choice(['example.com', 'test.com', 'sample.com'])
        return f"{username}@{domain}"

The *generate_email* function generates a random email address by randomly selecting a username of a random length between 5 and 10 characters from lowercase alphabets and a domain from a predefined list. The generated email address is then returned as a string.

However, we want to ensure that the generated email addresses are from a specific domain for a particular set of tests. You can achieve this by Monkey Patching the data generation function to override the domain part of the email address with the desired domain. Here’s a Python code example:

    def generate_custom_email():
        email = generate_email()
        # Modify the domain part of the email address
        custom_email = email.replace('@example.com', '@customdomain.com')
        print(custom_email)
        return custom_email

You can use the *generate_email()* function in your code to provide the update in the implementation. Then, in your automation code, you can do just this:

    import random_email_generator
    # Call the modified generate_custom_email function
    random_email_generator.generate_custom_email()

Now, whenever the testing framework generates an email address, it will use our custom function, ensuring that the generated email addresses have the desired domain.
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### Enhanced Test Reporting

By patching reporting functions or classes, you can customize the format, content, or destination of [test reports](https://www.lambdatest.com/learning-hub/test-reports?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub) based on project requirements. This flexibility enables you to generate reports in different formats or integrate them with external systems.

Let’s say you have a testing framework that [generates HTML reports](https://www.lambdatest.com/blog/webdriverio-html-reporter/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) by default, but you want to generate reports in a PDF format. You can achieve this by Monkey Patching the reporting function to use a PDF generation library. Here’s a Python code example using the reporting module:

    #reporting and pdf_generator are fictitious libraries for didactic purposes
    import reporting
    from pdf_generator import generate_pdf_report
    
    
    def generate_pdf_report_custom():
        # Generate the PDF report using a PDF generation library
        data = reporting.get_test_results()
        complete_data = reporting.addToHistoric(data)
        generate_pdf_report(complete_data, 'custom_report.pdf')
    
    
    # Monkey patch the reporting function
    reporting.generate_report = generate_pdf_report_custom

Now when the testing framework generates a test report, it will invoke our custom function, which generates a PDF report instead of the default HTML report.

### Framework Extensions

Monkey Patching in Python enables you to extend the functionality of existing test frameworks. By patching test case classes or test runner classes, you can introduce additional features, specialized assertions, or custom execution logic tailored to project-specific needs.

Let’s consider a scenario where you have a test framework that lacks a specific assertion for verifying the presence of an element on a web page. You can use Monkey Patching in Python to add this assertion to the framework. Here’s a Python code example:

    #test_framework is a fictitious library for didactic purposes
    import test_framework
    
    
    def assert_element_present(self, locator):
        if not self.is_element_present(locator):
            raise AssertionError(f"Element {locator} is not present on the page")
    
    
    # Monkey patch the test case class
    test_framework.TestCase.assert_element_present = assert_element_present

Now, all test cases derived from the *TestCase* class in the test_framework will have the *assert_element_present* method available for verifying the presence of elements on web pages.

These examples demonstrate how Monkey Patching in Python empowers test automation engineers to adapt their testing frameworks and tools to the unique demands of their projects.
>  **Dive into Appium tutorial and uncover the advantages of [appium automation testing](https://www.lambdatest.com/appium?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) tool. Learn how to effectively test your mobile applications with Appium.**

## Patching Third-Party Libraries

Monkey Patching in Python also finds significant applications in patching third-party libraries, such as Selenium, Requests, and pytest. When working with external libraries or frameworks, there are instances where customization or bug fixes are necessary. However, directly modifying library code is often impractical. In such cases, Monkey Patching provides an elegant solution.

By dynamically modifying the behavior of third-party library classes or functions, you can extend or fix functionalities without altering the library’s source code directly. This approach enables you to temporarily overcome limitations or bugs in the library and adapt it to our specific needs.

**Example:**

Consider a scenario where an API testing library exhibits a bug in handling response headers. Instead of waiting for an official update or maintaining a separate library fork, you can temporarily apply Monkey Patching to address the issue. You can fix the bug until an official fix becomes available by patching the library’s response handling functions.

## Patching Python Standard Library

Monkey Patching in Python can also be employed to modify or extend the behavior of the Python Standard Library itself. The Python Standard Library offers vast modules and functionalities for diverse purposes. However, there are situations where customization or enhancement of certain standard library modules becomes necessary to fulfill specific requirements.

One common use case involves patching the logging module. Although the logging module provides extensive logging capabilities, its default behavior may not align with project-specific logging requirements. Monkey Patching in Python allows us to modify the logging module’s behavior, such as altering the log message format, redirecting logs to different destinations, or introducing additional log levels.

Patching the Python Standard Library requires careful consideration, as it can impact the behavior of other modules or third-party libraries that depend on standard library modules. Thorough testing and documentation are crucial when applying Monkey Patching to the Python Standard Library.

In conclusion, Monkey Patching in Python is a versatile technique with practical applications in real-world scenarios. Whether it is customizing test automation frameworks, patching third-party libraries, extending the Python Standard Library, or implementing custom modifications, Monkey Patching offers flexibility and adaptability. However, it should be used responsibly, carefully considering its impact on code readability, maintainability, and compatibility.

With a solid understanding of its applications and limitations, developers and QAEs can leverage Monkey Patching in Python effectively to tailor their code and achieve desired outcomes.
>  **With numerous programming languages coming up for test automation, choosing one can be a tough call. Which is why we are highlighting the top 7 [automation testing languages](https://www.lambdatest.com/blog/top-7-programming-languages-for-test-automation-in-2020/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) to look out for test automation in 2020.**

## Monkey Patching vs. Inheritance and Decorators

When it comes to extending and customizing code in Python, developers and QAEs have multiple techniques at their disposal. Monkey Patching, Inheritance, and Decorators are among the most powerful tools. While each method serves the purpose of modifying code, they differ in their approach and application.

Let’s delve into the distinctions and explore their respective use cases.

## Comparing Monkey Patching with Inheritance

Inheritance is a cornerstone of object-oriented programming. It allows classes to inherit attributes and methods from their parent classes, creating a hierarchical structure. This approach promotes code reusability and encourages a structured approach to code organization. Inheritance is ideal when the goal is to build upon an existing class, extending its functionality while maintaining a clear and organized codebase.

On the other hand, Monkey Patching takes a different approach. It involves dynamically modifying existing code at runtime, enabling developers and QAEs to add, modify, or replace attributes or methods of classes or objects. Monkey Patching in Python offers flexibility and agility, allowing on-the-fly changes without creating new classes or directly modifying the source code. It is particularly useful for making temporary or localized modifications to existing code.

The choice between Monkey Patching and Inheritance depends on the specific requirements and constraints of the project. Inheritance shines when the goal is to establish a hierarchical relationship between classes and maintain a structured codebase. It promotes code clarity and maintainability, making it easier to understand and extend.

On the other hand, Monkey Patching in Python is suitable for situations where localized modifications or quick fixes are needed. It offers the flexibility to modify code behavior dynamically, making it well-suited for ad-hoc changes or addressing specific issues without extensive code restructuring. However, it’s important to exercise caution with Monkey Patching as it can introduce complexity and make code harder to debug.

Let’s look at the code sample below, which shows the difference between Inheritance and Monkey Patching.

    # Base test class
    class BaseTest:
        def run_test(self):
            print("Running base test...")
    
    
    # Derived test class inheriting from BaseTest
    class SmokeTest(BaseTest):
        def run_test(self):
            print("Running smoke test...")
    
    
    # Monkey patching the BaseTest class
    def monkey_patch_run_test(self):
        print("Monkey patch run test")
    
    
    BaseTest.run_test = monkey_patch_run_test
    
    
    # Creating instances and running tests
    base_test = BaseTest()
    smoke_test = SmokeTest()
    
    
    base_test.run_test()   # Output: Monkey patch run test
    smoke_test.run_test()  # Output: Running smoke test... 

In this code, we have a base test class called *BaseTest* and a derived test class called *SmokeTest*. The *BaseTest* class has a *run_test* method that represents a generic test execution. The *SmokeTes*t class inherits from BaseTest and overrides the *run_test* method to perform [smoke testing](https://www.lambdatest.com/learning-hub/smoke-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub) specific actions.

We then perform Monkey Patching by defining a new function *monkey_patch_run_test*, and assigning it to the *run_test *attribute of the *BaseTest* class. This modifies the behavior of the *BaseTest* class at runtime. As a result, when we create an instance of *BaseTest* and invoke the *run_test* method, it now executes the *monkey_patch_run_test* instead of the original implementation.

The code also demonstrates that the derived class *SmokeTest* remains unaffected by the monkey patching. It still implements the *run_test* method, showcasing the contrast between Monkey Patching and Inheritance.

When executing this code, we get the below result:

![](https://cdn-images-1.medium.com/max/2000/0*aETUYL_iH54SNM77.png)
>  **Learn why Python is the top choice for automation testing. This comprehensive tutorial provides a step-by-step guide to [automation testing in python](https://www.lambdatest.com/blog/python-automation-testing/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) to help you streamline your testing process.**

## Comparing Monkey Patching with Decorators

Another popular technique in Python is Decorators. Decorators allow the modification or extension of functions or classes by wrapping them with additional functionality. Unlike Monkey Patching, Decorators do not directly modify existing code; instead, they provide a way to augment the behavior of functions or classes.

Decorators are particularly useful when modifications must be consistently applied across multiple functions or classes. They promote code reuse and separation of concerns by allowing developers to encapsulate cross-cutting concerns in reusable decorators. Decorators enhance code readability and maintainability by providing a modular approach to code customization.

Monkey Patching in Python, on the other hand, is more suitable when modifications are specific to individual functions or classes and do not require a global application. It allows for quick and localized changes without the need to modify the source code. However, it’s important to use Monkey Patching judiciously to avoid introducing unnecessary complexity and potential conflicts.

Let’s look at the code sample below, where the difference between Decorators and Monkey Patching is shown.

    # Decorator function for test logging
    def log_test(func):
        def wrapper():
            print("Logging test...")
            return func()
        return wrapper
    
    
    # Test function with decorator
    @log_test
    def perform_test():
        print("Performing test...")
    
    
    # Running the original test
    perform_test()  # Output: Logging test... Performing test...
    
    
    # Monkey patching a test function
    def monkey_patch_test():
        print("Monkey patch test")
    
    
    perform_test = monkey_patch_test
    
    
    # Running the modified test
    perform_test()  # Output: Monkey patch test

In this code, we define a Decorator function called *log_test* that wraps a test function with logging functionality. We apply the Decorator to the *perform_test* function using the @ syntax. The *perform_test* function is modified to include test logging actions.

When you run *perform_test()*, it executes the code inside the *log_test* decorator and the code inside the *perform_test()* itself.

In contrast to Decorators, we demonstrate Monkey Patching in Python by defining a new function *monkey_patch_test*, and assigning it to the *perform_test* variable. This dynamically modifies the behavior of the *perform_test* function.

When you run *perform_test()*, it executes the Monkey Patch function instead of the original implementation. This highlights the difference between Decorators, which provide a clean and reusable way to modify test functions, and Monkey Patching, which allows for direct and localized changes to existing test code.

When executing this code, we get the below result:

![](https://cdn-images-1.medium.com/max/2000/0*aBRM19aqSx_ua37c.png)
>  **Perform live mobile [website device service](https://www.lambdatest.com/test-site-on-mobile?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) tests on different devices with LT Browser 2.0. Test on multiple mobile and tablet viewports and debug UI issues on the go across 50+ device viewports!**

## Choosing the Right Approach

Choosing the right approach — Monkey Patching, Inheritance, or Decorators — depends on several factors. Consider the following points to make an informed decision:

* **Code Structure**

If the goal is to build upon an existing class or establish a structured hierarchy, inheritance is a suitable choice. It provides a clear and organized approach to code organization.

* **Reusability**

If modifications need to be consistently applied across multiple functions or classes, decorators offer a reusable and modular solution. They promote code reuse and encapsulate cross-cutting concerns effectively.

* **Dynamic Modifications**

If the modifications are temporary, localized, or require runtime changes, Monkey Patching in Python provides the flexibility to modify code behavior on-the-fly. For example, if you need a third-party library to have different behavior depending on your software flow, Monkey Patching is the best choice for being used.

Monkey Patching, Inheritance, and Decorators are powerful techniques in Python that allow for code customization and extension. Each approach has its strengths and weaknesses, and choosing the right one depends on the specific requirements and constraints of the project. By carefully considering factors such as code structure, reusability, dynamic modifications, and maintainability, you can make informed decisions to achieve code flexibility and maintainable solutions.

## Monkey Patching Best Practices

When it comes to Monkey Patching, understanding the limitations and risks involved is crucial. While Monkey Patching in Python provides the flexibility to modify code at runtime, it can introduce complexities and potential issues if not approached carefully. In this section, we will explore some best practices to help ensure the effective and safe use of Monkey Patching in Python. 
>  **Perform live mobile website tests on different devices with LT Browser 2.0. Test on multiple mobile and tablet viewports and debug UI issues on the go across 50+ device viewports, [view website on different devices](https://www.lambdatest.com/test-site-on-mobile?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage)!**

## Limitations and Risks

Before diving into Monkey Patching in Python best practices, it is important to be aware of its limitations and the risks it poses. Here are a few key considerations:

* **Compatibility Concerns**

Monkey Patching in Python relies on internal details of the code being patched. Changes to the original codebase can break the patched functionality, leading to compatibility issues. It is crucial to thoroughly understand the codebase and consider the implications of the patches to ensure compatibility.

* **Maintenance Challenges**

Monkey Patching in Python can make codebases harder to maintain. Dynamic changes introduced through patching can decrease code readability and complicate debugging. It is advisable to document and communicate the presence of Monkey Patches to facilitate collaboration among developers.

* **Testing Complexity**

Testing Monkey-Patched code can be more challenging. Since the patched behavior may not be evident in the original codebase, comprehensive [test coverage](https://www.lambdatest.com/learning-hub/test-coverage?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub) is essential. Additionally, it is important to test the patched functionality to avoid introducing new bugs thoroughly.

* **Dependency Management**

Monkey Patching in Python can introduce dependencies on specific versions or implementations of libraries or frameworks. This can result in compatibility issues when upgrading or switching dependencies. Careful management and documentation of these dependencies are necessary to prevent future complications.

* **Unintended Side Effects**

Modifying code dynamically through Monkey Patching can lead to unintended side effects or conflicts. Changes in object behavior can cause unexpected issues in other parts of the codebase. Extensive testing and validation of the patched functionality are crucial to minimize the risk of unintended consequences.

## Guidelines for Effective Monkey Patching

To ensure the effective and safe use of Monkey Patching in Python, consider the following guidelines:

* **Deep Understanding**

Gain a thorough understanding of the codebase and its internal structure. This knowledge will help you identify suitable areas for Monkey Patching in Python and reduce the chances of compatibility problems.

* **Documentation and Communication**

Document your Monkey Patching implementations, including the reasons behind the patches and any dependencies introduced. Communicate these changes to the development team to facilitate understanding and collaboration.

* **Comprehensive Testing**

Invest in comprehensive testing to validate the functionality introduced through Monkey Patching in Python. Create specific test cases to cover the patched behavior and ensure that the patches do not introduce new bugs. Automated testing frameworks can streamline the testing process and provide consistent results.

* **Scoped Patches**

Limit the scope of your patches to specific areas of the codebase. Avoid making broad changes that can increase the risk of unintended side effects. Focused patches are easier to track and maintain.

* **Consider Alternatives**

Before resorting to Monkey Patching in Python, explore alternative approaches such as inheritance or decorators. These techniques might offer cleaner and more maintainable solutions in certain scenarios.

* **Version Control**

Utilize version control systems to track and manage changes made through Monkey Patching in Python. This allows for easy reversion or tracking of modifications if issues arise.

* **Stay Updated**

Keep an eye on updates and changes in the codebase or libraries/frameworks being patched. Regularly check for official updates or fixes that might render your patches unnecessary. Evaluate whether the patches can be replaced or removed once official solutions become available.

By following these best practices, you can effectively utilize Monkey Patching in Python while minimizing risks and maintaining a stable and maintainable codebase.
>  **In this guide to [device compatibility testing](https://www.lambdatest.com/learning-hub/compatibility-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub), learn how to build compatible software applications for different platforms and environments.**

## Parallel Testing using Monkey Patching

Parallelizing [automation testing](https://www.lambdatest.com/automation-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) using Monkey Patching is a valuable approach that can significantly [enhance test execution speed](https://www.lambdatest.com/blog/speed-up-selenium-test-cases-execution/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) and efficiency. By combining the power of parallelism with the flexibility of Monkey Patching, testers can achieve faster results and maximize their testing capabilities.

In this section, we will explore how to perform [Selenium automation](https://www.lambdatest.com/selenium-automation?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) testing in parallel using Monkey Patching in Python. [Parallel testing](https://www.lambdatest.com/blog/what-is-parallel-testing-and-why-to-adopt-it/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) allows us to run multiple test cases simultaneously, reducing the overall testing time and increasing efficiency.

The demonstration will be done on a cloud-based grid like LambdaTest, an AI-powered test orchestration and execution platform that helps developers and testers to perform [Python automation testing](https://www.lambdatest.com/python-automation-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) of their websites or web applications on over 3000+ real browsers and operating system combinations. While performing testing on LambdaTest, you can run your automated tests in parallel across multiple browsers and OS combinations, reducing the overall test execution cycles.
{% youtube SqQ8SugRDos %}
Don’t miss out on thе latеst tutorials on automation tеsting! Subscribe to thе [LambdaTеst YouTubе Channеl](https://www.youtube.com/c/LambdaTest?sub_confirmation=1?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=youtube) for tutorials on [Sеlеnium tеsting](https://www.lambdatest.com/selenium-automation?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage), [Playwright testing](https://www.lambdatest.com/playwright-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage), and more.

Visit our documentation to [get started with Selenium Python testing](https://www.lambdatest.com/support/docs/python-with-selenium-running-python-automation-scripts-on-lambdatest-selenium-grid/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=support_docs)

To perform [Selenium Python testing](https://www.lambdatest.com/selenium-python-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) on the LambdaTest cloud grid, you need to use the capabilities to configure the environment where the test will run. In this tutorial on Monkey Patching in Python, we will run the tests with the following characteristics:

**Environment 1**

* Browser: Chrome

* OS: Windows 11

**Environment 2**

* Browser: Firefox

* OS: [macOS](https://www.lambdatest.com/test-on-mac-mavericks?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) Ventura

We are going to execute the below test scenario:

**Test Scenario**

 1. Open the Simple Form Demo page in the [LambdaTest Playground](https://www.lambdatest.com/selenium-playground/simple-form-demo?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage).

 2. Fill in the first field with a message.

 3. Click the Get Checked Value button.

 4. Check that the typed message is shown to the user on the right side.

![](https://cdn-images-1.medium.com/max/2000/0*o6FKSzbIJxasMNcr.png)

## Setting up the Environment

Before diving into parallel testing, you must ensure your testing environment is properly set up. Here are the essential steps that you need.

To start our coding, you need to proceed with the following steps to prepare the environment.

**Step 1: Download and Install Python**
Begin by installing Python, if not already installed, from the official Python website *https://www.python.org.*

**Step 2: Install Selenium and pytest libraries**
Once Python is installed, use the Python package manager, *pip*, to install Selenium and pytest just running the following command:

    pip install -r requirements.txt

Requirements.txt contains the dependencies that you want to install.

![](https://cdn-images-1.medium.com/max/2000/0*fEPhDZ6oGXp7dFS9.png)

After running, you can see the below output:

![](https://cdn-images-1.medium.com/max/2000/0*BV91sX19of5sYzP9.png)

**Step 3: Download and Install Visual Studio Code**
Visual Studio Code (VSC) will be the IDE we will use in this tutorial on Monkey Patching in Python. However, you are free to use your preferable IDE!

**Step 4: Configure pytest in Visual Studio Code**
To finish the configuration, you need to tell Visual Studio Code that pytest will be your test runner. You can do this by following the below instructions:

 1. Create a folder for your project (in our example python-headless-browser).

 2. Open the project folder in Visual Studio Code.

 3. Open the command palette (menu View > Command Palette), and type “Configure Tests”.

 4. Select pytest as the test framework.

 5. Select the *root directory* option.

Now, you are ready to start!

You need also to prepare the LambdaTest capabilities code to be inserted in our test script.

You can generate the capabilities code from the [LambdaTest Capabilities Generator](https://www.lambdatest.com/capabilities-generator/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage).

Then, you should get the “*Username*” and “*Access Token*” from your account in your [LambdaTest Profile Section](https://accounts.lambdatest.com/detail/profile?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) and set it as an environment variable.

With the environment set up, you can proceed to the implementation of parallel testing using headless browsers.
>  **Test your native app and website on real iOS and Android devices hosted on the cloud. LambdaTest is a convenient, cost-effective and centralised solution for running realtime and Automated test on real [cloud device](https://www.lambdatest.com/real-device-cloud?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage).**

## Implementation of Parallel Testing on LambdaTest

**Implementation**

    from selenium import webdriver
    from selenium.webdriver.common.by import By
    from selenium.webdriver.remote.webelement import WebElement
    import pytest
    import os
    from time import sleep
    import datetime
    
    
    @pytest.fixture(params=["chrome-Windows11", "firefox-macOSVentura"],scope="class")
    def driver(request):
        username = os.getenv("LT_USERNAME")
        accessToken = os.getenv("LT_ACCESS_KEY")
        gridUrl = "hub.lambdatest.com/wd/hub"
    
    
        if request.param == "chrome-Windows11":
            web_driver = webdriver.ChromeOptions()
            platform = "Windows 11"
        if request.param == "firefox-macOSVentura":
            web_driver = webdriver.FirefoxOptions()
            platform = "MacOS Ventura"
    
    
        lt_options = {
            "user": username,
            "accessKey": accessToken,
            "build": "monkey patching build",
            "name": "monkey patching test",
            "platformName": platform,
            "w3c": True,
            "browserName": "Chrome",
            "browserVersion": "latest",
            "selenium_version": "4.8.0",
        }
        options = web_driver
        options.set_capability('LT:Options', lt_options)
    
    
        url = "https://"+username+":"+accessToken+"@"+gridUrl
       
        driver = webdriver.Remote(
            command_executor=url,
            options=options
        )
    
    
        yield driver
       
        driver.quit
    
    
    def custom_click(self):
        # Perform additional actions before clicking
       
        # Get time (Before profiling)
        st_time = datetime.datetime.now()
        sleep(10)
    
    
        # Get time (After profiling)
        en_time = datetime.datetime.now()
    
    
        # Calculate the time difference
        time_diff = en_time - st_time
    
    
        # Convert the time difference to seconds
        elapsed_time = time_diff.total_seconds()
    
    
        print("\nSleep in Monkey Patching: ", elapsed_time)
    
    
        # Call the original click() method
        self._original_click()    
    
    
    # Patching the WebElement class with the custom_click() method
    WebElement._original_click = WebElement.click
    WebElement.click = custom_click
    
    
    def test_simple_demo_form(driver):
        # Load a webpage
        driver.get("https://www.lambdatest.com/selenium-playground/simple-form-demo")
    
    
        # Find an input element by its ID and enter text
        input_element = driver.find_element(By.ID, "user-message")
        input_element.send_keys("This is a monkey patching text!")
    
    
        # Find an element by its ID and click on it
        element = driver.find_element(By.ID, "showInput")
        element.click()
    
    
        # Find an element by its ID and extract its text
        element = driver.find_element(By.ID, "message")
        assert element.text == "This is a monkey patching text!"

**Code-Walkthrough**

Let’s explore a practical example demonstrating the combination of parallel testing and Monkey Patching in Python for performing Selenium automation testing. We’ll utilize the popular pytest framework for organizing and executing our tests.

{% youtube KZstMSOHIvQ %}


In our code, we start by importing the necessary modules and defining the [pytest fixture](https://www.lambdatest.com/blog/end-to-end-tutorial-for-pytest-fixtures-with-examples/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) called driver using the *pytest.fixture *decorator. This fixture is responsible for initializing the Selenium WebDriver based on the specified browser and platform configurations. By parameterizing the driver fixture, you can run tests in parallel on different combinations of browsers and platforms. You can learn more about it through this blog on [parameterization in pytest](https://www.lambdatest.com/blog/parameterization-in-pytest-with-selenium/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog).

![](https://cdn-images-1.medium.com/max/2000/0*JmRAVjpIfximzHq3.png)

Inside the driver fixture, we set up the LambdaTest credentials and configure the *WebDriver* options based on the chosen browser-platform combination. We then create a remote *WebDriver* instance using the LambdaTest URL and the configured options. The driver fixture is then yielded, allowing the test function to utilize the *WebDriver*. After the test is completed, the *driver.quit* method is called to close the *WebDriver* session gracefully.

![](https://cdn-images-1.medium.com/max/2000/0*2dBYty4tY1mgFDSa.png)

To showcase Monkey Patching, we define a custom method called *custom_click()*. This method allows us to add additional actions before invoking the original *click()* method of the *WebElement* class.

In our example, we get the timestamp, do a sleep delay of 10 seconds, get the timestamp again, and calculate and print the difference between the timestamps, which is the sleep time in Monkey Patching, before the actual click action.

![](https://cdn-images-1.medium.com/max/2000/0*Sikam4eW-U-rZBZV.png)

We then patch the *click()* method of the *WebElement* class by assigning our *custom_click()* method to it. This effectively overrides the default behavior of the *click()* method with our customized actions.

![](https://cdn-images-1.medium.com/max/2000/0*CHkT4kHddGCwMPN4.png)

Finally, we define a test function called *test_simple_demo_form* that demonstrates the usage of the Monkey Patched *click()* method. Inside this function, we navigate to a webpage, interact with elements, and perform assertions to validate the expected results.

![](https://cdn-images-1.medium.com/max/2000/0*igy_Xr7fVHnbQG59.png)

In the above code snippets, we use the *get()* method to access the webpage we are going to test, then we use the [*find_element() *method in Selenium](https://www.lambdatest.com/blog/findelement-and-findelements-in-selenium/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog) to locate elements on the page. These methods allow us to find elements using different strategies, such as by ID, Name, Class Name, CSS Selector, and more. Once you have a reference to an element, you can interact with it using the *click() *or [*send_keys()* method in Selenium](https://www.lambdatest.com/blog/how-to-use-selenium-sendkeys/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=blog).

For our sample, we will use three components on the page: the message field to type, the button to click, and the message on the right side to check if it was correctly set.

![](https://cdn-images-1.medium.com/max/2000/0*7UYfsQGvX863NcHv.jpg)

For the message field, after locating the element, we want to type inside it using the *send_keys()* method. Then we will use the *click()* method for the button element and the *.text* to extract the text from the message element.

You can run the entire code using the below command:

    pytest -s

![](https://cdn-images-1.medium.com/max/2000/0*FVB7IMtSR4CbXstS.png)

![](https://cdn-images-1.medium.com/max/2000/0*wlb-2F6ExXbAVxKe.jpg)

The –*s* flag allows the timestamps to be printed in the console.

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## Conclusion

In this [Selenium pytest tutorial](https://www.lambdatest.com/learning-hub/selenium-pytest-tutorial?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub), we have explored the concept of Monkey Patching in Python and its various applications. Monkey Patching in Python provides developers and QAEs with a powerful tool to modify code at runtime, allowing for customization, bug fixing, and extending functionality. However, it is important to approach Monkey Patching in Python cautiously and adhere to best practices to avoid potential risks and limitations.

Monkey Patching in Python is a powerful technique that should be approached with care and consideration. When used appropriately and following best practices, it can be a valuable tool in a developer’s and QAE`s arsenal. Monkey Patching in Python offers the flexibility to modify code dynamically and tailor it to specific requirements. By understanding its limitations, documenting changes, and conducting thorough testing, you can harness the potential of Monkey Patching in Python effectively and contribute to the overall success of your projects.

Do checkout complete guides on [python basics](https://www.lambdatest.com/learning-hub/python-basics?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub), [software testing metrics](https://www.lambdatest.com/learning-hub/software-testing-metrics?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub), [webdriverio appium](https://www.lambdatest.com/learning-hub/webdriverio-appium?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=learning_hub) and more. We also have a comprehensive library of [software testing questions](https://www.lambdatest.com/software-testing-questions/?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=stq), where we cover generic questions like [what is bdd testing](https://www.lambdatest.com/software-testing-questions/what-is-bdd-testing?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=stq), [what is an iframe](https://www.lambdatest.com/software-testing-questions/what-is-an-iframe?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=stq), and more. We also support legacy browsers and OS, like you can [test on internet explorer](https://www.lambdatest.com/test-on-internet-explorer-simulator?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) or you can [test on macOS Mountain lion](https://www.lambdatest.com/test-on-mac-mountain-lion?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) and more. You can also [test screen orientation](https://www.lambdatest.com/screen-orientation?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage) and perform [selenium automation testing with gauge framework](https://www.lambdatest.com/selenium-automation-testing-with-gauge-framework?utm_source=devto&utm_medium=organic&utm_campaign=jan_22&utm_term=bh&utm_content=webpage).

## Frequently Asked Questions (FAQs)

## What is monkey patching in programming?

Monkey patching in programming refers to the practice of dynamically modifying or extending the code behavior at runtime, typically during the execution of a program. It allows developers to alter the behavior of functions, methods, or classes without changing the source code.

## What is monkey patch in pytest?

In pytest, a “monkey patch” refers to the ability to modify or replace objects, functions, methods, or modules at runtime during testing. It is a powerful feature provided by pytest that allows testers to change the behavior of the code being tested temporarily.

## What does @patch mean in Python?

In Python, *@patch* is a decorator provided by the *unittest.mock* module, which is a part of the Python standard library. It is commonly used for creating mock objects and patching functions or methods during testing.
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