Exploring Mocking and Patching in Pytest: A Beginner’s Guide

Exploring Mocking and Patching in Pytest: A Beginner’s Guide


Have you ever wondered how software testing ensure their code works seamlessly with external dependencies like databases, APIs, or external services? Well, that’s where mocking and patching come into play! In this comprehensive guide, we’ll dive into the world of mocking and patching in Pytest, helping you understand how to simulate external dependencies and write more robust tests. Whether you’re a seasoned developer or just starting your journey, buckle up as we unravel the mysteries of mocking and patching!

Table of Contents

Sr# Headings
1 Understanding Mocking
2 Introduction to Patching
3 Why Mocking and Patching?
4 Simulating External Dependencies
5 Using Mocks in Pytest
6 Patching Techniques
7 Practical Examples
8 Best Practices
9 Common Pitfalls
10 Conclusion
11 FAQs

Understanding Mocking

Mocking is a technique used in software testing to simulate the behavior of external dependencies. Instead of using real objects like databases or APIs, mock objects are created to mimic their behavior. This allows developers to isolate the code being tested and focus on specific scenarios without relying on external systems.

Introduction to Patching

Patching is a specific form of mocking that involves replacing attributes or functions within a module or object during the test execution. This allows developers to control the behavior of specific components within their code without modifying the original source code.

Why Mocking and Patching?

Mocking and patching are essential in Automation Testing with Python  for several reasons. They enable developers to write more isolated and deterministic tests, reduce dependencies on external systems, speed up test execution, and improve overall test coverage. By simulating external dependencies, developers can ensure their code behaves as expected in various scenarios.

Simulating External Dependencies

When testing code that interacts with external dependencies such as databases, APIs, or services, it’s crucial to simulate these dependencies rather than relying on the actual systems. Mocking and patching allow developers to create controlled environments for testing, ensuring consistent and reliable test results.

Using Mocks in Pytest

Pytest, a popular testing framework for selenium webdriver , provides built-in support for mocking through the use of the pytest-mock plugin. This plugin offers convenient utilities for creating and managing mock objects within your test code, making it easy to simulate external dependencies.

Patching Techniques

Pytest offers various patching techniques, including function patching, class patching, and object patching. These techniques allow developers to replace specific functions or attributes within their code during test execution, enabling more targeted and flexible testing.

Practical Examples

Let’s explore some practical examples of mocking and patching in action. Suppose we have a function that interacts with a database. We can use mocking to simulate database calls without actually connecting to a database, allowing us to test different scenarios such as successful queries, error handling, and edge cases.

Best Practices

When using mocking and patching in your python course in bangalore  code, it’s essential to follow best practices to ensure your tests remain robust and maintainable. This includes keeping mocks simple and focused, using patching judiciously, and documenting your test cases thoroughly.

Common Pitfalls

While mocking and patching can be powerful tools, they can also introduce complexity and potential pitfalls if not used correctly. Common pitfalls include over-mocking, tight coupling between tests and implementation details, and failing to update mocks when code changes occur.


In conclusion, mocking and patching are invaluable techniques for simulating external dependencies in online course on python . By mastering these techniques, developers can write more reliable and maintainable tests, leading to higher-quality software products.


  1. What is the difference between mocking and patching?

Mocking involves creating fake objects to simulate external dependencies, while patching specifically involves replacing attributes or functions within a module or object during python automation testing  execution.

  1. Can mocking and patching be used in any programming language?

Yes, mocking and patching techniques are applicable to various programming languages, although the implementation details may vary.

  1. Are there any performance implications of using mocking and patching?

While mocking and patching can introduce some overhead, modern testing frameworks and tools are optimized to minimize performance impact.

  1. How do mocking and patching help improve test coverage?

By simulating external dependencies, mocking and patching allow developers to test specific scenarios and edge cases that may be challenging or impossible to reproduce with real systems.

  1. Are there any alternatives to mocking and patching?

While mocking and patching are widely used in software testing, alternatives such as dependency injection and test doubles (e.g., stubs and spies) can also be effective in certain situations.