Data Science

Getting Started With PyTest Fixtures (How to Use Fixtures)

Published: 4th September, 2023

Harshini Bhat

Data Science Consultant at almaBetter

Learn how to use pytest fixtures effectively to streamline testing, improve code quality, create robust test suites and level up your python development process

Testing is a critical aspect of software development, ensuring code quality, reliability, and functionality. It acts as a safety net, catching and fixing bugs before they reach users, validating code behavior, and identifying performance and security issues. Pytest, a powerful testing framework in Python, introduces the game-changing feature of "fixtures." These magical tools provide necessary resources and data to test functions, simplifying setup and teardown logic, promoting code reusability, and enhancing test data management. The purpose of this article is to guide readers on effectively using pytest fixtures in their test suites. It will offer a step-by-step guide on creating and using fixtures and highlight best practices. By the end, readers will master the art of leveraging fixtures to optimize their testing workflow, resulting in robust and reliable software products.

Understanding What are Fixtures in Pytest

Pytest fixtures are a powerful feature in the Pytest testing framework that play a crucial role in setting up the test environment. They act as a bridge between test functions and the resources they require to execute successfully.

  • Fixtures are functions decorated with @pytest.fixture.
  • They provide necessary data, services, or resources needed during testing.
  • When a test function requires specific data or resources, Pytest automatically invokes the corresponding fixture.
  • Fixtures ensure that each test function starts with the required setup, creating a clean and controlled testing environment.

Why use Fixtures in Pytest ?

Using fixtures in pytest offers numerous advantages and benefits, making them an indispensable tool in the arsenal of any Python developer. Here's why you should use fixtures in pytest:

  • Setup and Teardown Simplification: Fixtures encapsulate the setup and teardown logic for your tests. By using fixtures, you can set up the necessary environment and resources before each test function runs and clean up after the test is done. This separation of concerns leads to cleaner and more focused test functions, making your testing code easier to read and maintain.
  • Code Reusability: Fixtures promote code reusability. Instead of repeating setup and teardown code across multiple test functions, you define the setup logic in a fixture and use it in various tests. This saves time and effort, reduces code duplication, and improves the maintainability of your test suite.
  • Parameterization of Tests: Fixtures can be parameterized, allowing you to run the same test with different data sets or configurations. This enables you to test your code against various scenarios, providing better test coverage and ensuring your code behaves correctly in different situations.
  • Isolation of Dependencies: Fixtures help in isolating dependencies, such as databases, APIs, or external services, from your test code. By using fixtures to provide mock or dummy data, you can ensure that your tests run independently and consistently, regardless of the availability or state of external resources.
  • Customization and Flexibility: Fixtures are highly customizable, allowing you to tailor the setup and teardown processes to your specific testing needs. You can use fixtures to simulate specific scenarios, manipulate test data, or control the test environment, providing the flexibility required to test different aspects of your code.
  • Enhanced Test Readability: By abstracting setup and teardown code into fixtures, your test functions become more focused on their actual testing logic. This improves the readability of your tests, making it easier for you and other team members to understand the purpose and intent of each test.
  • Scoping Options: Pytest fixtures offer various scoping options (function, class, module, package, and session) to control how long the fixture remains active during test execution. This allows you to optimize resource usage and improve test performance based on the scope required for each fixture.

Using fixtures in pytest simplifies test setup and teardown, promotes code reusability, enables parameterization, isolates dependencies, and offers customization options.

Creating Your First Pytest Fixture

Let us learn how to create a pytest fixture and with this fixture examples get familiar with how to use fixtures in pytest

  • Step 1: Install Pytest Before creating your first fixture, ensure you have Pytest installed. If not, you can install it using pip:
  • Step 2: Import Pytest and Decorate the Fixture

In your Python test file, import Pytest and decorate your fixture function using the @pytest.fixture decorator. This signals to Pytest that the function is a fixture and will be used to provide resources to test functions.

  • Step 3: Define the Fixture Function

Inside the fixture function (user_data in this case), define the setup logic to provide the desired resource. In this example of fixtures, the fixture returns a dictionary containing user data.

  • Step 4: Use the Fixture in a Test Function

Now, let's create a test function that uses the user_data fixture. Simply include the fixture name as an argument in the test function.

  • Step 5: Run Your Tests To run the test, execute the pytest command in the terminal:

In the example of  fixtures above, we created a fixture named user_data, which provides a dictionary containing user data. The fixture function returns the dictionary with keys for "username," "email," and "age," along with their respective values.

The test function test_user_data utilizes the user_data fixture by accepting it as an argument. When the test runs, Pytest recognizes the dependency on the user_data fixture and automatically invokes it. The fixture's return value (the dictionary with user data) is then passed to the test function as the user_data argument.

Inside the test function, we use assertions to verify that the provided user data matches the expected values. By using fixtures, we separate the setup logic for user data from the test itself, making the test function cleaner and more focused on its testing logic. Additionally, we can easily reuse the user_data fixture in other test functions, enhancing code reusability and maintainability throughout the test suite.

Pytest Fixture Scopes: Understanding Scope in Testing

Pytest fixtures can have different scopes that define when and how long they remain active during test execution. The different fixture scopes are:

Function Scope:

  • Fixture is invoked before and after each test function runs.
  • Ideal for fixtures that provide data or resources needed for individual test functions.
  • Useful when each test function requires a clean and isolated environment.

Class Scope:

  • Fixture is invoked once for all test methods within a test class.
  • Remains active throughout the test class, across multiple test methods.
  • Suitable for fixtures that require common setup for multiple test methods within a class.

Module Scope:

  • Fixture is invoked once per test module (Python file).
  • Remains active for all test functions within the same module.
  • Suitable for fixtures that provide shared resources across multiple test functions in a module.

Package Scope:

  • Fixture is invoked once for all test functions within a test package (across multiple Python files in the same directory).
  • Remains active throughout the entire test package.
  • Appropriate for fixtures providing shared setup and teardown across multiple modules in a package.

Session Scope:

  • Fixture is invoked once for the entire test session, covering all test functions in all modules and packages.
  • Remains active throughout the entire test session.
  • Useful for fixtures that require costly one-time setup and teardown operations across the entire test suite.

Best Practices for Using Pytest Fixtures:

Some of the best practices and tips for using pytest Fixtures are as follows:

Keep Fixtures Concise and Focused:

  • Write fixtures that provide only the necessary resources and avoid including complex setup logic.
  • Keep fixture functions focused on their specific purpose, making the test code more readable and maintainable.

Use Descriptive Names:

  • Choose descriptive and meaningful names for fixtures to convey their purpose clearly.
  • Well-named fixtures make the test suite easier to understand for yourself and other developers.

Organize Fixtures in a Structured Manner:

  • Group related fixtures together and maintain a clear structure in your test suite.
  • Consider using fixture classes or organizing fixtures in separate files to improve organization.

Limit Fixture Scopes:

  • Choose the appropriate fixture scope (function, class, module, package, or session) based on its usage in the tests.
  • Avoid using broader scopes when function scope is sufficient to prevent unintended side effects and resource waste.

Mock External Dependencies:

  • Use fixtures to mock external dependencies like databases or APIs for more controlled and consistent testing.
  • By isolating external resources, you can run tests faster and ensure they are not affected by external changes.


Pytest fixtures are a powerful and indispensable feature for writing clean, efficient, and reliable test suites in Python. They act as test assistants, providing necessary data, services, or resources to test functions, ensuring a controlled and consistent testing environment. The various fixture scopes, including function, class, module, package, and session, allow developers to tailor the extent of fixture activation based on specific testing needs. Choosing the appropriate scope ensures efficient resource utilization and avoids unintended side effects in test execution.

Fixtures play a significant role in isolating dependencies, promoting code reusability, and parameterizing tests, leading to comprehensive and comprehensive testing. They empower developers to create high-quality software with greater confidence in its correctness and reliability. Incorporating fixtures into your testing workflow will streamline the setup and teardown process, making testing more efficient and productive. Embracing the concepts and guidelines presented in this article, developers can unlock the full potential of Pytest fixtures and elevate the quality of their Python code through robust and effective testing practices. So, let's embrace the power of fixtures and embark on a journey of creating exceptional software with Pytest!

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