Unit Testing in Python

Remi Genet

Unit Testing in Python

Cours

Fondamentaux

Learn how to write and run unit tests in Python using the pytest framework.

Author

Remi Genet

Published

2024-10-21

Unit Testing in Python

Unit testing is a software development practice where individual components or functions of a program are tested in isolation. The primary goals of unit testing are:

To verify that each part of the program works correctly on its own.
To catch and fix bugs early in the development process.
To facilitate refactoring and maintenance of code.
To serve as documentation for how components should behave.

A unit test typically follows this pattern: 1. Set up the test conditions. 2. Call the function or method being tested. 3. Assert that the output or behavior matches the expected result.

Python offers several frameworks to help write and run unit tests efficiently. While the built-in unittest module is available, many developers prefer pytest for its simplicity and powerful features.

Popular Python Testing Frameworks:

unittest: Python’s built-in testing framework.
pytest: A more advanced, feature-rich testing framework.
nose: An extension of unittest (less common nowadays).

We’ll focus on pytest due to its popularity and ease of use, especially when integrated with modern Python project management tools like Poetry.

Basic pytest Usage

Install pytest:
```
pip install pytest
```
Write a test file (e.g., test_example.py):
Run tests:
```
pytest
```

Integrating pytest with Poetry

Add pytest to your project’s dev-dependencies:
```
poetry add --dev pytest
```

Update your pyproject.toml:

[tool.poetry.dev-dependencies]
pytest = "^7.4.0"

[tool.pytest.ini_options]
testpaths = ["tests"]

This configuration tells pytest to look for tests in the tests directory.

Organize your project structure:

myproject/
├── src/
│   └── mypackage/
│       └── example.py
├── tests/
│   └── test_example.py
└── pyproject.toml

Write your tests in the tests directory.
Run tests using Poetry:
```
poetry run pytest
```

Advanced pytest Configuration

You can add more options to your pyproject.toml:

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-v --cov=src"

This configuration: - Runs tests verbosely (-v) - Includes coverage reporting (--cov=src)

Running Tests with Poetry

To make running tests easier, you can add a script to your pyproject.toml:

[tool.poetry.scripts]
test = "pytest"

Now you can run tests with:

poetry run test

Continuous Integration

For CI/CD pipelines, you can use Poetry to install dependencies and run tests:

# Example GitHub Actions workflow
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: 3.9
    - name: Install dependencies
      run: |
        pip install poetry
        poetry install
    - name: Run tests
      run: poetry run pytest

This setup ensures your tests are run automatically on every push or pull request.

Best Practices

Write tests for all new features and bug fixes.
Aim for high test coverage, but focus on critical paths.
Use meaningful test names that describe the behavior being tested.
Keep tests independent and avoid dependencies between tests.
Use fixtures and parametrization to reduce code duplication in tests.

By integrating pytest with Poetry, you create a streamlined development workflow where dependency management, building, and testing are all handled by a single tool.

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