Advanced Testing with pytest

1. Introduction

pytest is a powerful testing framework for Python that enables developers to write simple as well as scalable test cases. This lesson covers advanced features such as fixtures, parameterization, and mocking.

2. Setup

To start using pytest, you need to install it. You can install pytest using pip:

pip install pytest

3. Fixtures

Fixtures allow you to set up some context for your tests. They can create a test environment and provide data that can be shared across multiple tests.

Here’s how to create a simple fixture:

import pytest

@pytest.fixture
def sample_data():
    return {"key": "value"}

def test_sample(sample_data):
    assert sample_data["key"] == "value"

4. Parameterization

Parameterization allows you to run a single test function with different sets of parameters. This is useful for testing the same function with various inputs.

@pytest.mark.parametrize("input,expected", [
    (1, 2),
    (2, 3),
    (3, 4),
])
def test_increment(input, expected):
    assert increment(input) == expected

5. Mocking

Mocking is a technique used to simulate the behavior of complex objects, allowing you to isolate the unit of work being tested.

from unittest.mock import MagicMock

def test_with_mock():
    mock = MagicMock(return_value=10)
    assert mock() == 10

6. Best Practices

Here are some best practices for advanced testing with pytest:

• Use fixtures to set up state.
• Make use of parameterization for exhaustive testing.
• Keep your tests isolated and avoid dependencies.
• Use descriptive test names.

7. FAQ