Pytest automatically discovers tests based on naming conventions.

Test files should start with test_ or end with _test.py. Test functions must also begin with test_.

Pytest scans directories recursively to locate tests. This automatic discovery removes the need for manual test registration.

Pytest runs test cases sequentially or in parallel (with plugins).

It executes test functions and checks assertions. If an assertion fails, the test is marked as failed.

Pytest provides detailed error output showing expected vs actual values. This makes debugging faster and easier.

Fixtures provide reusable setup and teardown logic for tests. They prepare test data, database connections, or configurations.

Fixtures are defined using the @pytest.fixture decorator. Tests request fixtures as function arguments. This promotes clean, modular, and maintainable test code.

Pytest is preferred for modern Python testing due to simplicity.

Fixtures are the recommended approach in Pytest.

Both are essential for writing robust tests.

These markers help manage unstable tests.

Pytest is installed using pip with pip install pytest. It works across platforms.

After installation, tests are run using the pytest command. Pytest integrates well with virtual environments. Installation is simple and fast.

Tests are run by executing the pytest command in the project directory.

Pytest automatically finds and runs tests. You can run specific files or tests using filenames or test names.

Command-line options customize execution. This flexibility is interview-relevant.

A test case is a function that verifies a specific behavior. It contains assertions to validate output.

Test cases should be independent and repeatable. Good test cases focus on one functionality. Pytest makes writing test cases easy.

Assertions validate expected behavior. Pytest uses Python’s built-in assert statement. If the condition fails, Pytest shows a detailed error message. Assertions improve test reliability. They are the core of test validation.

Markers categorize tests. Common markers include skip, xfail, and custom markers. They control which tests run under certain conditions. Markers help organize large test suites. They improve test management.

conftest.py stores shared fixtures and configurations. It allows reuse across multiple test files.

Pytest automatically loads it. No imports are required. It helps maintain clean test structures.

Parameterized testing runs the same test with different inputs. It reduces code duplication.

Pytest provides @pytest.mark.parametrize. It improves coverage and readability. It’s commonly used in real projects.

Exceptions are tested using pytest.raises(). It ensures code raises expected errors. This validates error-handling logic.

Exception testing is essential for robust applications. Interviews often ask this.

Test coverage measures how much code is tested. Tools like pytest-cov are used. High coverage reduces bugs. Coverage helps identify untested code. It improves software quality.

Pytest integrates easily with CI tools like GitHub Actions and Jenkins. Tests run automatically on commits. This prevents broken code from reaching production. Pytest supports fast feedback loops. CI integration is very common in interviews.

Mocking simulates external dependencies. It isolates code under test. Pytest uses unittest.mock or plugins. Mocking improves test reliability. It avoids real API or DB calls.

Pytest is simple, powerful, and flexible. It reduces boilerplate code. It supports fixtures, plugins, and parameterization. Pytest improves test readability and maintainability. This makes it the top choice for Python testing.