Pytest Essentials: Unit Testing

Learn how to perform unit testing in Python with pytest by creating simple functions for subtraction and division, writing tests, and running them to verify results with assertions.

Define a pytest test class to run multiple test cases in one file, asserting conditions like value less than 20 and value greater than or equal to 10.

Learn to verify substrings in Python using pytest unit tests, exploring passing and failing tests, and handling different data types through practical test examples.

Learn how to group tests using markers across multiple scripts by creating test files, importing playtest, and running marker-based groups to view test results.

Learn to create and use pytest fixtures to supply test data. Define a fixture with pytest.fixture, return a value, and inject it into tests by naming the fixture parameter.

Use fixtures to store dictionary data for pytest unit tests. Identify and fix errors shown by expected vs actual results and re-run tests.

learn to use parameterize to run a single test over multiple data sets in pytest, validating value and expected result, and reviewing test reports for any failures.

Engage in unit testing with pytest by implementing functions to compute minimum and maximum from a numeric array and verify results with sample tests.

Learn how to speed up unit tests with pytest-xdist by running tests in parallel, install the package, and see time reductions when executing multiple tests concurrently.

Learn how to use xfail and skip in pytest, creating test files, marking tests to fail or skip, and understand that skipped or non-executed tests do not print results.

Explore what unit testing is, why it matters, and how testing the smallest meaningful code units guarantees that failures reveal problems, using input and expected results.

explore doctest in unit testing through a simple example that defines a test function to compute a product, establishes expected outputs, and runs tests to show pass or failure.

Understand why we use mocks in unit testing and how simulated objects imitate behavior to replace parts of a system, enabling controlled tests and clear assertions.

Demonstrate the Dimmock class, create a mock instance, and configure attributes and return values to replace stubs and test doubles in unit tests.

Explore how to use the mocks side_effect attribute to return a sequence of values or raise exceptions in unit tests, with a 5, 6, 7 sequence and an exception example.

Learn to mock the current date in Python unit tests using a time machine package to verify behavior when the system date changes.

Explore teardown in pytest by using fixtures with yield to run cleanup after tests, and demonstrate mail-related fixtures and inbox cleanup in a practical demo.