Pytest Course: Python Test Automation & GitHub Actions CI/CD

Explore testing real-world Python code with pytest by unit testing a document editor, writing tests for write, clear, and get last content, and organizing tests into a test class.

Add descriptive error messages with asserts or pytest.fail to reveal last content and editor history, diagnose failures, and fix the clear method to pass all tests.

Demonstrates asserting exceptions in pytest by using pytest.raises to validate a value error when no document history exists; shows using match and exception info for precise error messages.

Leverage the given-when-then structure to craft modular, isolated pytest tests with asserts at the end, and split complex scenarios into simple cases such as initial empty editor and writing content.

Practice real-world testing with more-itertools by loading the project, setting up a virtual environment, installing pytest, and practicing test-driven development through tests for partition, windowed, interleave, and rolling_aggregate.

Implement and test real-world Python utilities using pytest, including partition, window, interleave, and rolling aggregation, with happy-path and edge-case coverage.

Explore pytest fixtures to reduce duplication and manage setup and cleanup, keeping tests isolated, fast, and readable. Learn how mocking and fixtures enable robust tests for real-world code.

Explore pytest fixtures and their scopes from function to session, using a FastAPI project to demonstrate reusable setup, teardown, and reduced test time with a single client across tests.

Move the shared client fixture to conf test.py to reuse it across user and analytics tests, enabling scalable fixture sharing.

Learn to test a fastapi app by implementing a file upload endpoint, using temporary files and directories with pytest tmp_path and tmp_path_factory, and capture output with capsys.

Mock external dependencies with pytest's monkeypatch fixture, replacing heavy calls with lightweight substitutes. Test analytics sales endpoints by using env vars and monkeypatch to simulate failures and a disabled db.

Learn to use pytest fixtures with autouse to mock the request context processing across tests, employing monkeypatch and fixture reuse for analytic endpoints testing.

Test logs with pytest fixtures by capturing standard output and logging using capsys and caplog, validating prints and logger messages for endpoints like upload file.

Practice testing a real-world Python personal finance tracker with Pytest, focusing on public methods, isolation via tmp path, and context manager usage for JSON storage.

Learn pytest fixtures as reusable test components, use yield for setup and teardown, and manage scope with built-in fixtures like tmp_path_factory and monkeypatch.

Discover how parametrization, combined with fixtures, lets you run the same test across many inputs and dynamic data from files, reducing duplication.

Learn to write scalable unit tests for a conversion graph using a unit registry, with parameterization to cover multiple cases and ensure accuracy with pytest approx.

Learn to parameterize tests in pytest with pytest.mark.parametrize, enumerate parameters, and use them as test inputs or fixtures for clearer, descriptive test results.

Explore parametrizing tests with fixtures in pytest, using a conversion case fixture and request.param. Learn about IDs, fixture versus test parameterization, and combining parameters across algorithms like BFS and DFS.

Explore three pytest parameterization methods—tests with pytest.mark.parametrize, fixtures with params, and dynamic parameterization via pytest_generate_tests and metafunctions used in discovery.

Explore the factory as fixture pattern in pytest to streamline unit conversion tests with a unit registry, handling unknown units (KeyError) and no conversion path (ValueError) while reusing fixtures.

Explore Pytest parametrization: use parameterized decorators and fixtures, combine values for tests, enable dynamic parameterization and factory fixtures for richer, cleaner test coverage.

Master advanced mocking to simulate deep behavior, guard against mock drift, and use fixtures and markers to run Pytest tests, uniting unit, integration, and end-to-end testing into a cohesive strategy.

Compare advanced mocking in pytest by examining monkeypatch limitations for environment variables and simple functions, and discover how pytest-mock and unittest.mock enable scalable test doubles with assertion support.

Replace monkeypatch with pytest mocker to mock functions and environment variables in real-world Python code, using return_value, side_effect, spies, and fixture scopes.

Learn to mock classes with pytest mocker and patch, using object attributes to target methods like get rate, and mock the ECB rate provider within a personal finance tracker.

Explore how mock drift occurs when mocks diverge from real behavior, and prevent it with patch using auto_spec to enforce expected arguments in a finance tracker’s currency conversion tests.

Explore patching targets in pytest by mastering lookup paths, imports, and mock placement using weather and analytics examples to ensure mocks affect the right code.

Explore pytest markers, including use fixtures, filter warnings, and xfail, learn to create custom markers and ini definitions, and run marker-specific tests with -m.

Make fixtures marker aware using pytest markers to tailor fixture data based on test markers and their arguments, using request.node.get_closest_marker and marker parameters.

Explore how configuration files in pytest extend and deduplicate test sessions with markers and fixtures, set default options like verbose, and choose between pytest.ini and pyproject.toml for project configuration.

Explore practical pytest coverage by evaluating statement and branch coverage. Analyze discount logic with HTML and terminal reports to reveal missing lines and branches for VIP, regular, and new users.

Explore how to boost test coverage with hypothesis and faker, using a unit converter project to generate edge cases, measure coverage, and validate conversions.

Explore the testing pyramid: unit, integration, system, and end-to-end tests, emphasizing isolation and mocking of dependencies, and compare regression and acceptance testing with TDD and BDD.

Explore the testing pyramid in action across unit, integration, and system tests, demonstrated on a converter module with pytest, registry, and CLI tests, plus practical tips for setup and coverage.

learn how pytest handles file name collisions in the testing pyramid across integration and unit test folders, and why __init__.py files prevent collection errors, keeping tests scalable.

Master advanced pytest techniques with mocker patch, mocker spy, mock drift handling with auto spec, and patching targets via import paths, while exploring markers and fixtures.

Learn how to automate pytest within a CI/CD pipeline, from planning and code through automated test runs, release, deployment to staging or production, and monitoring.

Implement continuous integration for the unit converter using GitHub actions, automatically run pytest tests, and set up a hello world workflow on push or pull requests.

Define and run a robust CI workflow with GitHub Actions to execute pytest, install dependencies, and perform smoke tests for the CLI.

Apply matrix testing with pytest to run tests across Python versions and OS in a CI workflow. Parameterize unit, integration, and system tests to run in parallel for faster feedback.

Boost test speed and quality by parallelizing pytest with xdist, auto tuning CPUs, and enforcing a minimum coverage gate in continuous integration.

Implement a CI/CD pipeline for a Python project with GitHub Actions, running pytest on every push and pull request, and apply metrics testing with pytest using xdist for parallel execution.

Apply pytest concepts through real-world practice by writing tests for existing code and experimenting with fixtures, parameterization, and mocking to build confidence and maintainable projects.