Python is one of the most popular programming languages in the world – it’s versatile, powerful, and used in everything from web applications to data pipelines and machine learning systems. But there’s one thing all serious Python projects need to stay healthy: good tests. If you care about writing high-quality code, maintaining stability, and keeping your codebase easy to change over time, you need a solid testing practice.
That’s where pytest comes in. pytest is one of the most widely used Python testing frameworks – it is fast, flexible, and designed to make testing real-world code straightforward instead of painful. This course is about pytest and practical Python testing – turning testing from a “nice to have” into a core tool for maintainable, stable code.
You’ll learn how to use pytest in real-world Python projects – both when you’re facing messy, complex legacy code that feels risky to touch, and when you are starting a new project and want a clean, test-driven foundation from day one. Instead of focusing on toy examples, we will look at patterns, techniques, and workflows that you can apply directly at work or in your own projects.
No advanced background is required:
Basic Python knowledge is enough to get value out of this course. Whether you are a software engineer, machine learning or data science practitioner, data engineer, backend/frontend developer, or startup founder, this course will help you write cleaner, more maintainable, and better-tested Python code. We will start at the very beginning – writing and running your first tests with pytest – and build up to a scalable, professional testing setup that fits naturally into modern development workflows and CI/CD pipelines.
In detail, this course covers:
- Installing & running pytest
- Setting up pytest in new and existing Python projects
- Understanding test discovery, naming conventions, and basic structure
- Using assertions effectively
- Testing functions, classes, and modules
- Avoiding typical mistakes when starting with testing
- Creating reusable fixtures for data, configuration, and environment setup
- Understanding fixture scopes (function, class, module, session) and when to use which
- Sharing fixtures across tests and modules
- Writing one test that covers multiple inputs, edge cases, and scenarios
- Structuring and naming tests for clarity and long-term maintainability
- Using parametrization to quickly increase coverage without duplicating code
- Structuring tests for small scripts and large applications
- Grouping, naming, and tagging tests so teams can work efficiently
- Using markers to manage slow, flaky, integration, or smoke tests
- Testing business logic, services, and data-processing code
- Strategies for testing code that works with files, APIs, databases, or external services
- Reducing flakiness and isolating side effects
- When to mock – and when not to
- Using monkeypatch and pytest-mock to replace or isolate dependencies
- Writing code that’s easier to test without unnecessary complexity
