Pytest Course: Practical Testing of Real-World Python Code

Pytest Course: Practical Testing of Real-World Python Code
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 11h 8m | 11.35 GB
Instructor: Artem Istranin

• How to use the pytest testing framework as a tool for making code maintainable.
  
• How to apply testing in real-world projects, from complex legacy code to new projects with a test-driven foundation.
  
• Design a scalable testing system integrated into existing CI/CD pipelines, or set up initial pipelines for automated test execution.
  
• Execute a practical path from 0% to 100% test coverage.
  
• Implement tests for real-world applications without overthinking or getting stuck on where to start.
  

• Basic Python skills are required - if you can implement functions and classes, you're good to go!
  
• NO prior experience with pytest or any testing framework is required at all
  

• 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
  

• Using coverage tools (like pytest-cov) to see what's actually tested
  
• Prioritizing what to test first in large, untested codebases
  
• Designing a realistic path from little or no coverage to high, meaningful coverage
  

• Adding tests around existing, fragile code without breaking it
  
• Building a "safety net" before making changes or refactors
  
• Gradually decoupling and improving tightly coupled code using tests as a guide
  

• When TDD is helpful - and when it's overkill
  
• Using tests to drive the design of new features
  
• Using tests to gain confidence and move faster, not slower
  

• Configuring workflows to run tests automatically on every push, pull request, or release
  
• Speeding up the test suite by parallelizing tests with pytest-xdist
  
• Handling slow or flaky tests in CI and shaping a testing strategy that matches your team's release cadence and risk tolerance
  

• Building up tests for realistic Python modules and mini-projects
  
• Seeing how to go from no tests at all to a useful, automated test suite
  
• Applying the same approach to your own personal projects or company codebase
  

• How to start when your project has 0% test coverage
  
• How to begin testing complex or legacy code that feels intimidating
  
• How to design a testing setup that scales with your team and project
  
• How to integrate tests into CI/CD so they run automatically and reliably
  

• Use pytest as a tool to keep your codebase stable and maintainable
  
• Implement tests for real-world projects without overthinking where to begin
  
• Design and maintain a scalable testing system integrated into CI/CD
  
• Follow a practical path from 0% to high test coverage that supports refactoring and new feature delivery
  

• Junior-, Mid-, or Senior-level Developers working with Python who want to deepen their knowledge and skills to advance their careers and apply these improvements within their organizations.
  
• Development Teams and Tech Leads at companies of any size will also benefit, as the course covers pytest - a mature and scalable testing framework that can be integrated into existing codebases to support legacy code refactoring and accelerate new features development.
  
• Anyone who wants to level up their Python skills and learn advanced concepts like testing.
  
• It is valuable for a wide range of developers, including software engineers, machine learning and data science practitioners, data analysts, data engineers, and both front-end and back-end web developers.
  
• The course is also well-suited for individuals working on any personal projects or building early-stage startups who aim to write clean, maintainable code.