Pytest Course: Practical Testing of Real-World Python Code

Pytest Course: Practical Testing of Real-World Python Code

Learn how to test real-world Python applications with pytest, from unit tests to full CI/CD automation

What you’ll learn

• 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.

Requirements

• 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

Description

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.

No advanced background is required:

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

Writing your first tests properly

• Using assertions effectively
• Testing functions, classes, and modules

Working with pytest fixtures

• 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

Parametrization & powerful test patterns

• 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

Organizing a scalable test suite

• 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 real-world Python applications

• 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

Mocking, patching & test doubles

• 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

From 0% to 100% test coverage (practically)

• 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

Working effectively with legacy code

• 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

Test-first and test-driven approaches

• 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

Integrating pytest into CI/CD pipelines

• 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

Realistic examples & complete workflows

• 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

Throughout the course, you won’t just learn how pytest works – you’ll learn how to think about testing as a practical tool:

• 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

By the end of this course, you’ll have a clear, hands-on understanding of pytest and Python testing. You’ll be able to:

• 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

After completing the course, you won’t just "know pytest" – you’ll be able to apply it confidently in your daily work, across teams, and in any Python project where clean, reliable, and maintainable code matters.

Who this course is for:

• 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.

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