2 godzin(y) temu -
[center]![[Obrazek: fe589a1327b4d3b85bb4a0458ad64927.jpg]](https://i126.fastpic.org/big/2025/1227/27/fe589a1327b4d3b85bb4a0458ad64927.jpg)
Pytest Course: Practical Testing Of Real-World Python Code
Published 12/2025
Created by Artem Istranin
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 69 Lectures ( 11h 13m ) | Size: 11.3 GB [/center]
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.
Implement tests for real-world applications without overthinking or getting stuck on where to start.
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.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 pytestSetting up pytest in new and existing Python projectsUnderstanding test discovery, naming conventions, and basic structureWriting your first tests properlyUsing assertions effectivelyTesting functions, classes, and modulesAvoiding typical mistakes when starting with testingWorking with pytest fixturesCreating reusable fixtures for data, configuration, and environment setupUnderstanding fixture scopes (function, class, module, session) and when to use whichSharing fixtures across tests and modulesParametrization & powerful test patternsWriting one test that covers multiple inputs, edge cases, and scenariosStructuring and naming tests for clarity and long-term maintainabilityUsing parametrization to quickly increase coverage without duplicating codeOrganizing a scalable test suiteStructuring tests for small scripts and large applicationsGrouping, naming, and tagging tests so teams can work efficientlyUsing markers to manage slow, flaky, integration, or smoke testsTesting real-world Python applicationsTesting business logic, services, and data-processing codeStrategies for testing code that works with files, APIs, databases, or external servicesReducing flakiness and isolating side effectsMocking, patching & test doublesWhen to mock - and when not toUsing monkeypatch and pytest-mock to replace or isolate dependenciesWriting code that's easier to test without unnecessary complexityFrom 0% to 100% test coverage (practically)Using coverage tools (like pytest-cov) to see what's actually testedPrioritizing what to test first in large, untested codebasesDesigning a realistic path from little or no coverage to high, meaningful coverageWorking effectively with legacy codeAdding tests around existing, fragile code without breaking itBuilding a "safety net" before making changes or refactorsGradually decoupling and improving tightly coupled code using tests as a guideTest-first and test-driven approachesWhen TDD is helpful - and when it's overkillUsing tests to drive the design of new featuresUsing tests to gain confidence and move faster, not slowerIntegrating pytest into CI/CD pipelinesConfiguring workflows to run tests automatically on every push, pull request, or releaseSpeeding up the test suite by parallelizing tests with pytest-xdistHandling slow or flaky tests in CI and shaping a testing strategy that matches your team's release cadence and risk toleranceRealistic examples & complete workflowsBuilding up tests for realistic Python modules and mini-projectsSeeing how to go from no tests at all to a useful, automated test suiteApplying the same approach to your own personal projects or company codebaseThroughout the course, you won't just learn how pytest works - you'll learn how to think about testing as a practical tool:How to start when your project has 0% test coverageHow to begin testing complex or legacy code that feels intimidatingHow to design a testing setup that scales with your team and projectHow to integrate tests into CI/CD so they run automatically and reliablyBy 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 maintainableImplement tests for real-world projects without overthinking where to beginDesign and maintain a scalable testing system integrated into CI/CDFollow a practical path from 0% to high test coverage that supports refactoring and new feature deliveryAfter 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.
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.
Homepage
Pytest Course: Practical Testing Of Real-World Python Code
Published 12/2025
Created by Artem Istranin
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 69 Lectures ( 11h 13m ) | Size: 11.3 GB [/center]
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.
Implement tests for real-world applications without overthinking or getting stuck on where to start.
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.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 pytestSetting up pytest in new and existing Python projectsUnderstanding test discovery, naming conventions, and basic structureWriting your first tests properlyUsing assertions effectivelyTesting functions, classes, and modulesAvoiding typical mistakes when starting with testingWorking with pytest fixturesCreating reusable fixtures for data, configuration, and environment setupUnderstanding fixture scopes (function, class, module, session) and when to use whichSharing fixtures across tests and modulesParametrization & powerful test patternsWriting one test that covers multiple inputs, edge cases, and scenariosStructuring and naming tests for clarity and long-term maintainabilityUsing parametrization to quickly increase coverage without duplicating codeOrganizing a scalable test suiteStructuring tests for small scripts and large applicationsGrouping, naming, and tagging tests so teams can work efficientlyUsing markers to manage slow, flaky, integration, or smoke testsTesting real-world Python applicationsTesting business logic, services, and data-processing codeStrategies for testing code that works with files, APIs, databases, or external servicesReducing flakiness and isolating side effectsMocking, patching & test doublesWhen to mock - and when not toUsing monkeypatch and pytest-mock to replace or isolate dependenciesWriting code that's easier to test without unnecessary complexityFrom 0% to 100% test coverage (practically)Using coverage tools (like pytest-cov) to see what's actually testedPrioritizing what to test first in large, untested codebasesDesigning a realistic path from little or no coverage to high, meaningful coverageWorking effectively with legacy codeAdding tests around existing, fragile code without breaking itBuilding a "safety net" before making changes or refactorsGradually decoupling and improving tightly coupled code using tests as a guideTest-first and test-driven approachesWhen TDD is helpful - and when it's overkillUsing tests to drive the design of new featuresUsing tests to gain confidence and move faster, not slowerIntegrating pytest into CI/CD pipelinesConfiguring workflows to run tests automatically on every push, pull request, or releaseSpeeding up the test suite by parallelizing tests with pytest-xdistHandling slow or flaky tests in CI and shaping a testing strategy that matches your team's release cadence and risk toleranceRealistic examples & complete workflowsBuilding up tests for realistic Python modules and mini-projectsSeeing how to go from no tests at all to a useful, automated test suiteApplying the same approach to your own personal projects or company codebaseThroughout the course, you won't just learn how pytest works - you'll learn how to think about testing as a practical tool:How to start when your project has 0% test coverageHow to begin testing complex or legacy code that feels intimidatingHow to design a testing setup that scales with your team and projectHow to integrate tests into CI/CD so they run automatically and reliablyBy 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 maintainableImplement tests for real-world projects without overthinking where to beginDesign and maintain a scalable testing system integrated into CI/CDFollow a practical path from 0% to high test coverage that supports refactoring and new feature deliveryAfter 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.
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.
Homepage
Cytat:https://rapidgator.net/file/f6d7027e7ba4...2.rar.html
https://rapidgator.net/file/12cc40aa9423...1.rar.html
https://rapidgator.net/file/20f420d0709e...0.rar.html
https://rapidgator.net/file/4c32bb198ecd...9.rar.html
https://rapidgator.net/file/692720ae7ae6...8.rar.html
https://rapidgator.net/file/871d19591be8...7.rar.html
https://rapidgator.net/file/77f9fb6f53cd...6.rar.html
https://rapidgator.net/file/0a627fb110a1...5.rar.html
https://rapidgator.net/file/d0d51a38ab54...4.rar.html
https://rapidgator.net/file/29054caff598...3.rar.html
https://rapidgator.net/file/56f284371336...2.rar.html
https://rapidgator.net/file/ad17cdb5fad7...1.rar.html
https://nitroflare.com/view/C44044A0FB88...part12.rar
https://nitroflare.com/view/2CAB901EFFB9...part11.rar
https://nitroflare.com/view/72E1BA47D6F6...part10.rar
https://nitroflare.com/view/25E1DF06B4B6...part09.rar
https://nitroflare.com/view/3C014E478CE8...part08.rar
https://nitroflare.com/view/1AD87A4BF776...part07.rar
https://nitroflare.com/view/21ED4D85F581...part06.rar
https://nitroflare.com/view/858FA66C9DD3...part05.rar
https://nitroflare.com/view/40E32DB0CEF1...part04.rar
https://nitroflare.com/view/13D6DDC08679...part03.rar
https://nitroflare.com/view/3D00D5CB3E8F...part02.rar
https://nitroflare.com/view/CB3840E411E4...part01.rar