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Python DevOps Best Practices and Automation · LearnSpace
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courseraПрограммирование

Python DevOps Best Practices and Automation

Курс от Packt
Средний≈ 13.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain the skills to integrate Python into DevOps practices by mastering best practices for automation, API interaction, static typing, testing, and CI/CD pipelines. You’ll explore using Python to interact with APIs, automate DevOps tasks, and implement best practices for writing robust code. You'll also dive deep into testing using Pytest and configuring a full CI/CD pipeline to automate development workflows and streamline deployments. The course begins by introducing API interaction using the requests library, where you’ll learn how to send GET and POST requests, handle authentication, and manage HTTP errors. Then, you'll master static typing in Python, applying type hints to ensure cleaner and more maintainable code. This section also includes techniques for flexible typing with Python's type system and best practices for using generics and type hints in decorators and generators. You will then learn how to implement robust testing practices with Pytest, including writing assertions, handling test failures, mocking external dependencies, and creating reusable fixtures. The course also provides a comprehensive guide to building a CI/CD pipeline, showing how to automate testing, versioning, deployment, and more with tools like GitHub Actions, Pytest, and Semantic Release. This course is intended for intermediate Python developers looking to streamline their DevOps practices. Familiarity with Python basics and DevOps concepts is required. By the end of the course, you'll have the knowledge and hands-on experience to automate processes, ensure code quality, and integrate DevOps best practices into your Python projects. By the end of the course, you will be able to build robust Python applications, automate DevOps workflows, write efficient and maintainable code, and create end-to-end automated CI/CD pipelines.

Навыки, которые вы освоите

Continuous DeploymentPackage and Software ManagementTest AutomationCI/CDContinuous IntegrationAuthenticationsPython ProgrammingSoftware TestingMaintainabilityConfiguration ManagementRelease ManagementApplication Programming Interface (API)IT AutomationAutomationDevops ToolsTest ToolsTest Script DevelopmentDevOpsUnit TestingSoftware Quality (SQA/SQC)

Программа курса

5 модулей · 98 учебных материалов

01API Interaction with the Requests Library16 материалов

API Interaction with the Requests Library

Introduction to the Course 'Python DevOps Best Practices and Automation'ЧтениеFull Specialization ResourcesЧтениеSection IntroductionВидеоGenerating a GitHub Personal Access Token for API AccessВидео

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Python DevOps Best Practices and Automation
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Обучение на Coursera

≈ 13.5 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Индонезийский, Испанский, Японский

Часть программы вашего университета
Making GET Requests to Retrieve DataВидео
Passing Data with Query ParametersВидео
Sending Data with POST RequestsВидео
Understanding and Interpreting HTTP Status CodesВидео
Automatic Exception Raising for HTTP ErrorsВидео
Implementing Basic AuthenticationВидео
Securing Requests with Token-Based AuthenticationВидео
Preventing Indefinite Hangs with TimeoutsВидео
Implementing a Simple Retry MechanismВидео
Advanced Retries: Exponential Backoff and JitterВидео
Building Resilient API Clients with Python RequestsDIALOGUE
API Interaction with the Requests Library - AssessmentЗадание
02Writing Robust Code with Static Typing20 материалов

Writing Robust Code with Static Typing

Section IntroductionВидеоConfiguring Pylance in VS Code for Optimal Type CheckingВидеоAn Introduction to Python's Type Hint SystemВидеоAvoiding Common Pitfalls in Type HintingВидеоApplying Type Hints to ListsВидеоTyping Dictionaries, Tuples, and SetsВидеоUsing Union and Optional for Flexible TypingВидеоCreating Strictly-Typed Dictionaries with TypedDictВидеоAdding Type Hints to ClassesВидеоResolving Type Dependencies with Forward ReferencesВидеоAn Introduction to Generic TypesВидеоCreating Constrained Type VariablesВидеоDefining Bounded Type VariablesВидеоUsing Generics with ClassesВидеоApplying Type Hints to DecoratorsВидеоEnhancing Type Information for DecoratorsВидеоAdding Type Hints to GeneratorsВидеоTyping Iterables and IteratorsВидеоMastering Advanced Typing Patterns in PythonDIALOGUEWriting Robust Code with Static Typing - AssessmentЗадание
03Mastering Automated Testing with Pytest23 материалов

Mastering Automated Testing with Pytest

Section IntroductionВидеоWriting Assertions with PytestВидеоA Practical Guide to Test-Driven Development (TDD)ВидеоAnalyzing Test Failures and Handling Floating-Point ComparisonsВидеоVerifying Exception Handling in TestsВидеоHandling Tests That Are Expected to FailВидеоConfiguring Pytest for Your ProjectsВидеоConditionally Skipping TestsВидеоMarking and Tracking Expected FailuresВидеоOrganizing Tests with Custom MarkersВидеоSimplifying Test Setups with FixturesВидеоControlling Fixture Lifecycle with ScopesВидеоCreating Sharable Fixtures with conftest.pyВидеоRunning Tests with Multiple Inputs via ParametrizationВидеоAdvanced Parametrization TechniquesВидеоCore Concepts of MockingВидеоUsing the patch Decorator and mocker FixtureВидеоCustomizing Mock Object BehaviorВидеоSimulating Exceptions and Dynamic Return ValuesВидеоAdvanced Side Effects with CallablesВидеоChoosing Between Mock and MagicMockВидеоAutomated Testing in Pytest: Markers, Fixtures, and Mocking FundamentalsDIALOGUEMastering Automated Testing with Pytest - AssessmentЗадание
04Best Practices for Structuring Python Projects11 материалов

Best Practices for Structuring Python Projects

Section IntroductionВидеоUnderstanding Python ModulesВидеоOrganizing Code with Python PackagesВидеоStructuring Projects with SubpackagesВидеоAbsolute vs. Relative Imports: A ComparisonВидеоTroubleshooting Common Import ErrorsВидеоExecuting Modules as ScriptsВидеоModern Project Configuration with pyproject.tomlВидеоIntegrating Tests into a Multi-File Project StructureВидеоStructuring Python Projects: Imports, Packages, and Project OrganizationDIALOGUEBest Practices for Structuring Python Projects - AssessmentЗадание
05Building a Full CI/CD Pipeline for Python Projects28 материалов

Building a Full CI/CD Pipeline for Python Projects

Section IntroductionВидеоCreating Command-Line Interfaces with ClickВидеоSelecting the Right CI/CD Tools for PythonВидеоAn Overview of Our CI/CD Pipeline DesignВидеоSetting Up the GitHub Repository and Local WorkspaceВидеоOutlining the Project Implementation PlanВидеоDefining Project Metadata with pyproject.tomlВидеоDeveloping the Core HTTP Checker ModuleВидеоBuilding the CLI – Part 1ВидеоBuilding the CLI – Part 2ВидеоCreating a Minimal Viable Workflow with GitHub ActionsВидеоIntegrating Linting and Code Formatting ChecksВидеоIncorporating Static Type and Security AnalysisВидеоConfiguring Pytest for the CI/CD PipelineВидеоWriting Tests for the HTTP CheckerВидеоDeveloping Tests for the CLIВидеоAutomating Test Execution in the WorkflowВидеоPackaging the Project as a Distributable LibraryВидеоSecuring PyPI Publishing with OIDCВидеоAutomating Versioning with Semantic ReleaseВидеоIntegrating Semantic Release into the PipelineВидеоDeploying to the TestPyPI RepositoryВидеоPublishing Your Package to the Official PyPIВидеоOptimizing the CI/CD Workflow with Release AssetsВидеоConclusion to the Course 'Python DevOps Best Practices and Automation'ЧтениеBuilding a Full CI/CD Pipeline for Python Projects - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание