There are already suitable introductory lessons for testing Python with Pytest. But there are essential concepts for functional and integration testing that require detailed explanations. In this course, you'll have a better grasp on solving a complex test matrix with ease, including highly functional tests with databases, web services, and containers. We will go from basic test runs that, although sufficient, isn't helping us in the case of failure, all the way to adding advanced techniques expanding on the framework. All of these changes live, debugging as we make progress.
Learn everything you need to know to be proficient with advanced testing in Python techniques, from unit tests to integration testing and functional testing with complex test matrixes.
Topics include:
* Introduction to Pytest fixtures.
* Create powerful plugins with conftest.py
* Monkeypatching, patching, and mocking with Pytest
* Apply patches and mocks globally, automatically, or on-demand per test
* Complex functional testing with remote Docker containers
* Integrate Pytest with Docker containers, assert and run assertions remotely
* Build Command-line tools with Click, run tests agains them
* Create docker command-line tools and easily test them
* Use Github Codespaces to develop continuous integration (CI/CD)
* Github Actions to test multiple Python versions and cloud environments, including Amazon Web Services (AWS) and Google's Cloud Platform (GCP), and Microsoft Azure

(Simplified) Chinese Translation: 已经有适合使用Pytest测试Python的入门课程。但是对于功能和集成测试，有一些基本概念需要详细说明。在本课程中，您将更轻松地解决复杂的测试矩阵，包括使用数据库，Web服务和容器进行的功能强大的测试。我们将从基本的测试运行开始，尽管这些运行足够，但是在失败的情况下并不能帮助我们，一直到添加在框架上扩展的高级技术。所有这些更改都是实时的，并且随着我们的进步而不断调试。
学习精通Python技术的高级测试所需的一切，从单元测试到集成测试和具有复杂测试矩阵的功能测试。
主题包括：
* Pytest固定装置简介。
*使用conftest.py创建功能强大的插件
*使用Pytest进行Monkeypatching，修补和模拟
*全局，自动或按需按需应用补丁和模拟
*使用远程Docker容器进行复杂的功能测试
*将Pytest与Docker容器集成在一起，远程声明和运行声明
*使用Click构建命令行工具，再次运行测试
*创建docker命令行工具并轻松对其进行测试
*使用Github Codespaces开发持续集成（CI / CD）
* Github Actions可测试多个Python版本和云环境，包括Amazon Web Services（AWS）和Google的Cloud Platform（GCP）以及Microsoft Azure