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- Everything to get started with Python testing, from the basics, to some advanced insights
- How to setup a cloud-based development environment
- Core DevOps principles in Python
- Some experience with Python. Any testing exposure is good but not a requirement
Noah and Alfredo have decades of experience testing with Python in major production environments. They are the authors of the book Testing in Python and Python for DevOps (O'Reilly). Learn from the best on how to get started and advance your automation with easy examples and code to follow up.
Why test at all?
Leveling up from simple scripts to robust implementations
Python, Pytest, Tox supported versions
Configuring the environment
Setting up and using Git
Setting up and using Virtualenv
Installing packages and dependencies
Setup Visual Code code
Setup and use Vim
Setup and Use ZSH/Bash
Using Cloud-based development environments
Functions, Classes, and test methods
Special test class methods
Good naming patterns
Introduction to Pytest
The most simple test possible
Why is Pytest important?
The power of assert
Pytest vs. Unittest
Setting up and teardown of xunit-style tests
Code Formatting with Python Black
Debugging with Pytest
How to debug code
Using a debugger
Python Debugger (PDB) integration
Pytest fixtures and plugins
What are fixtures?
Creating new fixtures
Advanced Fixture usage
Why and when to monkeypatch?
monkeypatching is hard
The simplest monkeypatching
Automatic and global monkeypatching
When not to monkeypatch
Testing matrix with Tox
Testing different Python versions
Expanding the testing matrix
Linting and other validations
Continuous Integration and Continuous Delivery
What is Continuous Integration and Continuous Delivery and Why Do They Matter?
GCP Cloud Build
Continuous Delivery for Hugo Static Site from Zero using AWS Code Pipeline
Case Studies and War Stories
Testing Click Commandline Tools
War Story: The Health Check that wasn’t wrong
War Story: The Nine Circles of Hell While Parsing XML
War Story: The Mysterious Vanishing Servers
Writing clean, testable, high-quality code in Python
Increase reliability in data science and machine learning projects with CircleCI
Data science project quality
Create a final portfolio project
- Python developers, college students, DevOps engineers, and data scientists
An introduction to Configuring Environment section.
Learn to set up an AWS Cloud9 based development from scratch.
In this screencast learn how to setup Docker in a Cloud-based development environment. Also, learn about some of the pitfalls of a Cloud-based environment.
Introduction to the Pytest Reporting section.