This repository contains all the code and materials for my Python for DevOps: Mastering Real-World Automation course. If you haven't enrolled yet, check the link below for a discount!

Note: You can find the code for the section about CI/CD for Python in the following repository: https://github.com/lm-academy/python-devops-cicd-project

Check my other courses:

• 👉 The Definitive Helm Course: From Beginner to Master
• 👉 Mastering Terraform: From Beginner to Expert
• 👉 Mastering GitHub Actions: From Beginner to Expert
• 👉 The Complete Docker and Kubernetes Course: From Zero to Hero
• 👉 Write better code: 20 code smells and how to get rid of them

I'm thrilled to have you here! This repository contains all the code, examples, and supplementary materials for my Python for DevOps course. My goal is to provide you with practical, real-world examples that will help you master Python for your automation and operational tasks.

To run the code examples locally, you'll need to set up your environment correctly. It is highly recommended to use a virtual environment to keep project dependencies isolated.

IMPORTANT: Make sure to have Python 3.12.9 installed to follow along without running into any issues 😊 Up until the release of this course, Python 3.13.x presents a bug that prevents some native logging functionality from working correctly. Hopefully, this will be fixed in later Python releases soon, but to ensure a smooth experience, Python 3.12.9 is recommended.

1. Clone the Repository

   git clone https://github.com/lm-academy/python-devops.git
   cd python-devops

2. Create and Activate a Virtual Environment Create a new virtual environment (the conventional name is .venv):

   python3 -m venv .venv

   Activate it for your current shell session:

   # On macOS and Linux
   source .venv/bin/activate
   
   # On Windows
   .venv\Scripts\activate

   Your shell prompt should now be prefixed with (.venv), indicating the environment is active.

3. Install Dependencies The required packages for this course are listed in requirements.txt. Install them using pip:

   pip install -r requirements.txt

4. Launch JupyterLab Many of the initial lectures use Jupyter Notebooks (.ipynb files) for interactive demonstrations. You can launch the JupyterLab interface by running:

   jupyter lab

   This will open a new tab in your web browser, allowing you to navigate the project folders and open any .ipynb notebook files.

This repository has two primary branches to support your learning:

• main branch: This branch contains the final, completed code for every lecture. Use it as a reference if you get stuck or want to see the finished solution.
• start branch: This branch is designed for you to follow along with the hands-on lectures. It contains the initial state for each lesson, including notebooks and markdown files, but with the code blocks often left for you to complete.

To switch to the start branch and code along with me, run:

git checkout start

The repository is organized into modules, each corresponding to a section in the course. Here is an overview of what you will learn:

• virtual-envs: Learn why virtual environments are essential and how to use Python's built-in venv module to create and manage isolated project environments.

• python-fundamentals: Master the core building blocks of Python. This module covers variables, comments, data types (str, int, list, dict, set, tuple), conditionals, loops, functions, including *args and **kwargs, classes, among other topics

• generators-decorators: Explore advanced Python features that promote clean and efficient code. This section covers first-class functions, memory-efficient generators with yield, and powerful decorators for adding functionality like timing and retries.

• error-handling: Build resilient scripts by mastering error handling. This module covers the try...except block, raising built-in and custom exceptions, and using context managers for safe resource management.

• logging: Go beyond print() statements and learn to use Python's logging module. You'll explore logging levels, handlers, formatters, file-based logging with rotation, and modern structured logging with JSON.

• files-regex-data-formats: Dive into file I/O and data handling. You'll learn to work with filesystem paths using pathlib, process text with regular expressions (re), and handle common data serialization formats like CSV, JSON, and YAML.

• interacting-with-os: Write scripts that interact with the operating system. This module covers managing environment variables, executing external commands with subprocess, handling filesystem operations with os and shutil, and creating temporary files.

• http-requests: Learn to interact with web services and REST APIs using the powerful requests library. Topics include making GET/POST requests, passing parameters, handling authentication, and implementing robust error handling with retries and timeouts.

• typing: Improve code clarity and catch bugs early with Python's type hinting system. This module introduces basic and advanced types from the typing module, generics with TypeVar, and how to use static analysis tools like mypy to validate your code.

• automated-testing: Write professional, automated tests for your DevOps scripts using pytest. You will learn about assertions, fixtures for setup/teardown, markers for organizing tests, parametrization for DRY tests, and mocking external dependencies.

• multi-file-projects: Learn to structure larger Python applications. This module covers creating modules and packages (__init__.py), using absolute vs. relative imports, defining project metadata with pyproject.toml, and setting up a testable project layout.

I'm looking forward to seeing you in the course!