🐍 Python for DevOps — Learning Repository

This repository contains my learning materials and practice projects from the Udemy course “Python for DevOps: Mastering Real-World Automation”.

🛠️ Technologies & Tools

• Python 3.12.9 — Primary programming language
• pyenv — Python version management
• Git & GitHub — Version control
• VS Code — Development environment
• JupyterLab — Interactive notebooks for learning

📌 Features

• The folders are based on exercises or notebooks used during lessons.

📂 Repository Structure

python-devops/
├── virtual-envs/            # Based on an exercise
├── python-fundamentals/     # Jupyter Notebooks with Python concepts
├── generator and decorators # [Coming soon]
├── error-handling/          # [Coming soon]
├── automated-testing/       # [Coming soon]
├── files-regex-data-formats/# [Coming soon]
├── automated-testing        # [Coming soon]
├── http-requests/           # [Coming soon]
├── interacting-with-os/     # [Coming soon]
├── logging/                 # [Coming soon]
├── multi-file-projects/     # [Coming soon]
├── typing                   # [Coming soon]
├── COURSE-README.md         # Course Overview
├── README.md                # Repository Resume
└── requirements.txt         # Python dependencies

📝 Progress Tracker

• Module 1: Environment setup with pyenv
• Module 2: Python fundamentals
• Module 3: Automation basics
• Module 4: CI/CD pipelines
• Module 5: Cloud integration
• Final Project

🔗 Course Information

• Course: Python for DevOps: Mastering Real-World Automation
• Platform: Udemy
• Instructor: Lauro Fialho Müller
• Started: December 2025

📚 Course Structure

• 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.

🕒 Last updated: December 26, 2025