Python For Devops: Mastering Real-World Automation

20+ hours, 35+ quizzes and 25+ coding labs for you to master Python to build and automate powerful DevOps tools

What you'll learn

Master Python fundamentals from variables and data structures to functions and classes.

Write elegant and memory-efficient code using advanced features like generators and decorators.

Implement robust error handling with try/except blocks and custom exceptions to build resilient scripts.

Integrate structured JSON logging into your applications for improved observability and troubleshooting.

Confidently parse, process, and generate essential data formats like JSON, YAML, and CSV.

Automate file and directory management using modern pathlib and shutil.

Run external system commands securely and capture their output using the subprocess module.

Automate REST API interactions by sending GET/POST requests and handling authentication with the requests library.

Build resilient API clients that handle timeouts and implement retry logic with exponential backoff.

Write and run professional unit tests using the powerful pytest framework.

Use fixtures and parametrization to write clean, reusable, and data-driven tests.

Isolate dependencies and test complex interactions by creating and configuring mocks.

Structure your code professionally using Python modules and packages for better maintainability.

Build and distribute your own installable command-line tools using pyproject.toml and entry points.

Requirements

Familiarity with basic programming concepts (variables, loops, functions) from any scripting or programming language.

Basic experience using a command-line terminal for navigating directories and executing commands.

A high-level understanding of common DevOps concepts like APIs, CI/CD, and infrastructure automation.

The ability to install software on your computer, such as Python itself and third-party packages using pip.

A desire to move beyond simple shell scripts and build robust, maintainable, and production-ready automation.

Basic familiarity with git to clone the course repository and naviage different branches

Description

Welcome to the definitive Python for DevOps course! Are you ready to move beyond simple scripts and start building powerful, reliable, and production-grade automation? This course is meticulously designed to equip you, the DevOps engineer, SRE, or system administrator, with the essential Python skills to automate your infrastructure and streamline your workflows. It offers a highly practical curriculum, packed with quizzes and coding labs for you to practice everything we discuss in the lectures.Why Learn Python for DevOps?Python has become the universal language for infrastructure automation, and for good reason. Mastering it is a critical step for any modern DevOps professional. Here’s why:Automate Everything: Stop doing repetitive manual work! With Python, you can automate interactions with any REST API, manage cloud resources, update configurations, and orchestrate complex deployment pipelines. This course will teach you how to write scripts that do the work for you.Become a More Versatile and Valuable Engineer: Python is the "glue" that connects different systems. By learning to script interactions between your CI/CD tools, monitoring platforms, and cloud services, you become the go-to person for solving complex integration challenges, making you an indispensable part of your team.Write Robust, Maintainable Tools: A simple script might work once, but professional automation needs to be reliable. This course goes beyond basics to teach you how to write code that includes proper error handling, logging, and automated tests, ensuring the tools you build are trusted and easy to maintain.Boost Your Career: Proficiency in Python automation is one of the most in-demand skills in the tech industry. Adding these skills to your resume will make you a more attractive candidate for new roles, promotions, and higher-paying opportunities.By investing in this course, you’re not just learning a language; you’re acquiring a powerful toolkit to solve real-world DevOps problems efficiently and reliably.Why Choose This Course?This course is built from the ground up with a DevOps focus, offering a unique blend of core Python concepts and their practical application in infrastructure environments.Practical, DevOps-Focused Approach: We won't be building web apps or doing data science. Every lecture, example, and exercise is tailored to the world of DevOps. You'll work with files, APIs, system commands, and data formats like JSON and YAML - the things you use every day.Practice, Practice, Practice: We go beyond theoretical discussions and dive deep into coding everything we discuss. In addition to the video lectures, the course is packed with quizzes and coding labs that will help you solidify every concept we discuss!Go Beyond the Basics: This isn't just a "learn Python syntax" course. We dive deep into advanced, powerful features like generators for memory-efficient data processing, decorators for adding reusable functionality, context managers for safe resource handling, logging for production-grade, robust application logging, and much more! You'll learn to write code that is not just functional, but also elegant and efficient.Focus on Production-Ready Code: Learn how to build automation that you can trust in production. We dedicate entire sections to crucial topics like structured logging, advanced exception handling, implementing retries with exponential backoff, and, most importantly, automated testing with pytest.Which Skills Will You Acquire During This Course?As you go through this course, you will gain a comprehensive and valuable set of skills, including:Master Python Fundamentals: Build a rock-solid foundation in Python syntax, data structures (lists, dictionaries, sets), control flow, functions, and object-oriented principles.Leverage Advanced Python Features: Harness the power of generators for efficient data pipelines and decorators for adding cross-cutting concerns like logging and retries without cluttering your code.Write Resilient, Production-Grade Scripts: Implement structured logging for better observability and craft robust exception handling logic to make your automation fail gracefully.Ensure Reliability with Automated Testing: Master pytest to write effective unit tests. You'll learn everything from basic assertions and fixtures to advanced techniques like parametrization and isolating dependencies with mocks.Automate System and File Operations: Confidently manage the file system using modern pathlib and run external commands securely with the subprocess module.Interact With Any REST API: Master the requests library to send GET and POST requests, handle various authentication methods (basic, token), and build resilient clients that can handle timeouts and retries.Handle Essential Data Formats: Fluently parse, process, and generate the data formats that power DevOps: JSON, YAML, and CSV.Build and Package Professional Tools: Structure your Python projects with modules and packages, and use pyproject.toml to create and distribute your own installable command-line tools.Get ready to transform your capabilities and elevate your career. Let's start building powerful DevOps automation together!

Overview

Section 1: Welcome and Introduction

Lecture 1 Welcome to the course!

Lecture 2 How to make the most of this course

Lecture 3 Aligning expectations

Lecture 4 Course resources

Section 2: Setting Up and Running Python

Lecture 5 Section overview

Lecture 6 Why Python for DevOps?

Lecture 7 Installing and configuring Python

Lecture 8 Managing multiple Python installations with pyenv

Lecture 9 Overview of virtual environments

Lecture 10 Creating our first virtual environment

Lecture 11 Managing multiple virtual environments

Lecture 12 IMPORTANT! Installing Python 3.12.9 and creating the venv for the course

Lecture 13 The Python REPL

Lecture 14 Writing and running Python files

Lecture 15 Using JupyterLab for interactive code execution

Section 3: Python Fundamentals

Lecture 16 Section overview

Lecture 17 Variables

Lecture 18 Comments

Lecture 19 Numbers

Lecture 20 Strings

Lecture 21 Hands-on: Disk usage calculation

Lecture 22 Introduction to lists

Lecture 23 Modifying list contents

Lecture 24 Hands-on: Practicing working with lists

Lecture 25 Tuples

Lecture 26 Introduction to sets

Lecture 27 Set operations

Lecture 28 Hands-on: Practicing working with sets

Lecture 29 Differentiating between lists, tuples, and sets

Lecture 30 Introduction to dictionaries

Lecture 31 Dictionary operations

Lecture 32 Hands-on: Practicing working with dictionaries

Lecture 33 Introduction to conditional code execution

Lecture 34 If/elif/else statements

Lecture 35 Guard clauses

Lecture 36 For and while loops

Lecture 37 Break and continue statements

Lecture 38 Introduction to list comprehension

Lecture 39 List, set, and dictionary comprehension

Lecture 40 Introduction to functions

Lecture 41 Defining functions and returning values

Lecture 42 Parameters and arguments

Lecture 43 Docstrings

Lecture 44 Hands-on: Practicing working with functions

Lecture 45 The range function

Lecture 46 Enumerate and ZIP functions

Lecture 47 Introduction to classes

Lecture 48 Class methods

Lecture 49 Inheritance

Lecture 50 Introduction to *args and **kwargs syntax

Lecture 51 Order in *args and **kwargs arguments

Lecture 52 Calling functions: *args and **kwargs

Lecture 53 Lambda functions

Lecture 54 Sorting collections with lambda

Lecture 55 Transforming collections with lambda

Lecture 56 Filtering collections with lambda

Section 4: Generators and Decorators

Lecture 57 Section overview

Lecture 58 The iteration protocol

Lecture 59 Iterator and iterable code demo

Lecture 60 Generator syntax

Lecture 61 The yield statement

Lecture 62 Pausing and resuming generator execution

Lecture 63 State in generators

Lecture 64 Generator exhaustion

Lecture 65 Return vs. yield statements

Lecture 66 Hands-on: Practicing working with generators

Lecture 67 Coding lazy pipelines

Lecture 68 Functions as first-class citizens

Lecture 69 Factory functions

Lecture 70 Functions in data structures

Lecture 71 Introduction to decorators

Lecture 72 Arguments in decorators

Lecture 73 Handling return values in decorators

Lecture 74 Handling exceptions in decorators

Lecture 75 The functools.wraps utility

Lecture 76 Stacking decorators

Section 5: Error Handling and Context Managers

Lecture 77 Section introduction

Lecture 78 Exceptions syntax

Lecture 79 Thinking in exceptions

Lecture 80 Inspecting built-in exceptions

Lecture 81 OS and KeyError exceptions

Lecture 82 Index, Value, and TypeError exceptions

Lecture 83 Attribute and ImportError exceptions

Lecture 84 The raise statement

Lecture 85 Raising exceptions

Lecture 86 Defining custom exceptions

Lecture 87 Adding context to custom exceptions

Lecture 88 Manual context cleanup

Lecture 89 The context manager protocol

Lecture 90 Context managers and the "with" statement

Lecture 91 Defining custom context managers

Lecture 92 The contextmanager decorator

Section 6: Logging for Operations

Lecture 93 Section introduction

Lecture 94 Why logging?

Lecture 95 Introduction to logging anatomy

Lecture 96 Hands-on: Practicing logging anatomy

Lecture 97 Log levels

Lecture 98 File handlers

Lecture 99 Rotating log files by size

Lecture 100 Rotating log files by time

Lecture 101 Structured logging with JSON

Lecture 102 Extra fields and exceptions in structured logging

Lecture 103 Log configuration with INI files

Lecture 104 Log configuration with dictionaries

Lecture 105 Log configuration with JSON files

Lecture 106 Dynamic log configuration

Section 7: Files, Regex, and Serialization

Lecture 107 Section introduction

Lecture 108 The Path object

Lecture 109 Listing directory contents, reading and writing files

Lecture 110 Read and write modes deep-dive

Lecture 111 Read and write methods deep-dive

Lecture 112 Regex: Introduction and essentials

Lecture 113 Regex: Quantifiers, greedy, and non-greedy search

Lecture 114 Regex: Capturing groups

Lecture 115 Regex: Non-capturing groups

Lecture 116 Regex: Back-references

Lecture 117 Regex: findall() and finditer()

Lecture 118 Regex: String splitting

Lecture 119 Regex: String substitution

Lecture 120 JSON deserialization

Lecture 121 JSON serialization

Lecture 122 Introduction to YAML operations

Lecture 123 YAML serialization and deserialization

Lecture 124 Reading CSV files

Lecture 125 Writing CSV files

Section 8: System Interaction and Automation

Lecture 126 Section introduction

Lecture 127 Accessing environment variables

Lecture 128 Setting and deleting environment variables

Lecture 129 Dotenv files

Lecture 130 Listing directory contents

Lecture 131 Creating directories

Lecture 132 Deleting files and directories

Lecture 133 Copying files and directories

Lecture 134 Moving files and directories

Lecture 135 Temporary files

Lecture 136 Temporary directories

Lecture 137 Introduction to subprocesses

Lecture 138 Handling subprocess errors

Lecture 139 Handling expired timeouts

Section 9: Interacting with APIs using requests

Lecture 140 Section introduction

Lecture 141 GET requests

Lecture 142 Query parameters

Lecture 143 POST requests

Lecture 144 HTTP status codes

Lecture 145 Raise for HTTP status

Lecture 146 Basic authentication

Lecture 147 Token-based authentication

Lecture 148 Handling timeouts

Lecture 149 Retries: Simple strategy

Lecture 150 Retries: Exponential backoff with jitter

Section 10: Static Typing and Type Hints

Lecture 151 Section introduction

Lecture 152 Introduction to type hints

Lecture 153 Type hints: Common pitfalls

Lecture 154 Typing lists

Lecture 155 Typing dictionaries, tuples and sets

Lecture 156 Union and optional types

Lecture 157 Typed dictionaries

Lecture 158 Typing classes

Lecture 159 Forward type references

Lecture 160 Introduction to generics

Lecture 161 Constrained type variables

Lecture 162 Bounded type variables

Lecture 163 Generics and classes

Lecture 164 Typing decorators

Lecture 165 Improving decorator types

Lecture 166 Typing generators

Lecture 167 Typing iterables and iterators

Section 11: Automated Testing with Pytest

Lecture 168 Section introduction

Lecture 169 Assertions in Pytest

Lecture 170 Hands-on: Test-driven implementation

Lecture 171 Inspecting failure output and float approximation

Lecture 172 Testing exceptions

Lecture 173 Marking tests as expected failures

Lecture 174 Pytest configuration

Lecture 175 Skipping tests

Lecture 176 Indicating expected failures

Lecture 177 Custom markers

Lecture 178 Introduction to fixtures

Lecture 179 Fixture scopes

Lecture 180 Sharing fixtures in conftest.py

Lecture 181 Introduction to parametrization

Lecture 182 Using the param function

Lecture 183 Mocking fundamentals

Lecture 184 Patch decorator and mocker fixture

Lecture 185 Configuring mock objects

Lecture 186 Setting exceptions and lists as side effect

Lecture 187 Using callable as side effect

Lecture 188 Mock vs. MagicMock

Section 12: Structuring Python Projects

Lecture 189 Section introduction

Lecture 190 Python modules

Lecture 191 Python packages

Lecture 192 Introduction to subpackages

Lecture 193 Absolute vs. Relative imports

Lecture 194 Common import errors and how to fix them

Lecture 195 Running Python modules

Lecture 196 Pyproject.toml file

Lecture 197 Adding tests to multi-file projects

Section 13: Conclusion

Lecture 198 Congratulations!

DevOps Engineers, SREs, and Cloud Engineers aiming to master Python for building powerful and reliable automation.,System Administrators looking to level up from shell scripting to building maintainable, production-grade tools in Python.,Software Developers working in a DevOps culture who need to automate testing, deployments, and infrastructure management.,Test Automation Engineers who want to leverage the full power of Python and pytest for creating robust test suites.,IT Professionals and students aspiring to a career in DevOps, Cloud, or Site Reliability Engineering and who need a core practical skill.,Anyone who wants to move beyond writing simple scripts and learn how to build professional, well-tested, and packaged Python applications.