Exploring Python

Automate everything. Build reliable tools. Scale your impact.

A practical guide to Python for SREs and Platform Engineers. Part of the BradPenney.io learning ecosystem.

Live Site: python.bradpenney.io

Overview

You manage 100 servers. You type the same kubectl commands 50 times a day. You manually parse JSON from APIs. You copy-paste deployment steps from a wiki. There's got to be a better way.

There is. It's called Python.

The Automation Language

Python isn't just another programming language—it's the automation language for infrastructure:

• Write a script once, run it on 100 servers
• Parse JSON/YAML with 2 lines of code instead of manual grep
• Turn tribal knowledge into repeatable tools
• Build CLI tools that your whole team can use
• Automate deployments, backups, monitoring, everything

Who This Is For

SREs and Platform Engineers who:

• Need to automate repetitive tasks (deployments, server configs, data migrations)
• Write "glue code" connecting systems (APIs, databases, cloud services)
• Build internal tools for their team (CLI utilities, monitoring scripts)
• Manage infrastructure as code (Terraform, Ansible wrappers)
• Work primarily in terminal environments

You may or may not have traditional programming experience. You might be a sysadmin learning to script, an operator building automation, or a DevOps engineer scaling infrastructure. This site is for you.

Site Structure

Content is organized by skill progression:

📦 Basics (Your First Python Scripts)

The Python knowledge you need to write useful automation today:

• Data Types - Strings, numbers, booleans, None
• Control Structures - If statements, loops, functions
• Data Structures - Lists, dictionaries, sets for organizing data
• File I/O - Reading configs, parsing logs, writing reports

Start here if you're new to Python or scripting.

⚡ Intermediate (Production-Ready Scripts)

Build reliable, maintainable automation:

• Error Handling - Try/except, logging, graceful failures
• Working with APIs - requests library, JSON parsing, authentication
• CLI Tools - argparse for professional command-line interfaces
• Working with YAML - PyYAML for K8s manifests and Ansible
• Subprocess - Calling shell commands from Python safely

Master these to build tools your team will actually use.

🎯 Advanced (Frameworks and Scale)

Build sophisticated systems and frameworks:

• Testing - pytest for reliable automation
• Packaging - Distributing your tools
• Async Python - For concurrent operations
• Web Frameworks - Flask/FastAPI for APIs and dashboards
• Performance - Optimization for large-scale operations

Optional deep dives for when you need maximum capability.

Project Structure

exploring_python/
├── docs/                       # Markdown content organized by skill level
│   ├── basics/                 # Fundamental Python
│   │   ├── data_types/         # Strings, ints, floats, booleans, None
│   │   ├── control_structures/ # If, loops, functions, comprehensions
│   │   └── data_structures/    # Lists, tuples, dicts, sets
│   ├── intermediate/           # Production-ready scripting
│   │   ├── error_handling/     # Exception handling, logging
│   │   ├── apis/               # requests, JSON, authentication
│   │   ├── cli_tools/          # argparse, user interaction
│   │   └── file_formats/       # JSON, YAML, CSV parsing
│   ├── advanced/               # Frameworks and scale
│   │   ├── testing/            # pytest, test automation
│   │   ├── async/              # Asyncio, concurrent operations
│   │   └── web_frameworks/     # Flask, FastAPI
│   ├── images/                 # Site images and screenshots
│   └── stylesheets/            # Custom CSS
├── mkdocs.yaml                 # Site configuration and navigation
├── pyproject.toml              # Poetry dependencies
├── CLAUDE.md                   # Content guidelines and quality standards
└── README.md                   # This file

Development

Prerequisites

• Python 3.8+
• Poetry (for dependency management)

Setup

# Install dependencies
poetry install

# Serve locally (http://localhost:8000)
poetry run mkdocs serve

# Build static site with link validation
poetry run mkdocs build --strict

Writing Content

Articles follow rigorous quality standards:

Content Standards:

• Real-world automation scenario openings
• Code examples with actual output
• Multiple approaches shown (beginner → Pythonic)
• Common pitfalls highlighted
• Practice problems with full solutions
• Key takeaways tables
• Further reading organized by category

Formatting Requirements:

• Function names in backticks (print(), len(), range())
• Blank lines before all lists
• External links validated with WebFetch
• Code blocks with titles and line numbers
• Code annotations for non-obvious logic

Quality Checklist:

• NO REPETITION audit across published articles
• Pre-publication link audit (4-step process)
• Visual elements present (diagrams where helpful)
• Serves multiple learning styles
• Cross-links to cs.bradpenney.io for CS fundamentals

See CLAUDE.md for comprehensive content guidelines (~50 quality standards).

Deployment

The site deploys automatically via GitHub Actions on push to main:

1. GitHub Actions builds the site using mkdocs gh-deploy
2. Static files are pushed to the gh-pages branch
3. GitHub Pages serves the content
4. Custom domain: python.bradpenney.io (via CNAME in docs/)

Tech Stack

• Framework: Material for MkDocs
• Theme: Material (slate color scheme, black/amber palette)
• Features: Code annotations, tabs, Mermaid diagrams, copy buttons
• Plugins: Search, htmlproofer (link validation)
• Deployment: GitHub Pages with custom domain

Contributing

This is a personal learning repository. Content improvements and corrections are welcome via issues or pull requests.

License

Content is available for personal and educational use. See individual files for specific licensing.

Related Sites

This site is part of an integrated learning ecosystem:

• cs.bradpenney.io - Computer Science fundamentals (algorithms, data structures, theory)
• linux.bradpenney.io - Linux for SREs (where you'll run your Python scripts)
• k8s.bradpenney.io - Kubernetes (Python automates K8s operations)
• tools.bradpenney.io - Development tools (Git, editors for writing Python)
• networking.bradpenney.io - Networking (Python scripts for network automation)
• storage.bradpenney.io - Storage (Python for backup scripts, data management)
• bradpenney.io - Main hub

How they connect:

• Linux site + Python site = Complete automation capability
• Kubernetes site + Python site = K8s automation and tooling
• Tools site + Python site = Efficient Python development
• CS site provides theory behind algorithms you'll implement in Python

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