YAML and PyYAML

YAML is the config format you meet everywhere in DevOps: Kubernetes manifests, Ansible playbooks, GitHub Actions and GitLab CI pipelines, Docker Compose files. It stores the same kind of structured data as JSON but is written to be easy for people to read and edit.

Python does not parse YAML out of the box. The standard library is PyYAML, imported as yaml, and it is already installed on this lab machine.

import yaml

Why YAML for config

YAML earns its place in config files for a few reasons:

• Indentation shows structure, so there are no braces or brackets to balance.
• It allows comments with #, which JSON does not. Config files live and die by good comments.
• Strings usually need no quotes, which keeps files clean.

A small YAML config looks like this:

# service configuration
service: api
port: 8080
enabled: true
tags:
  - web
  - public

The same shapes as JSON

Under the surface YAML describes the same data model as JSON. That config parses into a Python dict with the keys service, port, enabled, and tags, where tags is a list. Mappings become dicts, sequences become lists, and scalars become strings, numbers, or booleans, exactly like JSON.

Because the result is ordinary dicts and lists, everything you already know about working with them applies. The next node covers the safe way to parse YAML text into those objects.

What this node introduces

• import yaml loads PyYAML, the standard YAML library for Python.
• YAML is the config format behind Kubernetes, Ansible, and CI tools.
• YAML mappings become dicts and sequences become lists, the same data model as JSON.