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YAML

YAML in Python

YAML

  • YAML = YAML Ain’t Markup Language
  • Human-readable data-serialization language
  • It is commonly used for configuration files, but it is also used in data storage (e.g. debugging output) or transmission (e.g. document headers).
  • Filename extension: .yaml

Use YAML in Python

PyYAML is a YAML parser and emitter for Python.

$ pip install pyyaml

Read YAML

Let’s say we have a YAML file called person_info.yaml:

name: "Ecko"
age: 20
hobby:
    - "Basketball"
    - "gym"
    - "coding":
        language:
            - Python
            - Java
        editor:
            - vscode
            - JupyterLab
university: "KIT"
student: True

We use yaml.safe_load() to read a YAML file:

with open("test.yaml", "r") as f:
    person_info = yaml.safe_load(f)

person_info

{
    'age': 20,
    'hobby': [
    	'Basketball',
    	'gym',
    	{'coding': 
    		{
    			'editor': ['vscode', 'JupyterLab'],
    			'language': ['Python', 'Java']
    		}
    	}
    ],
    'name': 'Ecko',
    'student': True,
    'university': 'KIT'
}

The YAML file will be read to a dict, and we can access keys and values. For example,

print(person_info["name"])

Ecko

Read multiple YAMLs

Multiple YAML documents are read with yaml.safe_load_all().

Example: persons.yaml

---
name: "Ecko"
age: 20
hobby:
    - "Basketball"
    - "gym"
    - "coding":
        language:
            - Python
            - Java
        editor:
            - vscode
            - JupyterLab
university: "KIT"
student: True
---
name: "Tan"
age: 21
hobby:
    - football
    - reading
university: "TUM"
student: True

We read this YAML file and print name and age of each person:

with open("persons.yaml") as stream:
    persons = yaml.safe_load_all(stream)

    for person in persons:
        name = person["name"]
        age = person["age"]
        print(f"Name: {name}, Age: {age}")

Name: Ecko, Age: 20
Name: Tan, Age: 21

Write YAML

To serialize a Python object into a YAML stream, use yaml.safe_dump().

In order to format the output YAML, you can use keyword arguments of safe_dump(). Some useful arguments are:

  • default_flow_style : indicates if a collection is block or flow. The possible values are None, True, False.
  • indent: sets the preferred indentation
  • explicit_start: if True, adds an explicit start using ---
  • explicit_end: if True, adds an explicit end using ---

For example

person_info = {
    "name": "Ecko",
    "age": 20,
    "hobby": [
        "Basketball", 
        {
            "coding": {
                "language": ["Python", "Java", "C#"],
                "start_from": 2014
            }
        }
    ]
}

with open("person.yaml", "w") as f:
    yaml.safe_dump(person_info, f, default_flow_style=False, indent=2)

person.yaml will look like following:

age: 20
hobby:
- Basketball
- coding:
    language:
    - Python
    - Java
    - C#
    start_from: 2014
name: Ecko

Write multiple YAML files

Use yaml.safe_dump_all()

Reference