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YAML

YAML (YAML Ain't Markup Language) is a human-readable data serialization format. It is widely used for configuration files, data exchange between applications, and as a language-independent alternative to XML or JSON. YAML's simple and readable syntax makes it popular among developers.

YAML Syntax​

YAML uses indentation and whitespace to define its structure. It supports various data types such as mappings (key-value pairs), sequences (arrays/lists), strings, integers, floats, booleans, and null values.

Example YAML Syntax​

key1: value1
key2: value2
nested_key:
  - item1
  - item2

Installing PyYAML​

PyYAML is a Python library for working with YAML files. To install it, use Python’s package manager, pip:

pip install pyyaml

Reading YAML Files in Python​

The yaml module in Python allows you to read YAML files. Use the yaml.safe_load() function to safely load YAML data from a file or a string.

Example: Reading YAML Files​

import yaml

# Reading YAML file
with open('data.yaml', 'r') as file:
    data = yaml.safe_load(file)

print(data)

This code reads the contents of data.yaml and parses it into a Python dictionary.

Writing YAML Files in Python​

To write data to a YAML file, use the yaml.dump() function. It serializes Python data into a YAML-formatted string.

Example: Writing YAML Files​

import yaml

# Data to write
data = {
    'key1': 'value1',
    'key2': [1, 2, 3],
    'key3': {'nested_key': 'nested_value'}
}

# Writing YAML file
with open('output.yaml', 'w') as file:
    yaml.dump(data, file)

This code writes the given dictionary to output.yaml in YAML format.

Advanced Usage​

Handling Custom Objects​

PyYAML supports serialization and deserialization of custom objects. You can define custom representer and constructor functions to serialize and deserialize custom objects, respectively.

Example: Handling Custom Objects​

class CustomObject:
    def __init__(self, value):
        self.value = value

# Define representer
import yaml

def custom_representer(dumper, data):
    return dumper.represent_scalar('!CustomObject', data.value)

def custom_constructor(loader, node):
    value = loader.construct_scalar(node)
    return CustomObject(value)

# Register representer and constructor
yaml.add_representer(CustomObject, custom_representer)
yaml.add_constructor('!CustomObject', custom_constructor)

# Serialize and Deserialize
obj = CustomObject('example_value')
serialized = yaml.dump(obj)
print(serialized)

deserialized = yaml.safe_load(serialized)
print(deserialized.value)

Handling Anchors and Aliases​

YAML allows anchors (&) and aliases (*) to reference the same data elsewhere in the document. PyYAML fully supports anchors and aliases.

Example: Using Anchors and Aliases​

base: &base
  key1: value1
  key2: value2

alias:
  <<: *base
  key3: value3

PyYAML can parse and dump YAML with anchors and aliases, preserving references.

Conclusion​

YAML is a versatile and human-readable format for data serialization. By leveraging PyYAML, you can easily read, write, and manipulate YAML files in Python. From basic key-value mappings to advanced features like custom objects and anchors, PyYAML provides a comprehensive toolkit for working with YAML data.