Python server programming: YAML format parsing using PyYAML

Python server programming: Using PyYAML for YAML format parsing

With the rapid development of Internet technology, server programming has become more and more important. As a powerful programming language, Python is becoming more and more popular among developers. PyYAML is one of the most commonly used YAML format parsers in Python. This article will introduce how to use PyYAML to parse YAML format to help developers better program Python servers.

What is YAML?

YAML (Yet Another Markup Language) is a lightweight data exchange format. Compared with data formats such as XML and JSON, YAML is a format that is easier to read and write. Data in YAML format can be serialized and read and understood by humans. YAML was originally developed to solve the problem of XML being cumbersome and difficult to read.

YAML format example:

- name: Alice
  age: 25
  occupation: programmer
- name: Bob
  age: 30
  occupation: designer

Using PyYAML to parse YAML format

PyYAML is one of the most commonly used YAML format parsers in Python. It is a full-featured YAML parser that supports all core features of YAML 1.1 and 1.2. It is very simple to use PyYAML to parse the YAML format. You only need to convert the YAML format data into a Python object through the yaml.load() method.

import yaml

with open("data.yaml", 'r') as stream:
    data = yaml.load(stream)

print(data)

The above code reads and converts the YAML format data in the data.yaml file into a Python object, and finally prints the output.

In PyYAML, you can also use the yaml.dump() method to convert Python objects into YAML format data.

import yaml

data = [
    {'name': 'Alice', 'age': 25, 'occupation': 'programmer'},
    {'name': 'Bob', 'age': 30, 'occupation': 'designer'}
]

print(yaml.dump(data))

The above code converts the Python list into YAML format data and prints the output.

Advanced features of PyYAML

In addition to basic YAML format parsing and serialization, PyYAML also provides many advanced features, including type conversion, custom tags, validation, and extensions. Next, we'll look at some of these features in more detail.

Type conversion

PyYAML supports automatic conversion of data in YAML format to Python built-in types, including strings, integers, floating point numbers, dictionaries and lists, etc. For example, read the following YAML format data as a Python object:

date: 2021-06-25
count: 300
price: 99.99

During the reading process, PyYAML will automatically convert the date field to Python's datetime.dateObject, the count field is converted to Python's integer type, and the price field is converted to Python's floating point type.

Custom tags

PyYAML supports custom tags, which allows you to convert custom Python objects into YAML format data and convert them back when reading YAML data original object. For example, define the following custom class:

import datetime

class CustomDate:
    def __init__(self, year, month, day):
        self.date = datetime.date(year, month, day)

Then, we can use the following code to convert the custom class into YAML format:

import yaml

def custom_date_representer(dumper, data):
    return dumper.represent_scalar('!CustomDate', '{}/{}/{}'.format(data.date.year, data.date.month, data.date.day))

def custom_date_constructor(loader, node):
    value = loader.construct_scalar(node)
    year, month, day = map(int, value.split('/'))
    return CustomDate(year, month, day)

data = [
    CustomDate(2021, 6, 25),
    CustomDate(2021, 6, 26)
]

yaml.add_representer(CustomDate, custom_date_representer)
yaml.add_constructor('!CustomDate', custom_date_constructor)

print(yaml.dump(data))

In the above code, we register the custom tag!CustomDate, and defines the corresponding representer and constructor methods to convert the custom class into YAML format and restore it to the original object.

Validation and Extension

PyYAML also provides validation and extension functions, including verifying the correctness of YAML format data and registering new tags. For example, you can use the following code to verify the correctness of YAML format data:

import yaml

with open("data.yaml", 'r') as stream:
    try:
        data = yaml.safe_load(stream)
    except yaml.YAMLError as exc:
        print(exc)

The above code uses the yaml.safe_load() method to load YAML format data and output the corresponding data based on the correctness of the data. information.

At the same time, you can also use the following code to register a new tag:

import yaml

class CustomType:
    pass

def represent_custom_type(dumper, data):
    return dumper.represent_scalar('!CustomType', None)

yaml.add_representer(CustomType, represent_custom_type)

data = CustomType()

print(yaml.dump(data))

In the above code, we register the custom class CustomType as a new tag !CustomType, and defines the corresponding representer method to convert it into YAML format data.

Summary

This article introduces how to use PyYAML to parse and serialize YAML format, and introduces some advanced functions of PyYAML, including type conversion, custom tags, validation and extension, etc. Through the introduction of this article, I believe readers can have a deeper understanding of the use of PyYAML and get better applications in Python server programming.

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