Home Backend Development Python Tutorial Python server programming: YAML format parsing using PyYAML

Python server programming: YAML format parsing using PyYAML

Jun 19, 2023 am 10:33 AM
python Server programming 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
Copy after login

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)
Copy after login

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))
Copy after login

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
Copy after login

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)
Copy after login

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))
Copy after login

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)
Copy after login

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))
Copy after login

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.

The above is the detailed content of Python server programming: YAML format parsing using PyYAML. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Is the vscode extension malicious? Is the vscode extension malicious? Apr 15, 2025 pm 07:57 PM

VS Code extensions pose malicious risks, such as hiding malicious code, exploiting vulnerabilities, and masturbating as legitimate extensions. Methods to identify malicious extensions include: checking publishers, reading comments, checking code, and installing with caution. Security measures also include: security awareness, good habits, regular updates and antivirus software.

How to run programs in terminal vscode How to run programs in terminal vscode Apr 15, 2025 pm 06:42 PM

In VS Code, you can run the program in the terminal through the following steps: Prepare the code and open the integrated terminal to ensure that the code directory is consistent with the terminal working directory. Select the run command according to the programming language (such as Python's python your_file_name.py) to check whether it runs successfully and resolve errors. Use the debugger to improve debugging efficiency.

Can vs code run in Windows 8 Can vs code run in Windows 8 Apr 15, 2025 pm 07:24 PM

VS Code can run on Windows 8, but the experience may not be great. First make sure the system has been updated to the latest patch, then download the VS Code installation package that matches the system architecture and install it as prompted. After installation, be aware that some extensions may be incompatible with Windows 8 and need to look for alternative extensions or use newer Windows systems in a virtual machine. Install the necessary extensions to check whether they work properly. Although VS Code is feasible on Windows 8, it is recommended to upgrade to a newer Windows system for a better development experience and security.

Can visual studio code be used in python Can visual studio code be used in python Apr 15, 2025 pm 08:18 PM

VS Code can be used to write Python and provides many features that make it an ideal tool for developing Python applications. It allows users to: install Python extensions to get functions such as code completion, syntax highlighting, and debugging. Use the debugger to track code step by step, find and fix errors. Integrate Git for version control. Use code formatting tools to maintain code consistency. Use the Linting tool to spot potential problems ahead of time.

Choosing Between PHP and Python: A Guide Choosing Between PHP and Python: A Guide Apr 18, 2025 am 12:24 AM

PHP is suitable for web development and rapid prototyping, and Python is suitable for data science and machine learning. 1.PHP is used for dynamic web development, with simple syntax and suitable for rapid development. 2. Python has concise syntax, is suitable for multiple fields, and has a strong library ecosystem.

Can vscode be used for mac Can vscode be used for mac Apr 15, 2025 pm 07:36 PM

VS Code is available on Mac. It has powerful extensions, Git integration, terminal and debugger, and also offers a wealth of setup options. However, for particularly large projects or highly professional development, VS Code may have performance or functional limitations.

Golang vs. Python: Concurrency and Multithreading Golang vs. Python: Concurrency and Multithreading Apr 17, 2025 am 12:20 AM

Golang is more suitable for high concurrency tasks, while Python has more advantages in flexibility. 1.Golang efficiently handles concurrency through goroutine and channel. 2. Python relies on threading and asyncio, which is affected by GIL, but provides multiple concurrency methods. The choice should be based on specific needs.

PHP and Python: Different Paradigms Explained PHP and Python: Different Paradigms Explained Apr 18, 2025 am 12:26 AM

PHP is mainly procedural programming, but also supports object-oriented programming (OOP); Python supports a variety of paradigms, including OOP, functional and procedural programming. PHP is suitable for web development, and Python is suitable for a variety of applications such as data analysis and machine learning.

See all articles