Table of Contents
Basic usage
Practical Tips
1. Adding a default value
Home Backend Development Python Tutorial Practical tips for parsing Python command line parameters

Practical tips for parsing Python command line parameters

Feb 03, 2024 am 08:30 AM
Command line parameter parsing command line parser

Practical tips for parsing Python command line parameters

Practical tips for parsing Python command line parameters

When writing scripts in Python, you often need to obtain parameters from the command line. Python's built-in argparse module provides a simple and powerful tool for command line argument parsing. This article will introduce the basic usage of argparse and provide some practical tips and code examples.

Basic usage

First, you need to import the argparse module:

import argparse
Copy after login

Then, you can create an argparse.ArgumentParser object to Parsing command line parameters:

parser = argparse.ArgumentParser(description='命令行参数解析示例')
Copy after login

description parameter is used to provide a brief description for display in the help message.

Next, you can add different command line arguments to the ArgumentParser object. For example, adding a positional parameter:

parser.add_argument('input_file', help='输入文件路径')
Copy after login

This creates a positional parameter named input_file that specifies the path to the input file.

To provide more flexibility, optional parameters can be added. For example, add a --output parameter to specify the path of the output file:

parser.add_argument('--output', help='输出文件路径')
Copy after login

Use the long parameter form --output, you can also use the short The word form, such as -o. To add the short form of an argument, you can add -o to the argument's dest argument:

parser.add_argument('-o', '--output', help='输出文件路径')
Copy after login

Then, you can use parse_args()Method to parse command line parameters:

args = parser.parse_args()
Copy after login

The parsing results will be saved in the args object. These values ​​can be accessed through the object's properties:

print(args.input_file)
print(args.output)
Copy after login

For positional parameters, you can use the nargs parameter to specify that multiple values ​​are accepted. For example, to accept multiple input file paths, you can use nargs=' ':

parser.add_argument('input_files', nargs='+', help='输入文件路径')
Copy after login

Practical Tips

1. Adding a default value

works Provide default values ​​for parameters. For example, to set the default value of the --output parameter to output.txt:

parser.add_argument('--output', default='output.txt', help='输出文件路径')
Copy after login

If --output## is not specified on the command line #parameter, the default value will be used.

2. Add restrictions

You can add restrictions to parameters. For example, you can use the

choices parameter to specify that a parameter can only accept specific values:

parser.add_argument('--mode', choices=['A', 'B', 'C'], help='运行模式')
Copy after login

Only when the value of the

--mode parameter is A, B or C will be accepted.

3. Add mutually exclusive parameters

Sometimes, you need to ensure that certain parameters are mutually exclusive. A mutually exclusive parameter group can be created using the

add_mutually_exclusive_group() method. For example, to ensure that only one of the --input and --output parameters can be selected, you can do this:

group = parser.add_mutually_exclusive_group()
group.add_argument('--input', help='输入文件路径')
group.add_argument('--output', help='输出文件路径')
Copy after login

4. Add subcommand

Sometimes, you may want to add multiple subcommands to the script. This can be achieved using

subparsers. For example, assuming you want your script to have a run subcommand and a test subcommand, you can do this:

subparsers = parser.add_subparsers(dest='command')

run_parser = subparsers.add_parser('run', help='运行程序')
run_parser.add_argument('--input', help='输入文件路径')

test_parser = subparsers.add_parser('test', help='测试程序')
test_parser.add_argument('--input', help='输入文件路径')
Copy after login

Then, after parsing the command line parameters, you can The value of

args.command determines which subcommand to use.

Sample code

The following is a sample code that demonstrates the techniques and usage mentioned above:

import argparse

def main(args):
    print('输入文件:', args.input_file)
    print('输出文件:', args.output)

    if args.input_files:
        print('输入文件列表:', args.input_files)

    if args.mode:
        print('运行模式:', args.mode)

    if args.command == 'run':
        print('运行程序')
    elif args.command == 'test':
        print('测试程序')

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='命令行参数解析示例')

    parser.add_argument('input_file', help='输入文件路径')
    parser.add_argument('--output', default='output.txt', help='输出文件路径')
    parser.add_argument('-o', '--output', help='输出文件路径')
    parser.add_argument('input_files', nargs='+', help='输入文件路径')
    parser.add_argument('--mode', choices=['A', 'B', 'C'], help='运行模式')

    subparsers = parser.add_subparsers(dest='command')

    run_parser = subparsers.add_parser('run', help='运行程序')
    run_parser.add_argument('--input', help='输入文件路径')

    test_parser = subparsers.add_parser('test', help='测试程序')
    test_parser.add_argument('--input', help='输入文件路径')

    args = parser.parse_args()
    main(args)
Copy after login
The above is an introduction to practical techniques for Python command line parameter parsing and Sample code.

argparse Provides a flexible and powerful way to parse command line arguments and can be customized according to the needs of the application. Using argparse, you can easily handle various complex command line parameters and improve the scalability and ease of use of your scripts.

The above is the detailed content of Practical tips for parsing Python command line parameters. 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

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Repo: How To Revive Teammates
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

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)

How to Use Python to Find the Zipf Distribution of a Text File How to Use Python to Find the Zipf Distribution of a Text File Mar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

How Do I Use Beautiful Soup to Parse HTML? How Do I Use Beautiful Soup to Parse HTML? Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Image Filtering in Python Image Filtering in Python Mar 03, 2025 am 09:44 AM

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

How to Perform Deep Learning with TensorFlow or PyTorch? How to Perform Deep Learning with TensorFlow or PyTorch? Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Introduction to Parallel and Concurrent Programming in Python Introduction to Parallel and Concurrent Programming in Python Mar 03, 2025 am 10:32 AM

Python, a favorite for data science and processing, offers a rich ecosystem for high-performance computing. However, parallel programming in Python presents unique challenges. This tutorial explores these challenges, focusing on the Global Interprete

How to Implement Your Own Data Structure in Python How to Implement Your Own Data Structure in Python Mar 03, 2025 am 09:28 AM

This tutorial demonstrates creating a custom pipeline data structure in Python 3, leveraging classes and operator overloading for enhanced functionality. The pipeline's flexibility lies in its ability to apply a series of functions to a data set, ge

Serialization and Deserialization of Python Objects: Part 1 Serialization and Deserialization of Python Objects: Part 1 Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Mathematical Modules in Python: Statistics Mathematical Modules in Python: Statistics Mar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

See all articles