Practical tips for Python scripting in Linux
Practical tips for Python scripting in Linux, specific code examples are required
Introduction:
Python is a programming language that can be widely used in various fields , and Linux, as a free and open source operating system, is widely used in servers, embedded devices and other fields. In the Linux environment, Python scripts can exert powerful power to help us complete various tasks. This article will introduce some practical tips for using Python scripts in Linux and provide specific code examples.
1. Combining Shell Commands with Python
In Linux, we often need to use Shell commands to perform some system-level operations. Python provides the os
module and the subprocess
module, which can easily call Shell commands. Here are some common examples:
1. Execute a Shell command and get the output:
import subprocess result = subprocess.check_output("ls -l", shell=True) print(result.decode())
2. Execute multiple Shell commands:
import subprocess commands = [ "sudo apt update", "sudo apt upgrade -y", "sudo apt install python3-pip -y", ] for cmd in commands: subprocess.call(cmd, shell=True)
3. Restart via Shell command Directed output:
import subprocess with open("output.txt", "w") as f: subprocess.call("ls -l", shell=True, stdout=f)
2. File and directory operations
File and directory operations in Linux systems are frequently encountered tasks. Python provides the os.path
module and ## The #shutil module is used to process files and directories.
import os os.makedirs("my_directory")
import shutil shutil.rmtree("my_directory")
import os for root, dirs, files in os.walk("my_directory"): for file in files: print(os.path.join(root, file))
Network operations in the Linux environment are very common. Python provides the
socket module and the
requests module to handle network requests.
import requests response = requests.get("https://www.example.com") print(response.text)
import http.server handler = http.server.SimpleHTTPRequestHandler httpd = http.server.HTTPServer(("", 8000), handler) httpd.serve_forever()
import smtplib from email.message import EmailMessage msg = EmailMessage() msg.set_content("Hello, World!") msg["Subject"] = "This is a test email" msg["From"] = "sender@example.com" msg["To"] = "recipient@example.com" with smtplib.SMTP("smtp.example.com") as server: server.send_message(msg)
This article introduces some practical skills for using Python scripts in Linux, including combination with Shell commands, file and directory operations, and network operations. Through these tips, we can better utilize the power of Python to complete various tasks. The above sample code is only a demonstration and readers can modify and expand it according to their actual needs.
The above is the detailed content of Practical tips for Python scripting in Linux. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

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

Python provides a variety of ways to download files from the Internet, which can be downloaded over HTTP using the urllib package or the requests library. This tutorial will explain how to use these libraries to download files from URLs from Python. requests library requests is one of the most popular libraries in Python. It allows sending HTTP/1.1 requests without manually adding query strings to URLs or form encoding of POST data. The requests library can perform many functions, including: Add form data Add multi-part file Access Python response data Make a request head

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

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

PDF files are popular for their cross-platform compatibility, with content and layout consistent across operating systems, reading devices and software. However, unlike Python processing plain text files, PDF files are binary files with more complex structures and contain elements such as fonts, colors, and images. Fortunately, it is not difficult to process PDF files with Python's external modules. This article will use the PyPDF2 module to demonstrate how to open a PDF file, print a page, and extract text. For the creation and editing of PDF files, please refer to another tutorial from me. Preparation The core lies in using external module PyPDF2. First, install it using pip: pip is P

This tutorial demonstrates how to leverage Redis caching to boost the performance of Python applications, specifically within a Django framework. We'll cover Redis installation, Django configuration, and performance comparisons to highlight the bene

Natural language processing (NLP) is the automatic or semi-automatic processing of human language. NLP is closely related to linguistics and has links to research in cognitive science, psychology, physiology, and mathematics. In the computer science

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
