search
HomeBackend DevelopmentPython TutorialTips for using Python for script debugging on Linux platform

Tips for using Python for script debugging on Linux platform

Tips of using Python for script debugging on the Linux platform

Using Python for script debugging on the Linux platform is one of the common tasks in the development process. Script debugging can help us quickly locate and fix errors in the code and improve development efficiency. This article will introduce some techniques for script debugging using Python on the Linux platform and provide specific code examples.

  1. Use the pdb module for interactive debugging
    Python provides the pdb module, which can insert breakpoints in the code and enter interactive debugging mode during running. The following is a simple example:
import pdb

def divide(x, y):
    result = x / y
    return result

pdb.set_trace() # 插入断点

print(divide(10, 0))

After inserting the pdb.set_trace() statement in the code, the running code will pause at this location and enter interactive debugging mode. We can use the commands provided by the pdb module for debugging, such as step to execute code step by step, print to print variable values, etc. This way you can quickly locate the problem.

  1. Use the logging module for log debugging
    The logging module is Python's built-in logging tool, which can easily insert log statements into the code to help us track the execution process of the code. Here is an example:
import logging

logging.basicConfig(level=logging.DEBUG) # 设置日志级别为DEBUG

def divide(x, y):
    logging.debug("start divide function")
    try:
        result = x / y
    except ZeroDivisionError:
        logging.error("division by zero")
        return None
    return result

print(divide(10, 0))

By inserting logging.debug() and logging.error() statements in the code, we can run the process record relevant information. Use the basicConfig() function to configure the log, including setting the log level, log output location, etc. Log levels include DEBUG, INFO, WARNING, ERROR and CRITICAL. We can set different levels as needed. level.

  1. Use assertions for code checking
    Assertion is a statement in Python that is used to check the code. If the condition of the assertion is not met, the program will interrupt and throw an AssertionError exception. Here is an example:
def divide(x, y):
    assert y != 0, "division by zero"
    result = x / y
    return result

print(divide(10, 0))

In the above example, we use the assert statement to check if y is 0 and throw an exception if it is 0, and output an error message. By using assertions, we can pre-check various conditions in the code and reduce the occurrence of errors.

In addition to the above tips, there are some other debugging tools that can help us debug Python scripts on the Linux platform, such as using IDE integrated debuggers, using third-party tools such as pdb, etc. Choosing a debugging method that suits you can improve development efficiency and reduce debugging time.

To summarize, using Python for script debugging on the Linux platform requires mastering the use of the pdb module, the configuration of the logging module, and the use of assertions. By using these techniques appropriately, we can locate and fix errors in the code more quickly and improve development efficiency.

(564 words in total)

The above is the detailed content of Tips for using Python for script debugging on Linux platform. For more information, please follow other related articles on the PHP Chinese website!

Statement
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
Python vs. C  : Understanding the Key DifferencesPython vs. C : Understanding the Key DifferencesApr 21, 2025 am 12:18 AM

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Python vs. C  : Which Language to Choose for Your Project?Python vs. C : Which Language to Choose for Your Project?Apr 21, 2025 am 12:17 AM

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

Reaching Your Python Goals: The Power of 2 Hours DailyReaching Your Python Goals: The Power of 2 Hours DailyApr 20, 2025 am 12:21 AM

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Maximizing 2 Hours: Effective Python Learning StrategiesMaximizing 2 Hours: Effective Python Learning StrategiesApr 20, 2025 am 12:20 AM

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Choosing Between Python and C  : The Right Language for YouChoosing Between Python and C : The Right Language for YouApr 20, 2025 am 12:20 AM

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python vs. C  : A Comparative Analysis of Programming LanguagesPython vs. C : A Comparative Analysis of Programming LanguagesApr 20, 2025 am 12:14 AM

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

2 Hours a Day: The Potential of Python Learning2 Hours a Day: The Potential of Python LearningApr 20, 2025 am 12:14 AM

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

See all articles

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

MantisBT

MantisBT

Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

PhpStorm Mac version

PhpStorm Mac version

The latest (2018.2.1) professional PHP integrated development tool

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

mPDF

mPDF

mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

ZendStudio 13.5.1 Mac

ZendStudio 13.5.1 Mac

Powerful PHP integrated development environment