SoFunction
Updated on 2025-03-05

Sharing of error handling and debugging skills in Python

1. Introduction

In software development, errors are inevitable. Whether in the early stage of development or late stage of the project, the program may encounter various errors. As a concise and powerful programming language, Python provides rich error handling mechanisms and debugging tools to help developers discover and solve problems. This article will explore the error handling mechanism, common error types and their handling methods in Python, and introduce some practical debugging techniques to improve development efficiency and code quality.

2. Error handling mechanism in Python

In Python, errors (Exception) are mainly divided into two categories: syntax error (SyntaxError) and exception (Exception).

2.1 SyntaxError (SyntaxError)

Syntax errors occur when programming, usually because the code does not comply with Python's syntax rules. For example, missing colons, mismatch of brackets, etc. Syntax errors will be found by the Python interpreter before the code is executed and the corresponding error message will be thrown.

# Example: Missing colon causes syntax errorif x > 5
    print("x is greater than 5")

2.2 Exception

Exceptions are errors that occur during the program's running process. They are usually caused by program logic errors, unavailability of resources, etc. Python provides an exception handling mechanism, allowing developers to take appropriate responses when exceptions occur. Common exception types include:

  • ValueError: Invalid value
  • TypeError: The wrong type
  • IndexError: Index out of range
  • KeyError: The specified key does not exist in the dictionary
  • FileNotFoundError: file not found

Python usagetry...exceptThe statement is handled for exceptions, and the developer canexceptCatch and handle exceptions in blocks to avoid program crashes.

try:
    x = int(input("Please enter a number: "))
except ValueError:
    print("Input is invalid, please enter a valid number.")

2.3 Exception capture and processing

In Python, usetry...exceptBlocks to catch and handle exceptions. If an exception is thrown in the code block, Python will jump to the correspondingexceptblock, process.

try:
    # Code that may throw exceptions    result = 10 / 0
except ZeroDivisionError:
    #Exception handling    print("Can't be divided by zero!")

Can be usedelseandfinallyStatement blocks to improve the error handling mechanism:

  • else: iftryThe block did not throw an exception, executeelsepiece.
  • finally: Whether an exception occurs or not,finallyThe block code will be executed, usually used for resource release and other operations.
try:
    x = int(input("Please enter a number: "))
    result = 10 / x
except ValueError:
    print("Input is invalid!")
except ZeroDivisionError:
    print("Can't be divided by zero!")
else:
    print(f"The calculation result is: {result}")
finally:
    print("The program ends!")

3. Common Errors and Exceptions

Understanding common Python error types can help better error handling and debugging. Here are some common errors and exception types:

NameError: A reference is made to an undefined variable.

print(undeclared_variable)

TypeError: The data type does not match, for example, calling a string method on an integer.

number = 10
()  # mistake：Integers are not upper method

IndexError: An invalid index was used when accessing the list.

lst = [1, 2, 3]
print(lst[5])  # mistake：Index out of range

FileNotFoundError: When opening the file, the file does not exist.

with open('nonexistent_file.txt', 'r') as f:
    content = ()

4. Debug skills in Python

Debugging is an indispensable part of software development. Python provides some powerful debugging tools and techniques to help developers locate and fix problems.

4.1 Using the print() statement

The most common debugging method is to insert a print() statement and output the value of a variable at a critical location to help developers check the program status. This method is simple and direct, suitable for quick debugging.

x = 10
y = 0
print(f"x = {x}, y = {y}")
result = x / y  # Check the value of the variable during debugging

4.2 Using the logging module

Compared with print(), the logging module provides more powerful logging functions. Developers can set different log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL) and output the logs to a file or console. Logging is suitable for production environments and can provide more detailed information and help troubleshoot problems.

import logging
 
# Set up the logger(level=)
("Debug information")
("The program runs normally")
("Warning message")
("error message")
("Serious Error")

4.3 Using Python's built-in debugger pdb

Python provides a built-in debuggerpdb, can pause execution when the program is running, and allows developers to check the program's status, step through the code, view variable values, etc. usepdb.set_trace()The debugger can be started at a specified location.

import pdb
 
def divide(x, y):
    pdb.set_trace()  # Debug Point    return x / y
 
result = divide(10, 2)

After starting the debugger, the program will be paused and the developer will be allowed to enter commands, such as viewing variables, performing single-step operations, etc.

Commonly usedpdbThe debugging commands include:

  • n: Execute the next line of code
  • s: Enter the function inside
  • c: Continue to execute the program until the next breakpoint
  • q: Exit the debugger

4.4 Using the IDE debugging tool

Modern integrated development environments (IDEs) such as PyCharm and Visual Studio Code provide graphical debugging tools, allowing developers to debug through breakpoints, variable monitoring, call stacks and other functions. Using the IDE's debugger, you can track the code execution process more intuitively.

5. Summary of debugging skills

  • Understand the error message: Read the error message thrown by Python carefully to understand the cause and location of the error.
  • Localization problem: By gradually annotating code blocks and simplifying the problem, narrowing the scope of the problem to the smallest reproducible part.
  • Usage unit test: Ensure the correctness of the code by writing unit tests and identify potential problems as early as possible.
  • With debugging tools:usepdbOr debugging tools provided by the IDE can more efficiently locate and fix problems.

6. Conclusion

Python provides powerful error handling mechanisms and debugging tools to help developers troubleshoot and solve problems more efficiently. By mastering the skills of try...except exception handling, logging logging, pdb debugger, etc., the code robustness and development efficiency can be greatly improved. In actual development, error handling and debugging are processes of continuous learning and improvement, and mastering these skills is an important skill for every Python developer.

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