Essential Guide for Python Beginners: 5 Common Debugging Mistakes to Avoid!

Introduction: As a Python developer, do you often find yourself overwhelmed by various error messages? This article will reveal the top 5 common pitfalls that beginners encounter, along with practical debugging techniques to help you quickly improve your code quality! At the end of the article, there’s also a Python learning package waiting for you!

1. Why Does Your Code Keep Throwing Errors? — Analysis of Common Error Types

1️⃣ Syntax Error (SyntaxError)

Typical Scenario: Missing colon, mismatched parentheses

# Error example
if x > 5
    print("x is greater than 5")  # Missing colon

# Correct syntax
if x > 5:
    print("x is greater than 5")

Debugging Tips:

• Use real-time syntax checking in your IDE (e.g., PyCharm’s red wavy line indicator)
• Check syntax in advance using python -m py_compile your_script.py

2️⃣ Name Error (NameError)

Fatal Pitfall: Variable/function not defined

def calculate_area(radius):
    return 3.14 * r ** 2  # Should be radius instead of r

# When calling
print(calculate_are(5))  # Function name typo

Solution:

• Use the variable tracking feature in your IDE
• Develop a habit of Ctrl+F to globally search for variable names

3️⃣ Type Error (TypeError)

Common Case: Data type mismatch

# Concatenating string with a number
name = "Xiao Ming"
age = 25
message = "My name is " + name + " and I am " + age  # This will throw an error!

Debugging Tool:

print(type(variable))  # Quickly confirm variable type
# Solution: Type conversion
message = f"My name is {name} and I am {age} years old"  # Use f-string for automatic type handling

2. Debugging Secrets: 5 Techniques to Quickly Locate Issues

🔍 Technique 1: Block Debugging Method

# Break complex code into independent test blocks
def process_data(data):
    # Test the data cleaning part separately
    cleaned = [x.strip() for x in data]  # Ensure this step outputs correctly
    # Then test the calculation logic
    total = sum(map(float, cleaned))
    return total

🛠️ Technique 2: Logging Method

import logging
logging.basicConfig(level=logging.DEBUG)

def divide(a,b):
    logging.debug(f"Input parameters: a={a}, b={b}")
    return a/b

📊 Technique 3: Advanced Exception Handling

try:
    result = 10 / 0
except ZeroDivisionError as e:
    print(f"Caught a division by zero error: {str(e)}")
except Exception as e:  # Catch all other exceptions
    print(f"Unknown error: {str(e)}")

3. Doubling Efficiency: Recommended Debugging Tools

Tool Name	Core Functionality	Applicable Scenarios
PyCharm	Intelligent breakpoint debugging/variable monitoring	Debugging complex projects
VSCode	Plugin extensions/real-time preview	Lightweight development
Jupyter	Interactive debugging/step execution	Data analysis scenarios
PySnooper	Automatically prints function execution flow	Quickly locate logical errors

4. Pitfall Guide: Code Standardization Recommendations

1. Naming Conventions: Use snake_case naming style (e.g., user_age)

2. Defensive Programming:

def get_user(id):
    user = db.query(id)
    if not user:
        logger.warning(f"User {id} does not exist!")
        return None
    return user