search
HomeBackend DevelopmentPython TutorialDirty Code: Simple Rules to Avoid It

Dirty Code: Simple Rules to Avoid It

Every developer has faced it: dirty code—the kind of code that looks like it’s been written in a hurry, sprinkled with magic numbers, duplicated blocks, and cryptic variable names. It works... barely. But maintaining it? That’s a nightmare.

If you've ever muttered under your breath while refactoring someone else's code (or even your own), this article is for you. Here are some simple rules to keep your code clean, readable, and future-proof.

What is Dirty Code?
Dirty code is code that:

  • Is hard to read or understand.
  • Lacks structure and consistency.
  • Is difficult to modify or debug without breaking something else.

This often happens when developers:

  1. Work under tight deadlines.
  2. Skip code reviews.
  3. Don’t follow best practices or standards.

Why is Dirty Code a Problem?

  • Hard to Debug: Fixing one bug can introduce five others.
  • Expensive to Maintain: Poorly written code takes longer to improve.
  • Team Confusion: New developers spend extra hours just trying to understand what’s happening. Dirty code may get the job done today, but it’s a ticking time bomb for your team and future self.

Simple Rules to Avoid Dirty Code

1. Follow the Single Responsibility Principle (SRP)
Each function, method, or class should do only one thing. If you find yourself writing methods with too many responsibilities, break them into smaller units.

❌ Bad Example:

✅ Good Example:

Each function now has one clear job, making the code easier to test and modify.

2. Avoid Magic Numbers and Strings
Hard-coded values (“magic numbers”) make code unreadable and hard to maintain. Use constants instead.

❌ Bad Example:

✅ Good Example:

The constant NOT_FOUND is self-explanatory, making your code easier to read.

3. Write Descriptive Variable and Function Names
Your variable names should reflect what they represent. Avoid abbreviations and cryptic names.

❌ Bad Example:

✅ Good Example:

The same applies to functions. Avoid vague names like doStuff() or process(). Be specific.

4. DRY (Don’t Repeat Yourself)
If you’re copying and pasting code, you’re doing it wrong. Duplicated code makes bug fixing a nightmare. Abstract repetitive logic into functions or classes.

❌ Bad Example:

✅ Good Example:

5. Keep Your Functions Short
If your function is longer than 20-30 lines, it’s doing too much. Break it down into smaller, reusable functions.

Long functions make it harder to understand and test specific behavior.

6. Use Comments Sparingly
Write code that explains itself. Use comments only when necessary to clarify complex logic. Avoid comments that state the obvious.

❌ Bad Example:

✅ Good Example:
If your code is clear, no comment is needed:

Use comments for things like clarifying why a certain decision was made, not what the code is doing.

7. Format and Organize Your Code

  • Follow a consistent coding style guide (e.g., PEP8 for Python, ESLint for JavaScript).
  • Use proper indentation.
  • Group related code together. Good formatting makes code clean and readable without any extra effort.

The Developer’s Mindset: Write Code for Humans
Code isn’t just written for machines; it’s written for humans too—your teammates, future maintainers, or even yourself six months down the line. When you write clean code:

  • You reduce mental load for others.
  • You make it easier to debug, extend, and improve.
  • You look like a professional developer who values quality.

Final Thoughts
Avoiding dirty code isn’t hard—it just takes discipline. Follow these simple rules:

  1. Stick to the Single Responsibility Principle.
  2. Avoid magic numbers.
  3. Use clear, descriptive names.
  4. DRY out repetitive code.
  5. Keep functions short.
  6. Use comments wisely.
  7. Format your code consistently.

Clean code isn’t about perfection; it’s about making your work maintainable and understandable. Your future self—and your team—will thank you.

Now go and refactor that messy code you’ve been ignoring! ?

The above is the detailed content of Dirty Code: Simple Rules to Avoid It. 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's Execution Model: Compiled, Interpreted, or Both?Python's Execution Model: Compiled, Interpreted, or Both?May 10, 2025 am 12:04 AM

Pythonisbothcompiledandinterpreted.WhenyourunaPythonscript,itisfirstcompiledintobytecode,whichisthenexecutedbythePythonVirtualMachine(PVM).Thishybridapproachallowsforplatform-independentcodebutcanbeslowerthannativemachinecodeexecution.

Is Python executed line by line?Is Python executed line by line?May 10, 2025 am 12:03 AM

Python is not strictly line-by-line execution, but is optimized and conditional execution based on the interpreter mechanism. The interpreter converts the code to bytecode, executed by the PVM, and may precompile constant expressions or optimize loops. Understanding these mechanisms helps optimize code and improve efficiency.

What are the alternatives to concatenate two lists in Python?What are the alternatives to concatenate two lists in Python?May 09, 2025 am 12:16 AM

There are many methods to connect two lists in Python: 1. Use operators, which are simple but inefficient in large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use the = operator, which is both efficient and readable; 4. Use itertools.chain function, which is memory efficient but requires additional import; 5. Use list parsing, which is elegant but may be too complex. The selection method should be based on the code context and requirements.

Python: Efficient Ways to Merge Two ListsPython: Efficient Ways to Merge Two ListsMay 09, 2025 am 12:15 AM

There are many ways to merge Python lists: 1. Use operators, which are simple but not memory efficient for large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use itertools.chain, which is suitable for large data sets; 4. Use * operator, merge small to medium-sized lists in one line of code; 5. Use numpy.concatenate, which is suitable for large data sets and scenarios with high performance requirements; 6. Use append method, which is suitable for small lists but is inefficient. When selecting a method, you need to consider the list size and application scenarios.

Compiled vs Interpreted Languages: pros and consCompiled vs Interpreted Languages: pros and consMay 09, 2025 am 12:06 AM

Compiledlanguagesofferspeedandsecurity,whileinterpretedlanguagesprovideeaseofuseandportability.1)CompiledlanguageslikeC arefasterandsecurebuthavelongerdevelopmentcyclesandplatformdependency.2)InterpretedlanguageslikePythonareeasiertouseandmoreportab

Python: For and While Loops, the most complete guidePython: For and While Loops, the most complete guideMay 09, 2025 am 12:05 AM

In Python, a for loop is used to traverse iterable objects, and a while loop is used to perform operations repeatedly when the condition is satisfied. 1) For loop example: traverse the list and print the elements. 2) While loop example: guess the number game until you guess it right. Mastering cycle principles and optimization techniques can improve code efficiency and reliability.

Python concatenate lists into a stringPython concatenate lists into a stringMay 09, 2025 am 12:02 AM

To concatenate a list into a string, using the join() method in Python is the best choice. 1) Use the join() method to concatenate the list elements into a string, such as ''.join(my_list). 2) For a list containing numbers, convert map(str, numbers) into a string before concatenating. 3) You can use generator expressions for complex formatting, such as ','.join(f'({fruit})'forfruitinfruits). 4) When processing mixed data types, use map(str, mixed_list) to ensure that all elements can be converted into strings. 5) For large lists, use ''.join(large_li

Python's Hybrid Approach: Compilation and Interpretation CombinedPython's Hybrid Approach: Compilation and Interpretation CombinedMay 08, 2025 am 12:16 AM

Pythonusesahybridapproach,combiningcompilationtobytecodeandinterpretation.1)Codeiscompiledtoplatform-independentbytecode.2)BytecodeisinterpretedbythePythonVirtualMachine,enhancingefficiencyandportability.

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

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

SecLists

SecLists

SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.

PhpStorm Mac version

PhpStorm Mac version

The latest (2018.2.1) professional PHP integrated development tool

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

SublimeText3 Linux new version

SublimeText3 Linux new version

SublimeText3 Linux latest version