Over-optimized loops hurt the eyes
TL;DR: Don't optimize loops without a clear need and concrete real-world evidence
Problems
- Premature Optimization
- Reduced readability
- Increased complexity
- Difficult to maintain
- Slower debugging
Solutions
- Keep it simple
- Prioritize clarity
- Avoid premature tweaks
- Refactor when needed
Context
You might think optimizing every loop will improve performance, but this approach backfires when you sacrifice clarity for unproven gains.
Writing complex code to avoid hypothetical slowdowns often makes it hard for others (and your future self) to understand or debug your code.
It would be best if you prioritized readability.
Keep loops simple and only optimize when you know a bottleneck exists in real usage scenarios.
Sample Code
Wrong
# Over-optimized and less readable
result = [item.process() for item in items if item.is_valid()]
Right
# Clearer and easier to understand
result = []
for item in items:
if item.is_valid():
result.append(item.process())
Detection
[X] Semi-Automatic
Look for list comprehensions or complex loop structures that optimize performance without real performance benchmark evidence.
Exceptions
- Concrete evidence on mission-critical algorithms
Tags
- Premature Optimization
Level
[X] Intermediate
AI Generation
AI tools often prioritize functional correctness so that they might produce clean, simple loops.
if you prompt AI for performance at all costs, it could create over-optimized code even for straightforward tasks.
AI Detection
With proper instructions to stress readability and maintainability, AI can detect and fix this smell by simplifying loops and choosing clarity over premature optimization.
Try Them!
Remember: AI Assistants make lots of mistakes
| Without Proper Instructions | With Specific Instructions |
|---|---|
| ChatGPT | ChatGPT |
| Claude | Claude |
| Perplexity | Perplexity |
| Copilot | Copilot |
| Gemini | Gemini |
Conclusion
Don’t sacrifice readability by optimizing too early.
You can optimize later if a loop becomes a proven bottleneck.
Until then, clear and simple code will save time, reduce bugs, and make it more maintainable.
Relations
Code Smell 20 - Premature Optimization
Maxi Contieri ・ Nov 8 '20
Disclaimer
Code Smells are my opinion.
Credits
Photo by Tine Ivanič on Unsplash
More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason.
W. A. Wulf
This article is part of the CodeSmell Series.
Top comments (7)
Many (but not all) Python programmers will argue that the list comprehension is actually clearer and easier to read because it is more "Pythonic". So, for them it is often more of a stylistic choice to improve readability than it is for optimization.
writing pythonic code is also a code smell.
Languages are accidental and trends come and code and python will not be an exception.
You should write code for humans, not just por python readers
I actually like the idea to bring this into question. Do we have to dogmatically follow a concept, because someone defined it as the way-to-go?
The point of my comment wasn't actually to indicate one way or the other is the way to go. This is why I began with "many (but not all)". When I use Python, I often veer from some common "Pythonic" stylistic choices.
My point was that the OP's premise that the list comprehension is a premature loop optimization is not really the case at all. Although it is usually faster than the OP's traditionally styled loop, that is not the reason that most Python programmers use them. They are mostly used for style, and not for optimization.
Which style is better and which is more readable is not so clear cut. It depends on who is reading/writing the code, and their background. Someone with in-depth knowledge and experience in a language like C or C++ or Java, etc, who is brand new to Python might find the traditional loop version much easier to read as it is far more familiar to them. Or they might see the relationship between list comprehensions and discrete math's set builder notation as a better way to express intention (e.g., clearer to see we are specifying the content of a list). While someone who has primarily programmed in Python from the start, whose first introduction to the keyword
formay have been in the context of a list comprehension, and who is working on a team with similar background, will likely find the list comprehension the easier to read style.Fully agree here, I started with Java in 2007 and then went over to php and then finally to JS, all highly influenced by c-style syntax. It was hard to get into the python logic when reading things but I can clearly see the benefits of it, especially when you "think in data".
If you can adapt to the pythonic way then this is good and you can become more productive but if it's a constant barrier then bring it into question and look for a different code style that is still valid code.
let the machines do the optimization for us.
Write code for humans!
Amazing