Table of Contents
The Zen of Python: "Do less, fool"
Home Backend Development Python Tutorial Python Sucks at For Loops – And That's Exactly Why We Love It

Python Sucks at For Loops – And That's Exactly Why We Love It

Jan 12, 2025 am 08:07 AM

Python Sucks at For Loops – And That’s Exactly Why We Love It

Python, the elegant cat in the programming language world: independent, sophisticated, and seemingly doesn’t need you until it really does. This quality is most vividly reflected in its for loop, which can make you feel like both a genius and an idiot in an instant.

It’s not that Python’s for loop is bad, it’s just that it’s too good at pretending to know better than you.

  1. The Zen of Python: "Do less, fool"

For loops in most programming languages ​​are intuitive. Want to count to 10? No problem, give you a bunch of boilerplate code to make you feel smart.

How to write in C language:

for (int i = 0; i < 10; i++) {
    printf("%d\n", i);
}
Copy after login
Copy after login

Simple, predictable, and respectful of your IQ.

And Python says:

"Why go to the trouble of defining i, specifying a range, or doing basic arithmetic? I'll lay it all out in front of you so you can experience what it's like to be a fake programmer."

Python version:

for i in range(10):
    print(i)
Copy after login
Copy after login

That’s it. No declarations, no braces, just "vibe". Python’s for loop is so simple that it feels a little wrong.


  1. Python is more than that: it wants you to do less

Suppose you have a list of fruits and want to print it out.

C language version (again, very respectful):

char* fruits[] = {"apple", "banana", "cherry"};
for (int i = 0; i < 3; i++) {
    printf("%s\n", fruits[i]);
}
Copy after login
Copy after login

Python version:

fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
    print(fruit)
Copy after login
Copy after login

Did you notice anything? Python doesn't even bother to give you index. It just throws the entire element at you like a Frisbee and says, "Here, take care of it."

Do you want to show your ingenuity by manually indexing a list? What a shame. Python already knows what you want and feeds it to you directly.


  1. List comprehension: the terminator of for loop

Python's list comprehension is the grave of for loops.

Want to create a new list where every number is doubled? In any other language this would take 3 to 4 lines of code. Python easily demonstrates its one-line coding skills:

doubled = [x * 2 for x in range(10)]
Copy after login
Copy after login

Not only is this efficient, it also makes you feel like you are writing code in some secret programming language that mere mortals will never understand. But the cost is: Your for loop now looks like a cryptic crossword puzzle.

Example:

results = [f"Employee-{i}" for i in range(10) if i % 2 == 0]
Copy after login

Congratulations! You just wrote a line of code and two weeks later you don’t even know what it means.


  1. “Let’s destroy stuff for fun”

Python’s for loop also likes to betray you in subtle ways. This is a classic mistake:

Unexpected variable override

for (int i = 0; i < 10; i++) {
    printf("%d\n", i);
}
Copy after login
Copy after login

Wait, what? Didn’t we replace everything with “pineapple”? No! Python is snickering in the corner because fruit is just a temporary variable. The actual list is not modified.

Meanwhile, JavaScript developers are chuckling to themselves because they know they can bring down entire production systems with variable scope issues.


  1. enumerate: The MVP we don’t deserve

Sometimes you need both an index and a value. Python could have made you use boring i like other languages. But it gives you enumerate(), which sounds more like a corporate term than a programming function.

for i in range(10):
    print(i)
Copy after login
Copy after login

"enumerate". Really? Python, this is not a board meeting. Just relax.


  1. Python loop suddenly... stopped working

Want to modify a list while looping through it? Python will look at you blankly and say:

"You're overthinking."

Example:

char* fruits[] = {"apple", "banana", "cherry"};
for (int i = 0; i < 3; i++) {
    printf("%s\n", fruits[i]);
}
Copy after login
Copy after login

There are now missing elements in the list because Python got lost along the way. If Python were a waiter, this would be the equivalent of clearing your table before you even finish your meal.


  1. An infinite loop that is not an infinite loop

Try writing a classic infinite loop in Python. You know, for fun. The following is how to write it in C language:

fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
    print(fruit)
Copy after login
Copy after login

The following is the Python version:

doubled = [x * 2 for x in range(10)]
Copy after login
Copy after login

It does work, but it feels weird. Python doesn't even try to emulate a classic infinite loop. It's just an...infinite truth.


Conclusion: Python’s for loop is not bad - It’s just us who can’t do it

The truth is, Python’s for loop is not that bad. It’s just that we ourselves are spoiled. Python's loops are so intuitive, concise, and powerful that we forget the pain of manually tracking indexes or dealing with segfaults.

So, the next time you complain about Python’s for loops, remember this: Python is not terrible. It's just tired of holding your hand.

The above is the detailed content of Python Sucks at For Loops – And That's Exactly Why We Love It. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
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

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

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

Java Tutorial
1664
14
PHP Tutorial
1268
29
C# Tutorial
1242
24
Python vs. C  : Applications and Use Cases Compared Python vs. C : Applications and Use Cases Compared Apr 12, 2025 am 12:01 AM

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

Python: Games, GUIs, and More Python: Games, GUIs, and More Apr 13, 2025 am 12:14 AM

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

The 2-Hour Python Plan: A Realistic Approach The 2-Hour Python Plan: A Realistic Approach Apr 11, 2025 am 12:04 AM

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 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.

How Much Python Can You Learn in 2 Hours? How Much Python Can You Learn in 2 Hours? Apr 09, 2025 pm 04:33 PM

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python and Time: Making the Most of Your Study Time Python and Time: Making the Most of Your Study Time Apr 14, 2025 am 12:02 AM

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python: Automation, Scripting, and Task Management Python: Automation, Scripting, and Task Management Apr 16, 2025 am 12:14 AM

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python: Exploring Its Primary Applications Python: Exploring Its Primary Applications Apr 10, 2025 am 09:41 AM

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

See all articles