6 Python Anti-Patterns That Are Slowing You Down

Your code runs — but these patterns are quietly killing your speed, clarity, and sanity.

6 Python Anti-Patterns That Are Slowing You Down

Whether you’re a Python beginner or a seasoned developer, writing clean, efficient, and maintainable code is a constant pursuit. But sometimes, we pick up habits that seem fine — until they quietly start sabotaging our productivity and performance.

These subtle missteps are called anti-patterns. And if you’re not careful, they’ll bloat your code, slow down execution, confuse collaborators, and complicate debugging.

In this article, we’ll walk through six common Python anti-patterns you might be using without realizing it — and how to fix them.

1. Using Mutable Default Arguments

def append_item(item, target_list=[]): 
    target_list.append(item) 
    return target_list

Why it’s a problem:
That default list ([]) is evaluated only once — when the function is defined, not each time it’s called. This leads to state being shared between function calls.

print(append_item(1))  # [1] 
print(append_item(2))  # [1, 2] ← Surprise!

The fix:

def append_item(item, target_list=None): 
    if target_list is None: 
        target_list = [] 
    target_list.append(item) 
    return target_list

2. Overusing List Comprehensions for Side Effects

[print(x) for x in range(10)]

Why it’s a problem:
List comprehensions are meant to create new lists, not for executing side effects like printing or logging. This code creates a list of Nones (from print()), then throws it away — confusing and wasteful.

The fix:
Just use a regular loop:

for x in range(10): 
    print(x)

Clean, explicit, and readable.

3. Catching Broad Exceptions

try: 
    process_data() 
except Exception: 
    pass

Why it’s a problem:
Catching all exceptions (Exception, or worse, a bare except:) swallows errors you probably didn’t intend to handle — including programming errors like TypeError, NameError, or KeyboardInterrupt.

The fix:
Be precise. Catch only what you expect.

try: 
    process_data() 
except ValueError: 
    print("Invalid input.")

And if you must catch broadly, log or re-raise the error.

except Exception as e: 
    logging.error(f"Unexpected error: {e}") 
    raise

4. Neglecting Python’s Built-in Functions

squared = [] 
for i in range(10): 
    squared.append(i * i)

Why it’s a problem:
You’re reinventing the wheel. Python offers expressive, optimized tools like map(), sum(), any(), zip(), enumerate(), and generator expressions.

The fix:

squared = [i * i for i in range(10)]

Or for large datasets, use generators to save memory:

squared = (i * i for i in range(10))

Know your toolbox — it makes your code shorter and faster.

5. Writing Unpythonic Code

if x == True: 
    do_something()

Why it’s a problem:
Python is not JavaScript or C++. You don’t need to compare booleans to True or False directly. It’s verbose and awkward.

The fix:
Write in idiomatic Python:

if x: 
    do_something()

The same goes for checking empty collections:

# Instead of: if len(my_list) > 0: 
if my_list: 
    # do something

Writing “Pythonic” code isn’t just style — it’s about clarity, conciseness, and leveraging the language as intended.

6. Copying Instead of Understanding

dict1 = {"a": 1} 
dict2 = dict1 
dict2["b"] = 2 
print(dict1)  # {'a': 1, 'b': 2}

Why it’s a problem:
This isn’t just about variables — it’s about references. If you don’t understand mutability and object identity in Python, bugs will creep in silently.

The fix:
If you need a true copy, use .copy() or the copy module:

import copy 
 
dict2 = dict1.copy()           # Shallow copy 
dict3 = copy.deepcopy(dict1)   # Deep copy

Better yet, take time to understand Python’s object model. It’s a one-time investment that pays off forever.

Final Thoughts

Python is elegant, but it doesn’t always protect you from your own code. These anti-patterns don’t usually break your program — they degrade it, making it harder to maintain, slower to run, and tougher to debug.

The good news? Every anti-pattern is an opportunity. By recognizing and refactoring them, you’re not just writing better Python — you’re leveling up as a developer.

Which of these anti-patterns have you seen (or used)? Got one I missed? Let’s discuss in the comments.

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🧠 Happy coding!