10 Python Mistakes You Don’t Know You’re Making

Even experienced devs slip up.

10 Python Mistakes You Don’t Know You’re Making

Whether you’re a seasoned developer or just dipping your toes into Python, there are pitfalls that sneak past even the most attentive coders. Some of these mistakes don’t throw errors. They quietly degrade performance, cause bugs down the line, or simply make your code less Pythonic.

Here are 10 common Python mistakes you might not know you’re making — and how to fix them.

1. Using Mutable Default Arguments

def add_item(item, items=[]): 
    items.append(item) 
    return items

The Problem: The default list items=[] is shared across all function calls. This leads to unexpected behavior.

Fix it:

def add_item(item, items=None): 
    if items is None: 
        items = [] 
    items.append(item) 
    return items

2. Confusing is with ==

a = [1, 2] 
b = [1, 2] 
print(a is b)  # False 
print(a == b)  # True

The Problem: is checks identity (memory location), not value equality. Using it incorrectly can cause subtle logic bugs.

Use == when comparing values, and reserve is for identity checks (like x is None).

3. Overusing List Comprehensions

# Looks slick but... 
[print(x) for x in range(10)]

The Problem: List comprehensions are meant to create lists, not for side effects like printing. This creates an unnecessary list of None.

Fix it:

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

Readable > Clever. Every time.

4. Not Understanding Python’s Loop-Else

for item in items: 
    if item == target: 
        break 
else: 
    print("Not found")

Wait… what’s that else doing on a for loop?

The Gotcha: The else block only executes if the loop wasn’t terminated by a break. It’s one of Python’s lesser-known features—and easily missed.

5. Catching Too Broad Exceptions

try: 
    risky_operation() 
except: 
    print("Something went wrong.")

The Problem: Catching everything with a bare except can mask bugs like KeyboardInterrupt or SystemExit.

Fix it:

try: 
    risky_operation() 
except ValueError as e: 
    print(f"Value error: {e}")

Always be specific. Your future self (and your debugger) will thank you.

6. Modifying a List While Iterating Over It

items = [1, 2, 3, 4] 
for item in items: 
    if item % 2 == 0: 
        items.remove(item)

Result? Unexpected behavior. Some items get skipped.

Solution: Use a copy.

for item in items[:]: 
    if item % 2 == 0: 
        items.remove(item)

Or better yet, use a list comprehension for clean filtering.

7. Not Utilizing Python’s Standard Library

You’d be surprised how many developers write their own implementations of common utilities — like counting elements or reading files line by line.

Better way:

from collections import Counter 
counts = Counter(['a', 'b', 'a', 'c'])

Before reinventing the wheel, check the docs. Python’s standard library is rich, elegant, and battle-tested.

8. Misusing lambda for Complex Logic

sorted(data, key=lambda x: (x.price, x.quantity, -x.timestamp))

The Problem: It works, but if the logic gets complex, lambda quickly becomes unreadable.

Solution: Define a regular function:

def sort_key(item): 
    return (item.price, item.quantity, -item.timestamp) 
 
sorted(data, key=sort_key)

Readable code wins in the long run.

9. Neglecting Virtual Environments

We’ve all been there — accidentally installing a package globally and breaking another project.

Use:

python -m venv venv 
source venv/bin/activate  # Unix/macOS 
venv\Scripts\activate     # Windows

It keeps dependencies isolated and your projects safe from version conflicts.

10. Ignoring Type Hints (Even When You’re Not Using a Linter)

def process(data): 
    ...

Sure, it runs. But what is data? A string? A list? A dictionary?

Use type hints:

def process(data: dict[str, int]) -> None: 
    ...

Even without static type checking tools like mypy, type hints serve as valuable inline documentation—and future-proof your codebase.

Final Thoughts

Python is a beautiful, expressive language — but it comes with quirks that can catch even experienced devs off guard. The good news? Once you spot these common pitfalls, they’re easy to avoid.

Write clean, thoughtful code. Embrace Pythonic practices. And above all — keep learning.

If you’ve been making any of these mistakes, don’t sweat it. We all do. The goal is progress, not perfection.

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