10 Common Python Mistakes Every Developer Makes (And How to Avoid Them in 2026)

Even experienced developers make small mistakes that lead to bugs, slow performance, or unreadable code. Below are 10 common Python mistakes—and the simple, correct patterns you should use instead.

1. Using Mutable Default Arguments

Defining a function with a mutable default (like a list or dict) can cause the same object to persist across calls.

def add_item(item, items=[]):
    items.append(item)
    return items  # BAD: same list reused across calls

Use None as the default and create a new object inside the function.

def add_item(item, items=None):
    if items is None:
        items = []
    items.append(item)
    return items  # Good: fresh list each call

2. Misunderstanding Shallow vs Deep Copy

Assigning one list to another does not copy it; both names reference the same object.

list2 = list1  # Not a copy — both reference the same list

Use copy() for a shallow copy or deepcopy() (from copy) for nested objects.

copy_list = list1.copy()          # shallow copy
from copy import deepcopy
deep_copy = deepcopy(list1)         # full deep copy

3. Ignoring Exceptions or Using Bare except:

Catching all exceptions silently hides errors and makes debugging hard.

try:
    do_something()
except:
    pass  # BAD: hides all errors

Catch specific exceptions and handle or log them.

try:
    do_something()
except ValueError as e:
    print("Value error:", e)  # Good: specific handling

4. Using print() Instead of Logging

print() is fine for quick tests, but production code needs structured logging.

print("Error happened!")  # Not ideal for production

Use the logging module for levels, timestamps, and better control.

import logging
logging.basicConfig(level=logging.INFO)
logging.error("Error happened!")

5. Writing Slow Loops Instead of Pythonic Constructs

Explicit loops are sometimes necessary, but many patterns are faster and clearer with comprehensions.

result = []
for x in numbers:
    result.append(x * 2)  # OK, but verbose

Use list comprehensions where appropriate.

result = [x * 2 for x in numbers]  # Faster and more concise

6. Forgetting to Close Files

Opening files without context managers can leave files open and cause resource issues.

f = open("data.txt")
data = f.read()  # File may remain open

Always use with for safe file handling.

with open("data.txt") as f:
    data = f.read()  # File closed automatically

7. Confusing == with is

Use == for value equality and is for identity checks (same object).

if x is 10:    # Dangerous — identity not equality
    ...

Prefer == for numeric/string comparisons.

if x == 10:
    ...  # Correct for equality

8. Not Using Virtual Environments

Installing packages globally leads to version conflicts and deployment problems.

pip install flask  # Not isolated

Create a virtual environment for each project.

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

9. Overusing Global Variables

Global state increases coupling and makes testing harder.

x = 10
def update():
    global x
    x = 20  # Hard to track and test

Prefer passing values or returning new state from functions.

def update(x):
    return x + 10

10. Ignoring Code Readability

Cryptic variable names and dense expressions make maintenance costly.

a=b+c*d-f  # Hard to understand

Prefer clear variable names and break complex expressions into steps.

total = base + tax * rate - discount  # Readable and maintainable

Conclusion

Avoiding these common mistakes will make your code more reliable, maintainable, and professional. Start by fixing one or two in your next project — small changes compound into much better software.