11 Python Mistakes Even Senior Developers Make

Python's elegance and simplicity have made it a favorite among developers worldwide. Its readable syntax and vast ecosystem empower developers to build robust applications efficiently. However, even seasoned Python developers can fall into subtle traps that lead to bugs or inefficiencies. In this post, we'll explore 11 frequent mistakes even experienced developers make—and how to avoid them.

1. Using Mutable Default Arguments

Using mutable default arguments like lists or dictionaries can lead to unexpected behavior:

# Incorrect
def append_to_list(value, my_list=[]):
    my_list.append(value)
    return my_list

# Correct
def append_to_list(value, my_list=None):
    if my_list is None:
        my_list = []
    my_list.append(value)
    return my_list

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2. Catching All Exceptions Indiscriminately

Using a bare except: can mask real errors:

# Incorrect
try:
    risky_operation()
except:
    pass  # Swallows all exceptions

# Correct
try:
    risky_operation()
except ValueError as e:
    handle_value_error(e)

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3. Confusing is with ==

is checks identity; == checks value:

a = [1, 2, 3]
b = a
c = list(a)

print(a is b)  # True
print(a == c)  # True
print(a is c)  # False

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4. Shadowing Built-in Names

Avoid naming variables after built-ins like list, dict, etc.:

# Incorrect
list = [1, 2, 3]

# Correct
my_list = [1, 2, 3]

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5. Overusing List Comprehensions for Side Effects

List comprehensions are not meant for actions with side effects like printing:

# Incorrect
[print(x) for x in range(5)]

# Correct
for x in range(5):
    print(x)

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6. Misunderstanding Python's Scope (LEGB Rule)

Python resolves variable names using Local, Enclosing, Global, Built-in scopes:

def outer():
    x = 'enclosing'
    def inner():
        print(x)
    inner()

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7. Not Using Generators When Appropriate

Generators are more memory efficient than list comprehensions for large datasets:

# List comprehension
squares = [x*x for x in range(1000000)]

# Generator expression
squares = (x*x for x in range(1000000))

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8. Ignoring Virtual Environments

Virtual environments prevent dependency conflicts:

# Create and activate a virtual environment
python -m venv env
source env/bin/activate  # macOS/Linux
env\Scripts\activate     # Windows

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9. Writing Inefficient Loops Instead of Pythonic Constructs

Use comprehensions for readability and performance:

# Inefficient
squares = []
for x in range(10):
    squares.append(x * x)

# Pythonic
squares = [x * x for x in range(10)]

10. Not Using the with Statement for File Operations

The with statement ensures proper file cleanup:

# Correct
with open('file.txt', 'r') as file:
    data = file.read()

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11. Overusing Global Variables

Too many globals can lead to tightly coupled and hard-to-maintain code. Prefer passing arguments or using class attributes where possible.