The Python Interview Questions No One Prepares For

You’ve practiced list comprehensions and OOP. But when the real interview begins, it’s the unexpected questions that trip you up.

The Python Interview Questions No One Prepares For

These are the Python interview questions that rarely make it into prep guides — but show up when it really matters. Let’s make sure you’re ready.

You’ve studied list comprehensions, practiced OOP, and nailed decorators—but your Python interview still caught you off guard. Why? Because there are questions no one tells you about—until it's too late.

This article dives into the under-the-radar Python questions that don’t appear on most cheat sheets but absolutely show up in real-world interviews.

Prepare for the curveballs — and walk into your next Python interview with confidence.

1. What Happens When You Modify a List While Iterating Over It?

Most developers think they know how iteration works — until this happens:

nums = [1, 2, 3, 4] 
for i in nums: 
    if i % 2 == 0: 
        nums.remove(i) 
print(nums) 
 
# output : [1, 3]

But wait, shouldn’t it be [1, 3]? Yes—but do you know why?

Modifying a list while iterating over it leads to unpredictable behavior because the iterator doesn’t account for index shifts after remove(). This question tests your grasp of Python internals, not just syntax.

Use a copy (nums[:]) or list comprehension to avoid surprises.

2. Can You Explain Python’s Memory Model and the Role of id()?

Try this:

a = 256 
b = 256 
print(id(a) == id(b))  # True 
 
a = 257 
b = 257 
print(id(a) == id(b))  # False

Python interns small integers (from -5 to 256) for memory efficiency. Larger integers are not cached, so different objects are created.

Most developers don’t know this behavior, but interviewers love it to test your depth.

3. What’s the Difference Between is and ==?

It seems trivial… until it isn’t.

a = [1, 2, 3] 
b = a 
c = list(a) 
 
print(a == b)  # True 
print(a is b)  # True 
print(a == c)  # True 
print(a is c)  # False

== checks value equality.

is checks object identity.

This subtle distinction is a classic tripwire in interviews — especially with mutable objects.

4. Why Are Default Mutable Arguments Dangerous?

This one burns even experienced devs:

def append_to_list(value, my_list=[]): 
    my_list.append(value) 
    return my_list 
 
print(append_to_list(1))  # [1] 
print(append_to_list(2))  # [1, 2] ???

Wait, what? Shouldn’t it reset each time?

Nope. Default values in Python are evaluated once — when the function is defined. That list sticks around between calls.

Correct approach:

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

5. How Do You Handle Circular Imports?

This one doesn’t come up until you’re building something big — or during system design rounds.

# module_a.py 
from module_b import func_b 
 
def func_a(): 
    func_b() 
 
# module_b.py 
from module_a import func_a 
 
def func_b(): 
    func_a()

Result? ImportError.

Fix it:

Move imports inside functions

Restructure your modules

Use import guards or factory functions

It’s a question that tests not just Python knowledge but your architectural thinking.

6. What’s the Difference Between @staticmethod, @classmethod, and Instance Methods?

Simple definitions won’t cut it. Interviewers want you to explain use cases.

• @staticmethod: Doesn't access self or cls. Use when logic is related to the class but doesn't need context.
• @classmethod: Uses cls. Useful for alternate constructors.
• Instance method: Default. Uses self.

Bonus Question: Can @classmethod call @staticmethod? (Yes!)

7. What’s the Output of This Weird Slice?

x = "abcdef" 
print(x[::-1])  # ?

Answer: 'fedcba'

The slicing syntax [::-1] reverses the string. Understanding slice steps and direction can help with advanced questions on sequences and memory optimization.

8. What Is the GIL and Why Does It Matter?

If you’re applying for a role involving concurrency or performance, expect this.

GIL (Global Interpreter Lock) ensures that only one thread executes Python bytecode at a time — even on multi-core machines. It protects memory management but limits true parallelism.

Know when to use:

• multiprocessing for CPU-bound tasks
• threading for I/O-bound tasks

9. What’s a Closure? When Would You Use One?

def outer(x): 
    def inner(y): 
        return x + y 
    return inner 
 
add_5 = outer(5) 
print(add_5(3))  # 8

Why does add_5 remember x=5?

Closures remember the enclosing scope. This question evaluates your ability to write functional, elegant code.

10. Can You Explain Context Managers Beyond with open()?

If you think context managers are just for files, think again.

from contextlib import contextmanager 
 
@contextmanager 
def managed_resource(): 
    print("Before") 
    yield 
    print("After") 
 
with managed_resource(): 
    print("Inside block")

Output:

Before 
Inside block 
After

Custom context managers are used in DB connections, threading, testing, etc. You can even implement one using __enter__ and __exit__.

Final Thoughts: Prepare Smarter, Not Just Harder

If you want to stand out in Python interviews, don’t just memorize syntax. Understand the why behind the what. These overlooked questions reveal more than just knowledge — they show thought process, problem-solving, and maturity.

Bonus Tip: Practice Explaining Your Code

Being technically correct is only half the battle. Interviewers love candidates who can clearly explain what’s happening, why they made certain decisions, and what trade-offs were considered.

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