Advanced Python Interview Questions

🚀 Crack the Code: Advanced Python Interview Questions (With Deep Explanations & Examples) 🐍🔥

Python interviews at a senior / advanced level don’t test syntax—they test how you think. This guide will help you stand out by mastering advanced concepts, real-world examples, and interview-winning tricks 💡

🧠 1. What is the difference between __new__() and __init__()?

🔍 Explanation

• __new__() creates the object
• __init__() initializes the object

__new__() is called before __init__().

🧪 Example

class Demo:
    def __new__(cls):
        print("Creating instance")
        return super().__new__(cls)

    def __init__(self):
        print("Initializing instance")

obj = Demo()

✅ Output

Creating instance
Initializing instance

📌 Used in: Singletons, immutable objects

🧠 2. Explain Python’s Global Interpreter Lock (GIL)

🔍 Explanation

The GIL allows only one thread to execute Python bytecode at a time.

❌ Problem

• CPU-bound multithreading doesn’t scale well

✅ Solution

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

🧪 Example

import threading

def task():
    print("Running task")

threading.Thread(target=task).start()

🧠 Interview Tip: 👉 Python threads ≠ true parallelism (for CPU tasks)

🧠 3. What are Python Decorators? How do they work internally?

🔍 Explanation

Decorators wrap functions to modify behavior without changing original code.

🧪 Example

def log(func):
    def wrapper():
        print("Before execution")
        func()
        print("After execution")
    return wrapper

@log
def hello():
    print("Hello World")

hello()

✅ Output

Before execution
Hello World
After execution

📌 Used in: Authentication, logging, caching, rate-limiting

🧠 4. Explain Mutable vs Immutable Objects

🔍 Explanation

🧪 Example

a = [1, 2]
b = a
b.append(3)
print(a)

✅ Output

[1, 2, 3]

⚠️ Common Interview Trap

🧠 5. What is Python’s Memory Management?

🔍 Explanation

Python uses:

• Reference Counting
• Garbage Collection (GC) for cyclic references

🧪 Example

import sys

x = []
print(sys.getrefcount(x))

📌 GC handles cycles like:

a = []
b = []
a.append(b)
b.append(a)

🧠 6. What are Metaclasses?

🔍 Explanation

Metaclasses define how classes behave

“Classes are objects too!”

🧪 Example

class Meta(type):
    def __new__(cls, name, bases, dct):
        dct["version"] = 1.0
        return super().__new__(cls, name, bases, dct)

class App(metaclass=Meta):
    pass

print(App.version)

📌 Used in: ORMs, frameworks like Django

🧠 7. What is Monkey Patching?

🔍 Explanation

Changing a class or module at runtime

🧪 Example

class A:
    def greet(self):
        return "Hello"

def new_greet(self):
    return "Hi"

A.greet = new_greet
print(A().greet())

⚠️ Avoid in production unless absolutely required

🧠 8. Explain *args and **kwargs in Depth

🔍 Explanation

• *args → Variable positional arguments
• **kwargs → Variable keyword arguments

🧪 Example

def demo(*args, **kwargs):
    print(args)
    print(kwargs)

demo(1, 2, a=10, b=20)

📌 Used in: APIs, decorators, extensible functions

🧠 9. What are Generators and Why Are They Memory Efficient?

🔍 Explanation

Generators yield values one at a time, saving memory.

🧪 Example

def count_up(n):
    for i in range(n):
        yield i

gen = count_up(1000000)

🔥 Huge performance boost for large datasets

🧠 10. Difference Between Deep Copy and Shallow Copy

🔍 Explanation

• Shallow Copy → References
• Deep Copy → New objects

🧪 Example

import copy

a = [[1, 2]]
b = copy.copy(a)
c = copy.deepcopy(a)

a[0].append(3)
print(b)
print(c)

🧠 11. What is Python’s __slots__?

🔍 Explanation

Reduces memory usage by preventing dynamic attribute creation.

🧪 Example

class User:
    __slots__ = ["name", "age"]

u = User()
u.name = "Alex"

📌 Used in: Performance-critical systems

🧠 12. Explain Async/Await in Python

🔍 Explanation

Used for non-blocking I/O

🧪 Example

import asyncio

async def main():
    await asyncio.sleep(1)
    print("Done")

asyncio.run(main())

⚡ Faster than threading for I/O tasks

🎯 Interview Tips & Tricks (Must Read!) 🔥

✅ 1. Think Out Loud

Interviewers care about reasoning, not just answers 🧠

✅ 2. Use Real-World Examples

Relate answers to APIs, background jobs, data processing

✅ 3. Know Trade-offs

Always explain pros vs cons ⚖️

✅ 4. Master These Topics

• OOP & Design Patterns
• Memory Management
• Concurrency
• Data Structures
• Performance Optimization

✅ 5. Write Clean Code

Readable > Clever 🧼

✨ Final Words

💬 “Python rewards clarity of thought more than clever tricks.”

Master these advanced Python concepts, and you’ll walk into any interview with confidence & clarity 🚀🐍