Python Interview Questions for 2 Years Experience (Expert Guide)

Python has evolved from being just a scripting language to a core component of enterprise and startup stacks alike. With 2 years of experience under your belt, you’re no longer a fresher — interviewers now expect depth, real-world exposure, and clarity in design and debugging. This guide is tailor-made for you.

Whether you’re applying for a backend developer, data analyst, automation engineer, or machine learning role, this post breaks down the most asked Python interview questions for 2 years experience, along with real examples, explanations, and insights.

🔍 What to Expect in Python Interviews at 2 Years Experience?

At this stage, companies expect that:

You’ve worked on real-world projects.
You can read, write, and debug Python code efficiently.
You understand object-oriented design.
You’ve touched frameworks like Flask, Django, or tools like Pandas, SQLAlchemy.
You’re familiar with testing, version control, and clean code practices.

🔹 Section 1: Core Python – Deep Dive Questions

1. What are Python’s memory management mechanisms?

Python uses automatic memory management involving:

Reference Counting: Every object has a reference count; when it reaches 0, the object is deallocated.
Garbage Collection: Python has a cyclic garbage collector to handle reference cycles.

🔥 Pro Tip: Mention how you’ve used gc module in a memory-intensive script or profiling tool like objgraph.

2. What’s the difference between deep copy and shallow copy?

Shallow Copy: Copies references (one level deep).
Deep Copy: Recursively copies all nested objects.

import copy
a = [[1, 2], [3, 4]]
shallow = copy.copy(a)
deep = copy.deepcopy(a)

3. How is Python pass-by-object-reference different from pass-by-value?

Python passes references to objects, not actual objects or copies. However, immutable objects can’t be changed inside a function.

def update_list(lst):
    lst.append(4)

my_list = [1, 2, 3]
update_list(my_list)
print(my_list)  # [1, 2, 3, 4]

4. What are Python iterators and generators?

Iterator: Object with __iter__() and __next__() methods.
Generator: Function using yield to produce a lazy sequence.

def counter():
    yield 1
    yield 2
    yield 3

Use cases: Lazy loading, reading large files line-by-line, streaming APIs.

5. Explain the GIL (Global Interpreter Lock).

The GIL ensures only one thread executes Python bytecode at a time. It’s a bottleneck for CPU-bound multithreading but doesn’t affect I/O-bound concurrency.

💡 Tip: Mention using multiprocessing for parallel CPU-bound tasks.

🔹 Section 2: Object-Oriented Python (OOP)

6. How is inheritance implemented in Python?

class Animal:
    def speak(self):
        print("Animal speaks")

class Dog(Animal):
    def speak(self):
        print("Bark")

Python supports:

Single and multiple inheritance
super() for calling parent methods

7. What is method resolution order (MRO)?

MRO defines the order in which classes are searched during inheritance. Python uses C3 linearization.

print(Dog.__mro__)

8. What are dunder (magic) methods?

Special methods like:

__init__(), __str__(), __len__(), __eq__() etc.
Customize class behavior for operators, printing, hashing.

class Product:
    def __str__(self):
        return "Product as string"

🔹 Section 3: Coding + Real-Life Scenarios

9. Reverse words in a sentence without using built-in methods.

def reverse_words(s):
    return ' '.join(s.split()[::-1])

10. Find the missing number in a sequence 1 to n.

def find_missing(arr):
    n = len(arr) + 1
    return n * (n + 1) // 2 - sum(arr)

11. What’s the time complexity of Python list vs set lookup?

list: O(n)
set: O(1) average-case

Always use sets/dicts for membership tests for large datasets.

🔹 Section 4: Python Standard Library & Built-in Modules

12. Which modules have you used extensively?

Example answers:

collections (Counter, defaultdict)
itertools (chain, combinations)
os, sys, logging
datetime, re

13. **What does `*args` and `kwargs` allow you to do?

*args: Variable positional arguments
**kwargs: Variable keyword arguments

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

14. How to handle exceptions effectively in production?

Use try-except, log tracebacks, don’t suppress critical errors.

try:
    risky_operation()
except Exception as e:
    logging.error("Something went wrong", exc_info=True)

🔹 Section 5: Web Frameworks – Flask or Django

15. How does routing work in Flask?

@app.route('/user/<name>')
def user(name):
    return f"Hello {name}"

16. Explain Django ORM and how it simplifies DB operations.

You define models as Python classes. Django generates queries under the hood.

class Book(models.Model):
    title = models.CharField(max_length=100)

17. How do you handle file uploads in Django?

Use FileField or ImageField and configure MEDIA_ROOT.

class Upload(models.Model):
    file = models.FileField(upload_to='uploads/')

🔹 Section 6: Testing, CI/CD, and Code Quality

18. What are some Python testing tools you’ve used?

unittest (standard)
pytest (more powerful, fixtures)
mock (for mocking dependencies)

19. What are fixtures in pytest?

Fixtures help initialize data/setup for tests. They’re reusable.

@pytest.fixture
def sample_user():
    return User(name="Alice")

20. How do you ensure code quality in Python?

Linting: pylint, flake8
Type hints: mypy
Formatters: black, autopep8

🔹 Section 7: Advanced Python Questions

21. Difference between `is` and `==`?

== compares values.
is compares object identity (memory address).

22. Explain decorators with an example.

def log(func):
    def wrapper(*args, **kwargs):
        print("Calling", func.__name__)
        return func(*args, **kwargs)
    return wrapper

@log
def greet():
    print("Hello")

23. What is a context manager?

Used for resource management (e.g., files, DBs).

with open('file.txt') as f:
    content = f.read()

Create your own using __enter__() and __exit__().

24. How to implement a singleton pattern in Python?

class Singleton:
    _instance = None
    def __new__(cls):
        if not cls._instance:
            cls._instance = super().__new__(cls)
        return cls._instance

🔹 Section 8: Real-World Problem Solving

25. You’re building a REST API. What Python tools do you use?

Flask / Django REST framework
Marshmallow or Pydantic for validation
Celery for async jobs
Gunicorn + Nginx for deployment
Unit + Integration tests

26. How would you optimize a slow Python script?

Profile with cProfile
Use sets/dicts instead of lists
Replace recursion with iteration
Avoid unnecessary loops
Use NumPy for numerical operations

27. What’s the biggest Python bug you’ve fixed recently?

Use this question to demonstrate:

Debugging skills
Tools used (pdb, logs)
Communication with team

🧠 Final Interview Tips for 2-Year Python Developers

Think out loud: Explain your approach clearly during interviews.
Master the fundamentals: Lists, sets, dicts, loops, OOP, error handling.
Go beyond syntax: Understand performance and design implications.
Use GitHub to showcase real projects.
Prepare questions to ask interviewers about team practices and codebase.

🔚 Conclusion

Landing a Python role with 2 years of experience means proving you’re more than a syntax user—you’re a problem solver, collaborator, and software craftsman. By mastering these interview questions, you’ll not only crack the interview but also stand out as a confident and capable Python developer.