Python Interview Questions

The art of preparing for a Python interview goes beyond memorizing syntax. Many candidates focus on basic questions but overlook deeper concepts like Pythonic best practices. Are you confident in applying list comprehensions or understanding the internals of a dict?

Mastering these often-overlooked aspects can set you apart. In this article, we dive into key topics and give practical tips. By the end, you will feel ready to tackle questions with confidence and make a strong impression.

Data Types & Structures

Python offers flexible data types, but interviews often test your depth of understanding. Expect questions on the differences between lists, tuples, sets, and dictionaries. For example, why choose a tuple over a list? Or how does a dict manage key collisions under the hood?

Common practical tasks include swapping variables:

a, b = b, a

Or merging two dicts:

merged = {**dict1, **dict2}

Be ready to explain:

• Mutability and hashability
• Ordering guarantees in dicts (3.7+)
• Use cases for sets vs lists

Example question:

# Find unique items while preserving order
def unique_preserve(seq):
    seen = set()
    return [x for x in seq if not (x in seen or seen.add(x))]

Understand how operations on these structures affect performance. Time complexity matters.

Control Flow Mastery

Control flow questions look simple but can test deeper knowledge. You might face tasks on loops, comprehensions, or generator expressions. For instance, rewrite a loop as a comprehension.

Example:

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

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

Interviewers check if you know:

• Generator expressions vs list comprehensions
• The use of else in loops
• The difference between map/filter and comprehensions

Be comfortable with:

• Using break, continue, and else in loops
• Writing a generator to yield Fibonacci numbers

Practice by converting real tasks into comprehensions. It shows fluency.

Functions & Decorators

Questions on functions often focus on parameters, scope, and decorators. Expect topics like default arguments, *args, **kwargs, and closures.

Example decorator:

def timer(func):
    import time
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        print(f'Elapsed: {time.time() - start:.4f}s')
        return result
    return wrapper

@timer
def compute(n):
    return sum(range(n))

Key points to master:

• Difference between *args and **kwargs
• Default argument pitfalls (mutable defaults)
• How closures capture variables

Write small decorators to log or time a function. It shows you grasp advanced function features.

Object-Oriented Concepts

Python’s OOP features are a common focus. Be ready to explain classes, inheritance, and polymorphism. Questions like “What is method resolution order?” can pop up.

Example:

class Animal:
    def speak(self):
        raise NotImplementedError

class Dog(Animal):
    def speak(self):
        return 'Woof'

class Cat(Animal):
    def speak(self):
        return 'Meow'

for pet in (Dog(), Cat()):
    print(pet.speak())

Interviews may cover:

• MRO in multiple inheritance
• Abstract base classes vs interfaces
• Data encapsulation in Python

Demonstrate using isinstance, mixins, and custom __str__ methods.

Error Handling Essentials

Understanding exceptions is key. Expect questions on try/except/finally and custom exceptions.

Example:

import json

def load_data(s):
    try:
        return json.loads(s)
    except json.JSONDecodeError:
        print('JSON decode error')
    finally:
        print('Cleanup if needed')

Review:

• When finally runs
• Raising vs re-raising exceptions
• Best practices for custom exception classes

Always catch specific exceptions. It avoids hiding bugs and makes debugging easier.

Modules & Frameworks

Beyond core Python, interviewers assess your familiarity with popular modules and frameworks. You might discuss building a simple web API. For example, creating a REST API using Flask shows real-world experience.

Also, knowledge of version control complements technical skills. Check out this Git and GitHub Guide to sharpen your workflow.

Other areas:

• Working with asyncio for concurrency
• Data handling with pandas or numpy
• Testing with unittest or pytest

Highlight projects where you used these tools. It builds credibility.

Conclusion

Interview success in Python relies on solid basics and practical fluency. By focusing on data structures, control flow, functions, OOP, error handling, and common frameworks, you cover the core areas that employers value. Practice coding each concept, review common pitfalls, and build small projects that showcase your skills.

Remember, it’s not just about answering a question but showing clear thought and real-world application. Approach each problem with confidence, communicate your reasoning, and adapt examples from your experience. With thorough preparation and hands-on practice, you’ll stand out and ace your Python interview.