Python Programming Job Interview Questions and Answers

Fresher or Beginner-Level Python Job Interview Questions & Answers

1. What are the key features of Python?

Python is:

• Interpreted – Executes code line by line without compilation.
• Dynamically Typed – No need to declare variable types.
• Object-Oriented – Supports classes and objects.
• High-Level – Simple and readable syntax.
• Portable – Runs on multiple platforms (Windows, macOS, Linux).
• Extensible – Can integrate with C, C++, Java, etc.

2. Explain the difference between Python 2 and Python 3.

Python 3 is recommended as Python 2 is no longer supported.

3. How is memory managed in Python?

Python uses:

• Reference Counting – Each object has a reference count. When it reaches zero, the object is deleted.
• Garbage Collection (GC) – Detects and removes cyclic references.
• Memory Pooling – Uses private heaps for memory management.

Example:

import sys

a = [1, 2, 3]
print(sys.getrefcount(a))  # Shows reference count

4. What is the difference between a list and a tuple in Python?

Example:

my_list = [1, 2, 3]  # Can modify
my_tuple = (1, 2, 3)  # Cannot modify

5. Explain Python’s data types with examples.

• Numeric Types: int, float, complex
• Sequence Types: list, tuple, range, string
• Set Types: set, frozenset
• Mapping Types: dict

Example:

num = 10  # Integer
price = 10.5  # Float
fruits = ["Apple", "Banana"]  # List
person = {"name": "John", "age": 25}  # Dictionary

6. What are Python’s built-in data structures?

• Lists – Ordered, mutable collection ([1, 2, 3])
• Tuples – Ordered, immutable collection ((1, 2, 3))
• Dictionaries – Key-value pairs ({"key": "value"})
• Sets – Unordered, unique items ({1, 2, 3})

Example:

my_set = {1, 2, 3, 3}  # Output: {1, 2, 3} (removes duplicates)

7. How does Python handle type conversion (implicit vs. explicit)?

• Implicit conversion – Python automatically converts types: x = 5 # int y = 2.5 # float result = x + y # float (7.5)
• Explicit conversion – Manually convert types: num = "10" result = int(num) + 5 # Converts "10" to int

8. What is PEP 8, and why is it important?

PEP 8 is Python’s style guide that improves readability and maintainability.

Key guidelines:

• Use 4 spaces per indentation (not tabs).
• Follow naming conventions (snake_case for variables, CamelCase for classes).
• Keep line length ≤79 characters.

Example:

def add_numbers(a, b):  # Function with proper naming
    return a + b

9. What are *args and kwargs in Python functions?

• *args allows passing multiple positional arguments as a tuple.
• **kwargs allows passing multiple keyword arguments as a dictionary.

Example:

def my_function(*args, **kwargs):
    print(args)  # Tuple of arguments
    print(kwargs)  # Dictionary of keyword arguments

my_function(1, 2, 3, name="Alice", age=25)

Output:

(1, 2, 3)
{'name': 'Alice', 'age': 25}

10. How do you handle exceptions in Python?

Use try-except blocks:

try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero!")

Output:

Cannot divide by zero!

11. What is the purpose of the self keyword in Python?

• self represents the instance of a class.
• It allows accessing instance attributes and methods.

Example:

class Person:
    def __init__(self, name):
        self.name = name  # Instance variable

    def greet(self):
        return f"Hello, my name is {self.name}."

p = Person("Alice")
print(p.greet())

12. How does Python’s range() function work?

Generates a sequence of numbers:

print(list(range(5)))  # [0, 1, 2, 3, 4]
print(list(range(1, 10, 2)))  # [1, 3, 5, 7, 9]

13. What is the difference between deep copy and shallow copy in Python?

• Shallow Copy: Copies only references, so changes in the copy affect the original.
• Deep Copy: Creates a completely independent copy.

Example:

import copy

lst1 = [[1, 2], [3, 4]]
lst2 = copy.deepcopy(lst1)  # Deep Copy

lst1[0][0] = 100
print(lst1)  # [[100, 2], [3, 4]]
print(lst2)  # [[1, 2], [3, 4]] (unchanged)

14. What are Python lambda functions?

A lambda function is an anonymous function with a single expression.

Example:

add = lambda x, y: x + y
print(add(3, 5))  # Output: 8

15. Explain list comprehension with an example.

List comprehension provides a compact way to create lists.

Example:

squares = [x**2 for x in range(5)]
print(squares)  # Output: [0, 1, 4, 9, 16]

16. How do you read and write files in Python?

# Writing to a file
with open("file.txt", "w") as f:
    f.write("Hello, World!")

# Reading from a file
with open("file.txt", "r") as f:
    content = f.read()
    print(content)  # Output: Hello, World!

17. What is the difference between is and == in Python?

• == (Equality Operator) → Compares the values of two objects.
• is (Identity Operator) → Compares the memory addresses (identity) of two objects.

Example:

a = [1, 2, 3]
b = [1, 2, 3]
c = a

print(a == b)  # True (Same values)
print(a is b)  # False (Different memory locations)
print(a is c)  # True (Same memory location)

✅ Use == when checking values and is when checking object identity.

18. How does Python manage memory allocation for variables?

Python uses Automatic Memory Management with:

1. Reference Counting – Each object has a reference count. When it reaches zero, the object is deleted. import sys x = [1, 2, 3] print(sys.getrefcount(x)) # Shows reference count
2. Garbage Collection (GC) – Python automatically removes objects with cyclic references. import gc gc.collect() # Triggers garbage collection manually
3. Memory Pooling – Python uses PyObject_Malloc to allocate memory efficiently.

19. What are Python modules and packages?

• Module: A single Python file containing functions, classes, and variables (.py file).
• Package: A collection of modules inside a directory with an __init__.py file.

Example:

📌 Creating a module (math_utils.py)

def add(a, b):
    return a + b

📌 Using the module in another file

import math_utils
print(math_utils.add(3, 5))  # Output: 8

📌 Creating a package structure:

my_package/
  ├── __init__.py
  ├── module1.py
  ├── module2.py

✅ Modules help in code reusability, and packages organize related modules.

20. What is the difference between mutable and immutable data types in Python?

Example:

✅ Mutable Example (List)

my_list = [1, 2, 3]
my_list[0] = 10  # Allowed
print(my_list)  # Output: [10, 2, 3]

❌ Immutable Example (Tuple)

my_tuple = (1, 2, 3)
my_tuple[0] = 10  # TypeError: 'tuple' object does not support item assignment

✅ Mutable types can be changed, while immutable types remain constant.

Intermediate or Experienced Level Python Language Interview Questions

1. Explain the difference between append() and extend() in Python lists.

• append(item): Adds a single element to the end of a list.
• extend(iterable): Adds multiple elements from an iterable to the list.

Example:

my_list = [1, 2, 3]
my_list.append([4, 5])  
print(my_list)  # Output: [1, 2, 3, [4, 5]]

my_list = [1, 2, 3]
my_list.extend([4, 5])  
print(my_list)  # Output: [1, 2, 3, 4, 5]

✅ Use append() for single elements and extend() for multiple elements.

2. What is a Python generator? How is it different from an iterator?

• Generator: A special function that yields values lazily using yield, saving memory.
• Iterator: An object that implements __iter__() and __next__() methods.

Example:

✅ Generator: (Efficient, does not store all values in memory)

def my_generator():
    for i in range(3):
        yield i  # Returns value without storing all at once

gen = my_generator()
print(next(gen))  # Output: 0
print(next(gen))  # Output: 1

✅ Iterator: (Must define __iter__ and __next__)

class MyIterator:
    def __init__(self):
        self.num = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.num >= 3:
            raise StopIteration
        self.num += 1
        return self.num - 1

it = MyIterator()
print(next(it))  # Output: 0

✅ Generators are a simpler way to create iterators.

3. What are Python decorators? Give an example.

• Decorators modify the behavior of functions without changing their code.
• They use @decorator_name syntax.

Example:

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

@decorator
def say_hello():
    print("Hello!")

say_hello()

✅ Output:

Before function execution
Hello!
After function execution

✅ Decorators are used for logging, authentication, and function modification.

4. What is the difference between @staticmethod, @classmethod, and instance methods?

Example:

class MyClass:
    class_variable = "Class Level"

    @staticmethod
    def static_method():
        return "Static Method - No self, no cls"

    @classmethod
    def class_method(cls):
        return f"Class Method - {cls.class_variable}"

    def instance_method(self):
        return f"Instance Method - {self.class_variable}"

obj = MyClass()
print(MyClass.static_method())  # Static Method
print(MyClass.class_method())  # Class Method
print(obj.instance_method())  # Instance Method

✅ Use @staticmethod for independent utilities, @classmethod for class-level changes, and instance methods for object-specific behavior.

5. Explain the purpose of Python’s with statement.

• Manages resources automatically (like files, sockets, or database connections).
• Ensures proper cleanup using context managers (__enter__ & __exit__).

Example (File Handling):

with open("example.txt", "w") as file:
    file.write("Hello, World!")  # File auto-closes

✅ No need to manually close the file (file.close()).

6. How do you implement multithreading in Python?

Python uses the threading module for multithreading.

Example:

import threading

def print_numbers():
    for i in range(5):
        print(i)

thread1 = threading.Thread(target=print_numbers)
thread1.start()
thread1.join()  # Ensures thread completes execution before proceeding

✅ Useful for I/O-bound tasks, but Python threads are limited by the GIL.

7. What is the Global Interpreter Lock (GIL) in Python?

• GIL prevents multiple threads from executing Python bytecode simultaneously.
• Only one thread runs at a time, even on multi-core processors.

✅ Solution? Use multiprocessing for CPU-intensive tasks.

from multiprocessing import Process

def task():
    print("Running in a separate process")

process = Process(target=task)
process.start()
process.join()

✅ Multithreading is best for I/O tasks (file, network), while multiprocessing is for CPU tasks.

8. What is the difference between a Python set and a dictionary?

Example:

my_set = {1, 2, 3}
my_dict = {"a": 1, "b": 2}

✅ Use sets for unique values, and dictionaries for key-value data.

9. How does Python implement memory management and garbage collection?

✅ Python’s memory management includes:

1. Reference Counting: Objects are deallocated when the count reaches zero.
2. Garbage Collection (GC): Removes cyclic references.
3. Memory Pools: Uses a private heap for object storage.

Example (Manual Garbage Collection):

import gc
gc.collect()  # Manually triggers garbage collection

✅ Python automatically optimizes memory, but developers can manually trigger GC.

10. What is the difference between Python’s __init__ and __new__ methods?

Example:

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

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

obj = MyClass()

✅ Output:

Creating instance
Initializing instance

✅ Use __new__ for controlling instance creation and __init__ for initialization.

11. Explain the difference between deepcopy and copy in Python.

• copy.copy() → Creates a shallow copy, meaning changes to nested objects affect the copy.
• copy.deepcopy() → Creates a deep copy, meaning changes to nested objects do not affect the copy.

Example:

import copy

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

original[0][0] = 99  # Modify original

print(shallow)  # [[99, 2], [3, 4]] (Shallow copy affected)
print(deep)     # [[1, 2], [3, 4]] (Deep copy unchanged)

✅ Use deepcopy() when working with nested objects to avoid unintended modifications.

12. How can you optimize Python code for better performance?

✅ Best practices for optimization:

1. Use List Comprehensions numbers = [i for i in range(10)] # Faster than loops
2. Use Generators for Large Data def large_data(): for i in range(10**6): yield i # Saves memory
3. Use Built-in Functions (sum(), map(), zip())
4. Avoid Unnecessary Loops – Use vectorized operations (NumPy, Pandas).
5. Use multiprocessing for CPU-bound tasks
6. Use cProfile for performance analysis import cProfile cProfile.run('your_function()')

✅ Optimizing code improves speed and reduces memory usage.

13. What is the difference between map(), filter(), and reduce()?

Example:

from functools import reduce

nums = [1, 2, 3, 4]

print(list(map(lambda x: x * 2, nums)))  # [2, 4, 6, 8]
print(list(filter(lambda x: x % 2 == 0, nums)))  # [2, 4]
print(reduce(lambda x, y: x + y, nums))  # 10

✅ Use map() for transformation, filter() for selection, and reduce() for accumulation.

14. What are Python’s built-in functions for functional programming?

Python provides several functional programming tools:

✅ Higher-order functions:

• map(), filter(), reduce()
• lambda (Anonymous functions)

✅ Iterable utilities:

• zip(), enumerate(), sorted(), reversed()

✅ Examples:

nums = [1, 2, 3]
squared = list(map(lambda x: x**2, nums))  # [1, 4, 9]
print(squared)

✅ These functions help write cleaner, more efficient code.

15. What is the difference between Python’s sort() and sorted() functions?

Example:

nums = [3, 1, 2]

nums.sort()  
print(nums)  # [1, 2, 3] (Original modified)

nums = [3, 1, 2]
sorted_nums = sorted(nums)
print(sorted_nums)  # [1, 2, 3] (Original unchanged)

✅ Use sort() when modifying the list in place and sorted() when keeping the original unchanged.

16. How do you handle circular imports in Python?

Circular imports occur when two or more modules import each other.
✅ Solutions:

1. Use import inside a function instead of at the top def func(): import module_b # Delayed import
2. Use absolute imports instead of relative imports
3. Refactor code to avoid circular dependencies
4. Use import guards (if __name__ == "__main__")

✅ Rearranging imports or using local imports often resolves circular import issues.

17. What is the difference between del, pop(), and remove() for lists?

Example:

nums = [1, 2, 3]

del nums[1]  # Deletes element at index 1
nums.pop(1)  # Removes and returns element at index 1
nums.remove(3)  # Removes first occurrence of 3

✅ Use del for deletion, pop() when you need the removed element, and remove() when you know the value.

18. What are Python metaclasses?

• Metaclasses control how classes are created (like classes define objects).
• Every class in Python is an instance of a metaclass.
• The default metaclass is type.

Example:

class Meta(type):
    def __new__(cls, name, bases, dct):
        print(f"Creating class {name}")
        return super().__new__(cls, name, bases, dct)

class MyClass(metaclass=Meta):
    pass  # Automatically triggers Meta.__new__()

✅ Metaclasses are useful for enforcing rules and modifying class behavior.

19. What is the purpose of Python’s __call__ method?

• Makes an object behave like a function when called.
• Defined in classes using __call__().

Example:

class Counter:
    def __init__(self):
        self.count = 0

    def __call__(self):
        self.count += 1
        return self.count

c = Counter()
print(c())  # Output: 1
print(c())  # Output: 2

✅ __call__ is useful for caching, function objects, and decorators.

20. How do you implement caching in Python?

• Use functools.lru_cache() for caching function results.
• Improves performance by storing previously computed values.

Example:

from functools import lru_cache

@lru_cache(maxsize=5)
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

print(fibonacci(10))  # Computed once, then cached

✅ Use caching to speed up expensive or frequently called functions.

Advanced-Level Python Job Interview Questions

1. Explain the difference between multiprocessing and threading in Python.

Example:

import multiprocessing
import threading

def task():
    print("Task executed")

# Using multiprocessing
p = multiprocessing.Process(target=task)
p.start()

# Using threading
t = threading.Thread(target=task)
t.start()

✅ Use multiprocessing for CPU-heavy tasks and threading for I/O-heavy tasks.

2. How do you implement an LRU (Least Recently Used) cache in Python?

• LRU Cache stores recently accessed items and removes the least recently used ones when full.
• functools.lru_cache() automatically implements LRU caching.

Example:

from functools import lru_cache

@lru_cache(maxsize=5)  # Stores up to 5 results
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

print(fibonacci(10))  # Uses cached values for efficiency

✅ LRU caching improves performance by avoiding redundant computations.

3. What are coroutines in Python, and how do they differ from generators?

Example:

Generator:

def gen():
    yield 1
    yield 2

g = gen()
print(next(g))  # Output: 1
print(next(g))  # Output: 2

Coroutine:

import asyncio

async def greet():
    print("Hello")
    await asyncio.sleep(1)
    print("World")

asyncio.run(greet())

✅ Use coroutines (async/await) for asynchronous programming and generators (yield) for iteration.

4. What is monkey patching in Python?

• Monkey patching modifies code at runtime.
• Usually used for debugging or modifying third-party libraries.

Example:

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

def new_greet(self):
    return "Patched Hello"

Sample.greet = new_greet  # Monkey patch
print(Sample().greet())  # Output: "Patched Hello"

✅ Avoid monkey patching unless necessary, as it can introduce unexpected behavior.

5. How do you implement memoization in Python?

• Memoization stores function results to avoid redundant calculations.
• The best way is using functools.lru_cache().

Example:

from functools import lru_cache

@lru_cache(maxsize=None)
def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n-1)

print(factorial(5))  # Computed once, then cached

✅ Memoization speeds up recursive functions like Fibonacci and factorial calculations.

6. Explain the use of Python’s asyncio module.

• asyncio enables asynchronous programming using coroutines (async/await).
• Used for I/O-bound tasks like network requests, file operations, and database queries.

Example:

import asyncio

async def task():
    print("Start")
    await asyncio.sleep(2)  # Non-blocking
    print("End")

asyncio.run(task())

✅ Use asyncio for tasks that wait (e.g., API calls, databases) to improve efficiency.

7. What is the difference between synchronous and asynchronous programming in Python?

Example:

Synchronous:

import time

def task():
    time.sleep(2)
    print("Done")

task()
print("After Task")  # Runs after task completes

Asynchronous:

import asyncio

async def task():
    await asyncio.sleep(2)
    print("Done")

asyncio.run(task())  # Runs without blocking
print("After Task")  # Runs immediately

✅ Use async programming for network/database operations to improve responsiveness.

8. How does Python’s super() function work with multiple inheritance?

• super() calls methods from parent classes in method resolution order (MRO).
• Useful in multiple inheritance to avoid calling the same method multiple times.

Example:

class A:
    def show(self):
        print("A")

class B(A):
    def show(self):
        super().show()  # Calls A's method
        print("B")

class C(B):
    def show(self):
        super().show()  # Calls B's method
        print("C")

obj = C()
obj.show()

✅ Use super() to avoid redundancy in inheritance and maintain method order.

9. How do you manage memory leaks in Python?

✅ Ways to prevent memory leaks:

1. Use Weak References (weakref module) to avoid cyclic references.
2. Close file handlers and database connections properly.
3. Use del to remove unnecessary objects.
4. Use gc.collect() to manually trigger garbage collection.
5. Monitor memory with objgraph or memory_profiler.

Example:

import gc

class A:
    def __del__(self):
        print("Object deleted")

a = A()
del a  # Manually deleting object
gc.collect()  # Force garbage collection

✅ Managing memory prevents performance issues and resource exhaustion.

10. How do you optimize a large-scale Python application for efficiency and performance?

✅ Best practices for large-scale Python optimization:

1. Use Generators to save memory. def large_data(): yield from range(10**6) # Uses less memory
2. Use Multiprocessing for CPU-bound tasks.
3. Use Async Programming for I/O-bound tasks.
4. Profile Code with cProfile to find bottlenecks. import cProfile cProfile.run('your_function()')
5. Use Cython or NumPy for heavy computations.
6. Avoid global variables to prevent memory leaks.
7. Cache expensive computations with lru_cache().
8. Use efficient data structures (set, deque, dict instead of lists).

✅ Optimizing code ensures better performance and scalability in large applications.