Top 55 Python Interview Questions and Answers

Top 55 Python Interview Questions and Answers


Python's simplicity and robust library ecosystem make it ideal for diverse applications, enhancing enterprise efficiency and technological integration. This blog offers a detailed compilation of advanced Python interview questions covering fundamentals, error handling, and system design, aimed at preparing readers for technical interviews by testing their understanding of Python through practical and theoretical queries.

Rising demand for python in end-end applications is due to,

  1. Increase in adoption of data analytics by enterprises

  2. Growing dependency on Python over other programming languages

  3. Increase in demand of python for real time Internet of Things 


Python Interview Questions - Infographic

Python Interview Questions

Following are the topics on which Python interviews are conducted, so here are some Python interview questions with answers that might help you know how prepared you are.

Python Fundamentals

Questions may include topics like data types, variables, basic operators, and understanding Python's syntax and semantics.

Q1: Demonstrate the difference between a shallow copy and a deep copy when working with a list of lists in Python. Provide code examples that illustrate potential issues with each method.

# Create a list containing other lists
original = [[1, 2, 3], [4, 5, 6]]

# Shallow copy
shallow_copied = copy.copy(original)
shallow_copied[0][0] = 0

# Deep copy
deep_copied = copy.deepcopy(original)
deep_copied[1][1] = 0

print("Original:", original)  # Original: [[0, 2, 3], [4, 5, 6]], showing that shallow copy affected the original
print("Shallow Copied:", shallow_copied)  # Shallow Copied: [[0, 2, 3], [4, 5, 6]]
print("Deep Copied:", deep_copied)  # Deep Copied: [[1, 2, 3], [4, 0, 6]]

This example demonstrates how changes to a list within the list affect the original list when a shallow copy is used, due to both sharing references to the same inner lists. A deep copy, on the other hand, creates new inner lists, so changes do not affect the original.

Q2: How can cyclic dependencies in imports be problematic in Python? Provide an example and explain a strategy to resolve such issues.

Cyclic dependencies occur when two more modules depend on each other either directly or indirectly, causing an infinite loop and potential ImportError. Here's a simplified example:

from module_b import B
class A:
    def __init__(self):
        self.b = B()
from module_a import A
class B:
    def __init__(self):
        self.a = A()

This structure will lead to an ImportError. A solution is to refactor the import statements:
# In
class B:
    def __init__(self):
        from module_a import A
        self.a = A()

By moving the import statement inside the method or function, it defers the loading of the dependent module until it's needed, breaking the cyclic dependency.

Q3: Write a Python context manager that logs entry and exit from a block of code, demonstrating resource management.

from contextlib import contextmanager
import time

def timed_block(label):
    start_time = time.time()
        end_time = time.time()
        print(f"{label} took {end_time - start_time:.3f} seconds")

# Usage:
with timed_block("Processing"):
    sum = 0
    for i in range(1000000):
        sum += i

Q4: Implement a memoization decorator that caches the results of a function based on its arguments.

def memoize(f):
    memo = {}
    def helper(x):
        if x not in memo:            
            memo[x] = f(x)
        return memo[x]
    return helper

def fib(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
        return fib(n-1) + fib(n-2)
Q5: Explain a common pitfall in Python's asyncio library and how to avoid it. Provide a code example.

One common pitfall is calling an asynchronous function without await, which means the function is never actually executed. This often happens due to a misunderstanding of how coroutines work in Python.

import asyncio

async def do_work():
    print("Work Started")
    await asyncio.sleep(1)  # Simulate I/O task
    print("Work Finished")

async def main():
    # Incorrect call, does nothing

    # Correct call
    await do_work()

Data Structures

Understanding and using Python's built-in data structures like lists, dictionaries, sets, and tuples. Questions might involve operations on these data structures, such as adding or removing elements, iterating, or sorting.

Q6: Implement a function to reverse a singly linked list in Python without using any additional data structures. Describe the algorithm and provide the code.

A: To reverse a singly linked list, you need to change the next pointers of each node so that they point to the previous node. This can be achieved using three pointers: previous, current, and next.

class Node:
    def __init__(self, data): = data = None

def reverse_linked_list(head):
    previous = None
    current = head
    while current:
        next = = previous
        previous = current
        current = next
    return previous

# Example Usage
head = Node(1) = Node(2) = Node(3)
reversed_list = reverse_linked_list(head)
current = reversed_list
while current:
    print(, end=" -> ")
    current =

Q7: Using two stacks, implement a queue that supports all queue operations (enqueue, dequeue) in amortized O(1) time. Explain the mechanism behind your solution and provide the implementation in Python.

The key idea is to use two stacks, stack_in for enqueue operations and stack_out for dequeue operations. When stack_out is empty and a dequeue is required, the contents of stack_in are transferred to stack_out, reversing the order and making the oldest element available.

class QueueWithStacks:
    def __init__(self):
        self.stack_in = []
        self.stack_out = []

    def enqueue(self, x):

    def dequeue(self):
        if not self.stack_out:
            while self.stack_in:
        return self.stack_out.pop()

# Example Usage
q = QueueWithStacks()
print(q.dequeue())  # Output: 1
print(q.dequeue())  # Output: 2

Q8: Write a function to check whether a binary tree is a binary search tree (BST).

A BST is a tree in which each node contains a key greater than all the keys in the node's left subtree and less than those in its right subtree. To check this, perform an in-order traversal and ensure the resulting list of keys is sorted in ascending order.

class TreeNode:
    def __init__(self, x):
        self.val = x
        self.left = None
        self.right = None

def is_bst(node, lower=float('-inf'), upper=float('inf')):
    if not node:
        return True
    val = node.val
    if val <= lower or val >= upper:
        return False
    if not is_bst(node.right, val, upper):
        return False
    if not is_bst(node.left, lower, val):
        return False
    return True

# Example Usage
root = TreeNode(2)
root.left = TreeNode(1)
root.right = TreeNode(3)
print(is_bst(root))  # Output: True

Q9: Write a function to detect a cycle in a linked list. If a cycle exists, return the starting node of the cycle.

Floyd's Cycle Detection Algorithm (also known as the Tortoise and the Hare algorithm) is an efficient method to detect cycles. It uses two pointers moving at different speeds. If there is a cycle, the fast pointer (hare) will eventually meet the slow pointer (tortoise).

class ListNode:
    def __init__(self, x):
        self.val = x = None

def detectCycle(head):
    slow = fast = head
    while fast and
        slow =
        fast =
        if slow == fast:
            while head != slow:
                head =
                slow =
            return head
    return None

# Example Usage
node1 = ListNode(3)
node2 = ListNode(2)
node3 = ListNode(0)
node4 = ListNode(-4) = node2 = node3 = node4 = node2  # Creates a cycle that starts at node2

print(detectCycle(node1).val)  # Output: 2

Q10: Implement an LRU (Least Recently Used) cache that supports the get and put operations.

An LRU cache can be implemented using a combination of a doubly linked list and a hash map. The doubly linked list maintains items in order of usage, with the least recently used items near the tail and the most recently used near the head. The hash map stores keys and pointers to the corresponding nodes in the doubly linked list to ensure O(1) access time.

class LRUCache:
    class Node:
        def __init__(self, key, value):
            self.key = key
            self.value = value
            self.prev = None
   = None

    def __init__(self, capacity):
        self.capacity = capacity
        self.dict = {}
        self.head = self.Node(0, 0)
        self.tail = self.Node(0, 0) = self.tail
        self.tail.prev = self.head

    def get(self, key):
        if key in self.dict:
            n = self.dict[key]
            return n.value
        return -1

    def put(self, key, value):
        if key in self.dict:
        n = self.Node(key, value)
        self.dict[key] = n
        if len(self.dict) > self.capacity:
            n =
            del self.dict[n.key]

    def _remove(self, node):
        p, n = node.prev,, n.prev = n, p

    def _add(self, node):
        p = self.tail.prev = node
        self.tail.prev = node
        node.prev = p = self.tail

# Example Usage
cache = LRUCache(2)
cache.put(1, 1)
cache.put(2, 2)
print(cache.get(1))       # returns 1
cache.put(3, 3)           # evicts key 2
print(cache.get(2))       # returns -1 (not found)

Control Structures

Knowledge of conditional statements (if, elif, else), loops (for, while), and comprehension techniques is mandatory.

Q11: Write a function to find the position of a given value in a sorted 2D matrix where each row and each column is sorted in ascending order. Assume no duplicates. Provide both the position and the Python code.

This problem can be approached by using a staircase search algorithm, starting from the top-right corner and moving based on comparisons.

def search_in_sorted_matrix(matrix, target):
    if not matrix or not matrix[0]:
        return (-1, -1)
    row, col = 0, len(matrix[0]) - 1
    while row < len(matrix) and col >= 0:
        if matrix[row][col] == target:
            return (row, col)
        elif matrix[row][col] > target:
            col -= 1
            row += 1
    return (-1, -1)

# Example matrix and target
matrix = [
    [1, 4, 7, 11],
    [2, 5, 8, 12],
    [3, 6, 9, 16]
target = 8
print(search_in_sorted_matrix(matrix, target))  # Output: (1, 2)

Q12: Create a Python function that finds all unique triplets in an array that sum up to zero. Provide a solution that avoids using excessive brute force.

This can be solved efficiently using a combination of sorting and the two-pointer technique.

def three_sum(nums):
    result = []
    for i in range(len(nums) - 2):
        if i > 0 and nums[i] == nums[i - 1]:
        left, right = i + 1, len(nums) - 1
        while left < right:
            total = nums[i] + nums[left] + nums[right]
            if total < 0:
                left += 1
            elif total > 0:
                right -= 1
                result.append([nums[i], nums[left], nums[right]])
                while left < right and nums[left] == nums[left + 1]:
                    left += 1
                while left < right and nums[right] == nums[right - 1]:
                    right -= 1
                left += 1
                right -= 1
    return result

# Example usage
nums = [-1, 0, 1, 2, -1, -4]
print(three_sum(nums))  # Output: [[-1, -1, 2], [-1, 0, 1]]

Q13: Implement a Python function that returns the first recurring character in a string using a single pass and without additional data structures.

This can be challenging due to the constraints, but can be approached by using bitwise operations to check seen characters, assuming the string only contains letters a-z.

def first_recurring_character(s):
    seen = 0
    for char in s:
        pos = ord(char) - ord('a')
        if seen & (1 << pos):
            return char
        seen |= (1 << pos)
    return None

# Example usage
print(first_recurring_character("abca"))  # Output: 'a'

Q14: Write a function that checks whether a given number is 'interesting' according to the following rules: a number is interesting if it is divisible by 7, or if the sum of its digits is divisible by 7.

The function should handle both conditions and test for divisibility.

def is_interesting(num):
    if num % 7 == 0 or sum(int(digit) for digit in str(num)) % 7 == 0:
        return True
    return False

# Example usage
print(is_interesting(49))  # Output: True (divisible by 7)
print(is_interesting(28))  # Output: True (sum of digits is 10, which is not divisible by 7, but 28 is divisible by 7)

Q15: Create a function that reads integers from a file, converts them to integers, and calculates their sum. If a line contains something that can't be converted to an integer, the function should ignore that line.

This involves reading from a file, handling potential conversion errors, and summing the data.

def sum_integers_in_file(filename):
    total = 0
    with open(filename, 'r') as file:
        for line in file:
                total += int(line)
            except ValueError:
    return total

# Assume a file 'data.txt' with valid and invalid entries
# Example usage

He found Python much easier to learn when compared to other languages. Shubham explored further and learnt different Python tools like Pandas, Numpy, etc. At this stage, he wanted a certification in it. Today, Shubham Patwa, who was a freelance interior designer is a successful Data Scientist at Sutherland.

Functions and Modules

Creating functions, passing arguments, return values, variable scope, lambda functions, importing and using modules, and understanding Python's built-in functions.

Q16: Write a function that dynamically generates and returns other functions which compute the nth power of their input. Explain how closures are utilized in your solution.

def power_generator(n):
    """ Returns a function that calculates the nth power of its input. """
    def nth_power(x):
        return x ** n
    return nth_power

# Example usage
square = power_generator(2)
cube = power_generator(3)
print(square(5))  # Output: 25
print(cube(5))  # Output: 125

This solution uses a closure—a function object that remembers values in enclosing scopes even if they are not present in memory. Here, nth_power remembers the value of n from the enclosing scope power_generator

Q17: Implement a simple dependency injection system for managing dependencies in a Python application. Provide a basic framework that allows for registering dependencies and resolving them.

class DependencyInjector:
    def __init__(self):
        self.dependencies = {}

    def register(self, name, constructor):
        self.dependencies[name] = constructor

    def resolve(self, name):
        return self.dependencies[name]()

# Example usage
di = DependencyInjector()
di.register('config', lambda: {'key': 'value'})
config = di.resolve('config')
print(config)  # Output: {'key': 'value'}

Dependency injection is a technique whereby one object supplies the dependencies of another object. This simple framework allows for registering and resolving dependencies dynamically, promoting a modular architecture.

Q18: Write a decorator that wraps function executions in a try-except block, logs any exceptions, and re-raises them. Allow the decorator to accept an optional list of exceptions to catch.

import logging

def catch_and_log_exceptions(*exceptions):
    def decorator(func):
        def wrapper(*args, **kwargs):
                return func(*args, **kwargs)
            except exceptions as e:
                logging.error(f"Exception in {func.__name__}: {e}")
        return wrapper
    return decorator

def risky_function(x):
    return 10 / x

# Example usage
except Exception as e:
    print("Caught an exception!")  # Output: Caught an exception!

This decorator enhances error handling by allowing specific exceptions to be caught and logged, which is useful in larger applications where error logging and handling are essential.

Q19: Implement a memoization decorator that not only caches function results but also supports a max cache size, evicting the least recently used items first if the cache exceeds this size.

from collections import OrderedDict

def lru_cache(maxsize):
    cache = OrderedDict()
    def decorator(func):
        def wrapper(*args, **kwargs):
            key = args + tuple(kwargs.items())
            if key in cache:
                return cache[key]
            result = func(*args, **kwargs)
            cache[key] = result
            if len(cache) > maxsize:
            return result
        return wrapper
    return decorator

def compute(x):
    return x * x

# Example usage
print(compute(4))  # Output: 16
print(compute(5))  # Output: 25
print(compute(4))  # Output: 16
print(compute(3))  # Output: 9
print(compute(5))  # Cache eviction occurs here, so this will compute again

This decorator implements an LRU (Least Recently Used) cache eviction policy, which is particularly useful in scenarios where memory is limited but some cached results are accessed more frequently than others.

Q20: Design a simple coroutine-based event system where events can be emitted and listeners can be added dynamically to handle these events using Python's asyncio.

import asyncio

class EventSystem:
    def __init__(self):
        self.listeners = {}

    def add_listener(self, event_name, coroutine):
        if event_name not in self.listeners:
            self.listeners[event_name] = []

    async def emit(self, event_name, *args, **kwargs):
        if event_name in self.listeners:
            await asyncio.gather(*(listener(*args, **kwargs) for listener in self.listeners[event_name]))

# Example usage
async def handle_data(x):
    print(f"Handling data: {x}")

event_system = EventSystem()
event_system.add_listener('data_event', handle_data)

async def main():
    await event_system.emit('data_event', 123)

This system uses Python's asyncio library to manage asynchronous events and listeners. It allows for the dynamic addition of event listeners and the emission of events, suitable for real-time data handling and responsive systems.

Object-Oriented Programming (OOP)

Concepts such as classes, objects, inheritance, polymorphism, encapsulation, and methods. Interviewers might ask to design simple classes or to explain how OOP principles can be applied to solve a problem.

Q21: Explain and implement a Python class that uses both class variables and instance variables with method overriding and a class method. How do these interact when inherited in a subclass?

  • Class variables are shared among all instances of a class, making them ideal for attributes common to all objects. Instance variables are unique to each instance and are usually defined in the __init__ method.

  • Method overriding occurs when a subclass has a method with the same name as a method in the parent class. This allows the subclass to implement a behavior specific to it, even if the method in the parent class is invoked using the same interface.

  • A class method affects the class itself and, thus, all instances. It is bound to the class and not the instance of the class.

class Animal:
    kingdom = "Animalia"  # Class variable shared by all instances

    def __init__(self, name): = name  # Instance variable unique to each instance

    def speak(self):
        return f"{} makes a noise."

    def change_kingdom(cls, new_kingdom):
        cls.kingdom = new_kingdom

class Dog(Animal):
    def __init__(self, name, breed):
        self.breed = breed  # Additional instance variable for subclass

    def speak(self):  # Method overriding
        return f"{} barks."

# Using the classes
dog = Dog("Buddy", "Golden Retriever")
print(dog.speak())  # Outputs: Buddy barks.
print(Dog.kingdom)  # Outputs: Animalia
print(Dog.kingdom)  # Outputs: Canidae
print(Animal.kingdom)  # Outputs: Animalia

Q22: Implement an immutable class in Python using __slots__ to reduce memory usage. Explain the choice of using __slots__ and any limitations this introduces.

  • An immutable class is a type of class in Python where instances have fixed data and behavior, meaning their attributes cannot be changed after their creation. Using __slots__ helps reduce memory overhead by preventing the dynamic creation of new attributes and storing attribute names in a tuple rather than a dict.
  • Limitations include the inability to add new attributes beyond those specified in __slots__, and issues with subclassing where subclasses do not inherit the __slots__ but have their own __dict__ unless __slots__ are also defined in the subclass.

class ImmutablePoint:
    __slots__ = ('x', 'y')  # Only x and y attributes allowed

    def __init__(self, x, y):
        self._x = x
        self._y = y

    def x(self):
        return self._x

    def y(self):
        return self._y

    def __str__(self):
        return f"Point({self.x}, {self.y})"

# Using the class
pt = ImmutablePoint(1, 2)
print(pt)  # Outputs: Point(1, 2)
# pt.z = 5  # This would raise an AttributeError due to __slots__

Q23: Describe how the @staticmethod and @classmethod decorators differ in terms of their use with complex method interactions, including method resolution order (MRO) in multiple inheritance scenarios.

  • @staticmethod is used to define methods that neither operate on an instance of the class nor alter the class state. They are utility-type methods that take neither a self nor a cls parameter.

  • @classmethod, however, operates on the class itself and takes a cls parameter that points to the class and not the instance. It can modify class state that applies across all instances of the class, and it follows the Method Resolution Order (MRO), making it sensitive to changes in class hierarchy.

class Base:
    def factory(cls):
        if cls.__name__ == "DerivedA":
            return cls("from A")
        elif cls.__name__ == "DerivedB":
            return cls("from B")
        return cls()

    def __init__(self, msg=None):
        self.msg = msg if msg else "Base"

    def __str__(self):
        return self.msg

class DerivedA(Base):

class DerivedB(Base):

print(str(Base.factory()))  # Outputs: Base
print(str(DerivedA.factory()))  # Outputs: from A
print(str(DerivedB.factory()))  # Outputs: from B

Q24: Explain the concept of metaclasses in Python. How would you use a metaclass to enforce certain constraints on class properties?

  • Metaclasses in Python are classes of classes; they define how a class behaves. A common use for metaclasses is to enforce constraints or modify class attributes during class creation.
  • For instance, a metaclass can be used to ensure that certain methods are implemented in any subclass, or to automatically add new methods or decorators to all methods in the class.

class ValidatorMeta(type):
    def __new__(cls, name, bases, dct):
        # Ensure each class has a validate method
        if 'validate' not in dct:
            raise TypeError(f"{name} must implement a validate method")
        return super().__new__(cls, name, bases, dct)

class Entity(metaclass=ValidatorMeta):
    def validate(self):
        print("Validation logic here")

class Person(Entity):
    def __init__(self, name, age): = name
        self.age = age

# This will work fine
person = Person("John Doe", 30)

# This will raise a TypeError at class definition time
# class Animal(Entity):
#     pass

Q25: Discuss how Python's duck typing philosophy can be used to design highly flexible systems. Provide an example with a function that interacts with different classes.

Duck typing in Python allows for more flexible code designs by not requiring objects to be of a specific type in order for a function to operate on them. If an object has the necessary methods or attributes, it can be used as an argument.

This feature is useful when designing systems that need to operate on a variety of objects, allowing for the implementation of polymorphic behavior without the need for explicit inheritance

class Cat:
    def speak(self):
        return "Meow"

class Dog:
    def speak(self):
        return "Woof"

class Robot:
    def speak(self):
        return "Beep boop"

def make_noise(entity):
    # The function expects any 'entity' to have a 'speak' method.
    # It doesn't care about the class of 'entity'.

# Using the function with different classes
cat = Cat()
dog = Dog()
robot = Robot()

make_noise(cat)   # Outputs: Meow
make_noise(dog)   # Outputs: Woof
make_noise(robot) # Outputs: Beep boop

Exception Handling

Writing robust code with try, except, finally, and raising exceptions. Candidates might be asked to handle specific error types or to debug a piece of code with errors.

Q26: How does Python's exception hierarchy work, and why is it important to catch specific exceptions instead of a general exception?

Python's exceptions are organized in a hierarchy, and they all inherit from the BaseException class. Catching specific exceptions is crucial because it allows your program to handle different error types appropriately and can prevent the program from catching and possibly ignoring unexpected errors that should actually cause the program to fail.

    x = int(input("Enter a number: "))
    y = 1 / x
except ValueError:
    print("That's not a valid number!")
except ZeroDivisionError:
    print("Division by zero!")
except Exception as e:
    print(f"An unexpected error occurred: {e}")

Q27: What is the role of the else clause in try-except blocks, and provide an example of when it's useful?

The else clause in Python's try-except block executes if the try block does not raise an exception. It's useful for code that should run if the try block is successful and should not run if an exception occurs.

    print("Trying to open a file...")
    file = open('file.txt', 'r')
except FileNotFoundError:
    print("File not found.")
    print("File opened successfully.")
    content =

Q28: Explain the use of the finally clause in exception handling. Give an example where it's essential, regardless of an exception being raised or not.

The finally clause is executed after the try and except blocks have completed, regardless of whether an exception was raised or not, and even if an exception is not caught. It's essential for cleanup actions, like closing files or releasing resources.

    file = open('file.txt', 'r')
    data =
except IOError:
    print("An error occurred reading the file.")
    print("Closing the file.")

Q29: Discuss the implications of raising exceptions within a function and how it affects function calls up the stack.

When an exception is raised within a function and it is not handled inside that function, it propagates up the call stack to the caller. If none of the callers handle the exception, it propagates to the top-level of the program, potentially causing the program to crash. This can be used to signal error conditions that the current function is not equipped to handle.

def divide(x, y):
    if y == 0:
        raise ValueError("Cannot divide by zero.")
    return x / y

    result = divide(10, 0)
except ValueError as e:

Q30: How can custom exceptions be defined and used effectively in Python? Provide an example.

Custom exceptions can be defined by subclassing an existing exception class, typically Exception. This is useful for creating meaningful error messages and handling specific error scenarios in a way that's clear and understandable for other developers.

class InsufficientFundsError(Exception):
    def __init__(self, message, balance, amount):
        self.balance = balance
        self.amount = amount

def withdraw(balance, amount):
    if amount > balance:
        raise InsufficientFundsError("Not enough funds available.", balance, amount)
    balance -= amount
    return balance

    current_balance = withdraw(100, 150)
except InsufficientFundsError as e:
    print(f"{e} - Balance: {e.balance}, Withdrawal Amount: {e.amount}")

Learning Python during bench time, he became passionate about the language and transitioned to data science. Today, Sourav Karmakar is now a successful data scientist with Tiger Analytics, drawing almost 6 times the salary he used to get before the transition.

Libraries and Frameworks

Depending on the job role, knowledge of popular libraries like NumPy, Pandas for data analysis, Matplotlib or Seaborn for data visualization, Flask or Django might be assessed.

Q31: What are the key differences between Flask and Django, and how would you choose one over the other for a new web development project?

  • Flask is a micro-framework primarily aimed at small to medium applications with simpler requirements. It is lightweight and flexible, allowing developers to choose their tools and extensions.

  • Django is a full-stack framework that follows the "batteries-included" philosophy. It includes an ORM, forms, routing, authentication, and more, making it well-suited for larger applications with complex data schemes that benefit from rapid development.

  • Choosing Between Them: If you need a quick, customizable setup and are comfortable managing multiple extensions, Flask might be the better choice. For projects that require a comprehensive solution with less configuration, Django provides a more out-of-the-box setup.

Q32: Explain the event-driven programming model in Twisted, and how it differs from traditional synchronous I/O operations. Provide an example of a simple Twisted server.

Twisted is an event-driven networking engine in Python. Unlike synchronous I/O, where the execution blocks or waits for the operation to complete, Twisted's event-driven architecture allows it to perform non-blocking operations. It uses callbacks to respond to events, which enables handling multiple connections simultaneously.

from twisted.internet import protocol, reactor
class Echo(protocol.Protocol):
    def dataReceived(self, data):

class EchoFactory(protocol.Factory):
    def buildProtocol(self, addr):
        return Echo()
reactor.listenTCP(8000, EchoFactory())

In this example, an Echo server is created that simply sends back whatever data it receives. The server listens on TCP port 8000.

Q33: Discuss how Pandas and NumPy can be used together in data analysis. Give an example where both libraries are essential.

import pandas as pd
import numpy as np

# Creating a DataFrame with datetime index
ts = pd.date_range('2020-01-01', periods=100)
data = pd.DataFrame(np.random.randn(100, 1), index=ts, columns=['Value'])

# Using NumPy to perform a Fast Fourier Transform (FFT)
fft_values = np.fft.fft(data['Value'])


Q34: How does asyncio support concurrent programming in Python, and what are the limitations of using asyncio in a multi-threaded environment?

asyncio is a library to write concurrent code using the async/await syntax. It is used primarily for asynchronous I/O operations and enables single-threaded concurrent IO execution via coroutines.

  • asyncio is designed to be used in a single-threaded, single-process environment; using it with threads can lead to complex synchronization issues.

  • It does not inherently improve performance for CPU-bound tasks, which are better handled by multi-processing or threading.

Q35: llustrate how scikit-learn can be used to implement a machine learning pipeline, and discuss the role of pipelining in ML workflows.

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

data = load_iris()
X, y =,

# Create a pipeline
pipeline = make_pipeline(StandardScaler(), LogisticRegression()), y)  # Fit the pipeline
print(pipeline.score(X, y))  # Print the accuracy of the model

File Handling

Reading from and writing to files, understanding different file formats (like JSON, CSV), and working with file paths.

Q36: How can you handle large files in Python without loading the entire file into memory? Provide an example of processing such a file line-by-line.

To handle large files efficiently, you can read the file line-by-line using a loop. This approach ensures that only a small part of the file is in memory at any time.

def process_large_file(file_path):
    with open(file_path, 'r') as file:
        for line in file:
            # Process each line
            print(line.strip())  # Example processing: stripping newline characters

# Call the function with the path to a large file

Q37: Describe how to read and write binary data in Python. What are some use cases for binary file handling?

Binary data can be read and written by opening a file in binary mode ('rb' for reading binary, 'wb' for writing binary). Use cases for binary file handling include working with images, videos, and other media files, or when dealing with data formats that are not text-based (like serialized objects).

def write_binary_data(filepath, data):
    with open(filepath, 'wb') as file:

def read_binary_data(filepath):
    with open(filepath, 'rb') as file:

# Example usage
data = b'This is binary data'
write_binary_data('example.bin', data)
read_data = read_binary_data('example.bin')

Q38: Explain how file seeking works in Python and provide an example demonstrating how to reverse the contents of a file.

File seeking in Python involves moving the file pointer to a specific position in the file. You can use the seek() method, where, whence) sets the file's current position. The whence can be 0 (absolute file positioning), 1 (seek relative to the current position), and 2 (seek relative to the file's end).

def reverse_file_contents(filepath):
    with open(filepath, 'rb+') as file:, 2)  # Move to the end of the file
        position = file.tell()
        for i in range(position - 1, -1, -1):
            byte =

# Reverse contents of a binary file

Q39: How can you ensure that a file is always closed properly, even if an error occurs during processing?

Using the with statement to handle file operations is the best practice. It ensures that the file is properly closed after its suite finishes, even if an error occurs during the file operations.

def read_file_safely(filepath):
        with open(filepath, 'r') as file:
    except IOError as e:
        print(f"An error occurred: {e}")

# Example usage
content = read_file_safely('somefile.txt')

Q40: Discuss how to use Python to handle files in a directory recursively, including reading and processing each file.

To handle files in a directory recursively, you can use the os and os.path modules to traverse directories and subdirectories. Here, you can process each file as needed.

import os

def process_files_recursively(start_path):
    for root, dirs, files in os.walk(start_path):
        for file in files:
            file_path = os.path.join(root, file)
            # Process each file
            print(f"Processing {file_path}")

# Example usage

A. Saravana, who was stuck in the job role of a network engineer and decided to embark on a career change journey and discovered the world of data science. Today, I'm working as a data analyst at First American, a global financial corporation.

System and Network Programming

Topics could include working with the operating system, performing tasks related to file systems, process management, and making network requests.

Q41: How can you use Python to monitor and manipulate network traffic? Discuss the tools and methods involved.

Python can be used to monitor and manipulate network traffic using libraries like scapy or pyshark. These libraries allow for packet crafting, sniffing, and analysis. scapy, for instance, can create custom packets, send them, and capture the responses, making it suitable for security testing and network diagnostics.

from scapy.all import sniff, IP

def monitor_packet(packet):
    if IP in packet:
        print(f"IP: {packet[IP].src} -> {packet[IP].dst}")

# Sniff continuously for IP packets (requires root privileges).
sniff(filter="ip", prn=monitor_packet)

Q42: Discuss how Python can interact with system-level processes and demonstrate how to use the subprocess module to execute a shell command and capture its output.

The subprocess module in Python allows you to spawn new processes, connect to their input/output/error pipes, and obtain their return codes. This module can be used to execute shell commands and interact with other programs at the system level.

import subprocess

command = ["ping", "-c", "4", ""]
result =, stdout=subprocess.PIPE, text=True)

print("Exit Status:", result.returncode)
print("Output:\n", result.stdout)

Q43: Explain how to create a basic TCP server and client in Python using the socket module. Describe the roles of the server and client in establishing a connection.

The socket module in Python provides access to the BSD socket interface. A TCP server listens for connections to a given IP and port, accepts the connection, and communicates with the client. The client connects to the server and sends/receives data.

import socket

server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.bind(('localhost', 9999))

print("Server listening...")
connection, address = server_socket.accept()
print(f"Connected by {address}")

while True:
    data = connection.recv(1024)
    if not data:



import socket

client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.connect(('localhost', 9999))
client_socket.sendall(b'Hello, server')
response = client_socket.recv(1024)
print('Received:', response.decode())


Q44: How can Python be used to implement a secure SSH connection for automating tasks on a remote server? Provide an example using the paramiko library.

Paramiko is a Python library that implements the SSHv2 protocol, providing both client and server functionality. It can be used to automate tasks on remote servers by executing commands securely over SSH.

import paramiko

ssh = paramiko.SSHClient()
ssh.connect('hostname', username='user', password='pass')

stdin, stdout, stderr = ssh.exec_command('ls -l')
for line in stdout:


Q45: Describe how Python can be used to create a service daemon on a Linux system. What are the steps involved in making a Python script run as a daemon?

Creating a service daemon involves writing a Python script that runs in the background, detaches itself from the terminal (using os.fork), and manages its own process group. The script should handle signals appropriately and manage resources efficiently.

import os
import sys
import time
import signal

def handle_signals(signum, frame):
    print("Signal handler called with signal", signum)

def daemonize():
    if os.fork() > 0:
    signal.signal(signal.SIGTERM, handle_signals)
    signal.signal(signal.SIGHUP, handle_signals)

    if os.fork() > 0:

    with open('/dev/null', 'r') as f:
        os.dup2(f.fileno(), sys.stdin.fileno())
    with open('/dev/null', 'w') as f:
        os.dup2(f.fileno(), sys.stdout.fileno())
        os.dup2(f.fileno(), sys.stderr.fileno())

    while True:
        # Main daemon process loop

if __name__ == "__main__":

Multithreading and Multiprocessing

Understanding how to perform concurrent execution, the differences between multithreading and multiprocessing, and when to use each.

Q46: How do Python threads interact with the Global Interpreter Lock (GIL), and what implications does this have for CPU-bound and I/O-bound programs?

The Global Interpreter Lock (GIL) in Python ensures that only one thread executes Python bytecode at a time. This means that in CPU-bound programs, multithreading might not lead to performance improvements because the GIL prevents multiple threads from executing simultaneously on multiple CPUs.

However, for I/O-bound programs, where the bottleneck is often waiting for I/O operations, multithreading can improve performance because threads can be switched out while waiting for I/O, allowing other threads to run.

import threading
import time

def cpu_bound_task():
    count = 0
    for i in range(10**7):
        count += i

def io_bound_task():

# CPU-bound tasks won't see much performance improvement
start_time = time.time()
threads = [threading.Thread(target=cpu_bound_task) for _ in range(2)]
for thread in threads:
for thread in threads:
print(f"CPU-bound tasks duration: {time.time() - start_time}")

# I/O-bound tasks will see performance improvement
start_time = time.time()
threads = [threading.Thread(target=io_bound_task) for _ in range(2)]
for thread in threads:
for thread in threads:
print(f"I/O-bound tasks duration: {time.time() - start_time}")

Q47: Explain the difference between threading and multiprocessing in Python. When would you choose one over the other?

threading allows multiple threads to run in the same memory space, while multiprocessing uses separate memory spaces for each process. Use threading for I/O-bound tasks and when tasks can share memory space efficiently. Use multiprocessing for CPU-bound tasks that benefit from multiple CPUs, as it bypasses the GIL and truly parallelizes execution.

import multiprocessing
import threading
import time

def cpu_bound_function(number):
    return sum(i * i for i in range(number))

def calculate_sums(numbers):
    with multiprocessing.Pool() as pool:
        result =, numbers)
    print(f"Sum of squares (multiprocessing): {result}")

numbers = [10**6, 10**7, 10**8]

# Multiprocessing example
start_time = time.time()
print(f"Multiprocessing duration: {time.time() - start_time}")

# Threading example (for comparison, usually not ideal for CPU-bound tasks)
start_time = time.time()
threads = [threading.Thread(target=cpu_bound_function, args=(num,)) for num in numbers]
for thread in threads:
for thread in threads:
print(f"Threading duration: {time.time() - start_time}")

Q48: What is a deadlock in multithreading, and how can you prevent it in Python?

A deadlock occurs when two or more threads each wait for the other to release a resource they need. To prevent deadlocks, ensure that locks are acquired in a consistent order and use timeouts in lock acquisition.

import threading

lock1 = threading.Lock()
lock2 = threading.Lock()

def thread1_routine():
    while True:
        with lock1:
            with lock2:
                print("Thread 1")

def thread2_routine():
    while True:
        with lock2:
            with lock1:
                print("Thread 2")

thread1 = threading.Thread(target=thread1_routine)
thread2 = threading.Thread(target=thread2_routine)


Q49: How can you share state between processes in a multiprocessing environment?

Use multiprocessing module's shared memory objects like Value or Array, or use a Manager to create a shared data structure.

from multiprocessing import Process, Value, Array

def modify(n, a):
    n.value = 3.1415927
    for i in range(len(a)):
        a[i] = -a[i]

if __name__ == '__main__':
    num = Value('d', 0.0)
    arr = Array('i', range(10))

    p = Process(target=modify, args=(num, arr))


Q50: Discuss the use of the concurrent.futures module for both threading and multiprocessing. How does it abstract the complexities of thread and process management?

The concurrent.futures module provides a high-level interface for asynchronously executing callables using threads or processes. It abstracts the management of pools of threads or processes and offers a simple API for submitting tasks and handling their results via futures.

import concurrent.futures
import urllib.request

URLS = ['', '', '', '']

def load_url(url, timeout):
    with urllib.request.urlopen(url, timeout=timeout) as conn:

with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
    for future in concurrent.futures.as_completed(future_to_url):
        url = future_to_url[future]
            data = future.result()
        except Exception as exc:
            print(f'{url} generated an exception: {exc}')
            print(f'{url} is {len(data)} bytes')

Testing and Debugging

This is important to prepare for a Python Interview - Writing unit tests using frameworks like unittest or pytest, debugging techniques, and understanding how to use Python tools to find and fix bugs.

Q51: Explain the use of mock objects in unit testing Python applications. How can you use the unittest.mock library to simulate database operations?

Mock objects are used in unit testing to simulate real objects in controlled ways. The unittest.mock module allows you to replace parts of your system under test with mock objects and make assertions about how they have been used.

from unittest.mock import MagicMock
import unittest

class MyDatabase:
    def connect(self):
    def fetch_data(self):
    def close(self):

class MyTest(unittest.TestCase):
    def test_database_operations(self):
        db = MyDatabase()
        db.connect = MagicMock(name='connect')
        db.fetch_data = MagicMock(name='fetch_data', return_value={'id': 1, 'data': 'Test'})
        db.close = MagicMock(name='close')

        # Simulate operations
        result = db.fetch_data()

        # Assertions
        self.assertEqual(result, {'id': 1, 'data': 'Test'})

if __name__ == '__main__':

Q52: Discuss the integration of automated testing into a continuous integration/continuous deployment (CI/CD) pipeline for Python applications. What tools would you use?

Integrating automated testing in CI/CD pipelines ensures that tests are run automatically whenever changes are made, enhancing software quality. Tools like Jenkins, Travis CI, GitHub Actions, or GitLab CI are commonly used. For Python specifically, pytest for running tests, for measuring code coverage, and flake8 for style and syntax checks are important.

# Example of a simple GitHub Actions workflow for Python applications
name: Python application test

on: [push]

    runs-on: ubuntu-latest

    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
        python-version: '3.8'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install flake8 pytest
        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
    - name: Lint with flake8
      run: |
        # stop the build if there are Python syntax errors or undefined names
        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
    - name: Test with pytest
      run: |

Q53: How can you detect and prevent memory leaks in Python? Describe a tool or methodology.

Memory leaks in Python can often be attributed to circular references or the misuse of global variables. Tools like objgraph, gc (garbage collection module), and memory_profiler can help identify memory leaks.

import gc
import objgraph

def create_leak():
    leaks = []

gc.collect()  # Force garbage collection
print(objgraph.show_most_common_types())  # Show common types of objects in memory

Q54: What is the role of regression testing in software development, and how can you automate regression tests for a Python project?

Regression testing ensures that new code changes do not adversely affect existing functionality. It can be automated using frameworks like pytest or unittest, which can be integrated into development workflows via CI/CD tools.

import unittest

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

class TestAddition(unittest.TestCase):
    def test_addition(self):
        self.assertEqual(add_numbers(3, 4), 7)
        self.assertEqual(add_numbers(-1, 1), 0)
        self.assertEqual(add_numbers(-1, -1), -2)

if __name__ == '__main__':

Q55: Explain the use of conditional breakpoints in debugging Python code. Provide a scenario where this would be particularly useful.

Conditional breakpoints halt execution when a specified condition is met. This is particularly useful in debugging large loops or when dealing with complex data structures where issues occur under specific conditions.

# Example scenario: Debugging when a specific value appears in processing
values = [1, 2, 3, 4, 5, 99, 6, 7, 8, 9, 10]

for i in values:
    # Set a conditional breakpoint here in an IDE to break when i == 99

Why Naming is #1 Skill for Writing Clean Code 🧼🧑‍💻 - DEV Community

Python Best Practices and Design Patterns

Knowledge of writing clean, efficient, and readable code, understanding basic design patterns in Python, and coding principles like DRY (Don't Repeat Yourself) and KISS (Keep It Simple, Stupid).

Here are some best practices tailored for Python developers that focus on clarity, simplicity, and effectiveness:

  1. Follow the Zen of Python: The Zen of Python, accessible via the command import this, provides a set of aphorisms that capture the philosophy of Python. Key principles include "Readability counts," "simple is better than complex," and "There should be one—and preferably only one—obvious way to do it." These guidelines encourage writing code that is easy to read and maintain.

  2. Use Descriptive Naming: Names of variables, functions, classes, and modules should clearly describe what they represent and do. Avoid abbreviations and use nouns for variables and classes, verbs for functions. For example, use calculate_total instead of calc_t.

  3. Follow PEP 8 Style Guide: PEP 8 is Python's official style guide. It includes recommendations on how to format Python code, such as how to name variables (snake_case for functions and variables, CamelCase for classes), how much indentation to use (4 spaces per indentation level), and where to put spaces (a = 1, not a=1).

  4. Write Docstrings and Comments: Documenting Python code is crucial. Write docstrings for all public modules, functions, classes, and methods. Docstrings should explain what the function/class does, its parameters, and what it returns. Comments should be used to explain why certain decisions were made or to clarify complex parts of the code.

  5. Leverage Python's Built-in Functions and Libraries: Python comes with "batteries included," a comprehensive standard library. Use built-in functions and libraries whenever possible instead of reinventing the wheel, as they are optimized and well-tested.

  6. Keep Functions Small and Focused: Each function should have a single responsibility and be relatively small. A good rule of thumb is that a function should fit on your screen without scrolling. If a function is performing multiple tasks, consider breaking it into smaller ones.

  7. Use List Comprehensions and Generator Expressions: Python's list comprehensions and generators provide a readable, efficient, and Pythonic way of generating lists and iterators. They can often make your code more expressive and easier to understand at a glance.

  8. Error Handling with Exceptions: Handle possible errors with try-except blocks rather than letting your program crash. This helps in maintaining the robustness of your applications. Always try to catch specific exceptions rather than a general catch-all exception.

  9. Write Tests: Testing your code is essential. Utilize Python’s unittest or pytest frameworks to write tests. Tests help ensure your code works as expected and make refactoring and maintenance safer and easier.

  10. Refactor Repeated Code: DRY (Don't Repeat Yourself) is a fundamental principle of software development. Avoid code duplication by abstracting repeated code into reusable functions or classes.

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