Python Interview Questions and Answers

If you are preparing for a Python interview, you might be having a mix of excitement and uncertainty. It is common among many individuals. They often wonder what kind of questions will be asked, how deep your knowledge should be and what topics you should focus on first. I had appeared in many interviews and one thing I've clearly learned is this: preparing with the right strategy can truly make a huge difference in how you perform and how confident you feel.

Python interviews are not just about syntax or remembering definitions, it is about how well you understand the core concepts, how you handle problem-solving approach, and how confidently you can explain your logic. Basically, it evaluates your knowledge and technical skills. You just have to keep your mind clear while answering questions.

That is exactly why this guide is created. In this article, we will walk through the most commonly asked Python interview questions, covering everything from beginner to advanced level. No matter if you are a fresher just starting out or an experienced professional looking to brush up on your skills, this guide will help you build clarity, confidence and a strong foundation to crack your next Python interview.

Explore the most asked Python interview questions and answers along with MCQs on topics like KeyError, Scope, Function, and more to ace your next interview.

Basic Python Interview Questions for Beginners

1. What is Python?

Python is an object-oriented computer programming language. It is preferred in the development of different applications, websites, games and software. This programming language also performs complicated calculations.

2. Explain the benefits of using Python.

Listed below are some important Python features and benefits.

• An extensive set of libraries

• Open-source & large community

• Data science & web development capabilities

• Interpreted language

• Rapid development and faster prototyping
  

3. What does a dynamically typed language mean?

Dynamically typed languages are ones that perform type checking during runtime instead of at compile time. Some common examples of these languages are Python, JavaScript, PHP, Ruby, etc.

4. What is PEP 8? Why is it important?

PEP refers to the Python Enhancement Proposal, which is an official design document that delivers information to the Python community. It contains information on new features and processes of this language. PEP 8 is important because it documents all the style guidelines for this code.

5. What is the use of 'self' in Python?

In this programming language, basically self represents the instance of the class. This keyword is used to access the methods and attributes, while binding the attributes with presented arguments.

6. What is an interpreted language?

Interpreted languages are ones that execute their statements line by line. PHP, JavaScript, Ruby, and R programming are top examples. A program written in an interpreted language will directly run from the source code without any intermediary compilation step.

7. What is the difference between a Dictionary and a Set?

A dictionary is a key–value data structure that maps unique keys to values. From Python 3.7 onward, dictionaries preserve insertion order. Set refers to an unordered collection of data types. It is mutable, iterable, and does not have any duplicate elements.

8. What do you understand about Python literals?

Python literals are fixed values written directly in source code that do not change during program execution. They represent raw data assigned to variables or constants and are the basic building blocks used to define data without requiring computation or function calls.

Example:

# Numeric literals
a = 10
b = 3.5

# String literal
name = "igmGuru"

# Boolean literal
is_active = True

# Special literal
x = None

# Collection literals
my_list = [1, 2, 3]
my_dict = {"key": "value"}

print(a, b, name, is_active, x, my_list, my_dict)

Output:

10 3.5 igmGuru True None [1, 2, 3] {'key': 'value'}

9. Does Python require indentation?

Indentation is indeed required in this programming language. It defines the block and structure of the program. This replaces the requirements for curly braces like in other languages.

10. How is memory managed while working with Python?

Memory management in this programming language is managed automatically by the interpreter. It uses a private heap space to store all objects and data structures. The memory manager takes care of the allocation and deallocation of memory. It also uses garbage collection to remove unused objects and free up memory. It mainly uses reference counting to track object usage and when the reference count becomes zero, the memory is released automatically.

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Python Interview Questions and Answers for Intermediates

11. What is exception handling in Python? How is it done?

Exception handling is a method to handle exceptions and errors that disrupt the execution of a program. It is a fundamental practice of this programming language to check if applications are robust and reliable. It is performed with three steps including try, except and finally. These steps catch exceptions and manage them accordingly.

Example:

try:
    a = 10
    b = 0
    result = a / b   # This will cause an exception
    print(result)

except ZeroDivisionError:
    print("Cannot divide by zero")

else:
    print("Division successful")

finally:
    print("Execution completed")

Output:

Cannot divide by zero
Execution completed

12. What do you understand about the swapcase function in Python?

It is a function related to a string that transforms all lowercase characters into uppercase and vice versa. The main purpose of using this function is to customize an existing instance of string. This method makes a copy of the string that includes all the characters from the swap case. For instance -

string = "igmGuru"
string.swapcase()  --->  "IGMgURU"

13. What is Scope in Python?

Scope is the destination of a program where a variable, function or class can be accessed and written. There are four types of scopes available in this coding language including local variables, global variables, module-level scope and outermost scope.

Example:

x = "global"

def outer():
    x = "enclosing"
    
    def inner():
        x = "local"
        print("Inside inner:", x)
    
    inner()
    print("Inside outer:", x)

outer()
print("Outside function:", x)

Output:

Inside inner: local
Inside outer: enclosing
Outside function: global

14. How to manage KeyError in Python?

KeyError occurs when a developer tries to access a key that does not exist in a dictionary. It is because this coding language expects that each key mentioned by a developer exists in the dictionary. There are many options to remove this error including .get() method, except block and try block.

Example:

data = {"name": "igmGuru", "age": 5}

# Using try-except
try:
    print(data["city"])   # Key does not exist
except KeyError:
    print("Key not found")

# Using get() method (safe way)
print(data.get("city", "Default Value"))

Output:

Key not found
Default Value

15. What is the 'with' statement designed for?

The with statement is used for resource management via context managers. It ensures proper acquisition and release of resources such as files, locks, or network connections.

16. What do you understand about pass in Python?

Pass is a placeholder statement used when a statement is syntactically required but no action is needed. It helps define empty loops, functions, or classes.

17. What is _ _init_ _?

_ _init_ _ refers to a constructor method, which is called automatically to allocate memory as a new instance/ object gets created. The _ _init_ _ method is associated with all Python classes as it aids them in distinguishing attributes and methods of a class from all local variables.

# Class definition
class Student:
    def __init__(self, fname, lname, age, section):
        self.firstname = fname
        self.lastname = lname
        self.age = age
        self.section = section

# Creating a new object
stu1 = Student("Sara", "Ansh", 22, "A2")

Output-

print(stu1.firstname)   # Output: Sara
print(stu1.lastname)    # Output: Ansh
print(stu1.age)         # Output: 22
print(stu1.section)     # Output: A2

Also Read - Python Tutorial

18. What is data smoothing? How is it done?

In Python, data smoothing is commonly performed using techniques such as moving averages, exponential smoothing, or rolling window functions provided by libraries like Pandas and NumPy.

19. What is the difference between the '==' and 'is' operators in Python?

The == operator is used for comparing the equality of objects, while 'is' is used for finding out whether different variables point towards the same object in memory.

Example:

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

print(a == b)  # True, because the values are equal
print(a is b)  # False, because they are different objects in memory

Output:

True
False

20. How to easily remove duplicates from a Python list?

The easiest way to remove duplicates from a list is by converting it into a set. This is because sets do not consist of any duplicate data. Once done, you can reconvert it into a list.

list1 = [3, 6, 7, 9, 2, 3, 7, 1]
list2 = list(set(list1))
print(list2)

The result for list2 will contain [1, 2, 3, 6, 7, 9]. However, it is not compulsory that the list order would stay untouched.

Read Also -  Top Reasons To Learn Python

Python Interview Questions and Answers for Experienced Professionals

21. What do you understand by Scope Resolution?

There are many times when a few objects that are within the same scope share the same name even though they function differently. This is where scope resolution automatically comes into play.

For instance, modules named 'math' and 'cmath' have plenty of common functions, like exp(), acos(), and log10(). This ambiguity can be solved by adding the respected prefixes along, like math.acos() and cmath.acos().

22. What is the difference between a for loop and a while loop in Python?

The for loop is usually used for iterating across the elements of various collection types. These types include Tuples, Lists, Sets and Dictionaries. It is used for both start and end conditions. While loop is the genuine looping feature and used in different programming languages. Here it is used for end conditions only.

Example of for Loop:

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

Output-

0
1
2
3
4

Example of while Loop:

count = 0
while count < 5:
    print(count)
    count += 1

Output

0
1
2
3
4

23. Is it possible to pass a function as an argument in Python?

Yes, many functions are passed as arguments in this programming language. Objects, variables and functions are some of the instances.

24. Explain *args and **kwargs.

*args and **kwargs are basically special syntaxes that are used to pass arguments to a function. Both of these send a variable-length argument list to function. The *args is responsible for passing a non-keyworded variable-length argument list.

25. What will be the process to check if all characters from a string are alphanumeric?

There is a method to check if all the elements of a string are alphanumeric. It is called the isalnum() method. If the string has an alphanumeric element it will result in True, otherwise False.

string = "Hello123"

if string.isalnum():
    print("All characters are alphanumeric.")
else:
    print("Not all characters are alphanumeric.")

Output:

All characters are alphanumeric.

26. How to merge elements from a Sequence?

join() method is used to merge or concatenate elements from a sequence. This method focuses on all characters of the sequence with a pre-defined separator.

words = ['Hello', 'World', 'igmGuru']
merged_string = ' '.join(words)
print(merged_string)

Output

Hello World igmGuru

27. What do Generators mean in Python? (Most Asked Python Interview Questions)

Generators refer to functions that can return an iterable accumulation of items in a set manner, one at a time. Generally speaking, they are employed for creating iterators with distinct approaches. They are created using functions with the yield keyword.

Here is a program to help you build a generator for Fibonacci numbers:

## generate fibonacci numbers up to n
def fib(n):
    p, q = 0, 1
    while (p < n):
        yield p
        p, q = q, p + q

x = fib(10)  # create generator object

## iterating using __next__(), for Python2, use next()
x.__next__()  # output => 0
x.__next__()  # output => 1
x.__next__()  # output => 1
x.__next__()  # output => 2
x.__next__()  # output => 3
x.__next__()  # output => 5
x.__next__()  # output => 8
x.__next__()  # error

## iterating using loop
for i in fib(10):
    print(i)  # output => 0 1 1 2 3 5 8

28. How do arguments get passed by value and by reference in Python?

Python does not use pass-by-value or pass-by-reference. Instead, it follows pass-by-object-reference (also called call-by-sharing). Mutable objects can be modified inside a function, while immutable objects cannot.

def appendNumber(arr):
    arr.append(4)

arr = [1, 2, 3]
print(arr)  # Output: => [1, 2, 3]

appendNumber(arr)
print(arr)  # Output: => [1, 2, 3, 4]

29. What is monkey patching in Python? (Most Asked Python Interview Questions)

Monkey patching in Python is a code that modifies or further extends other code at runtime. It is usually done at the startup.

class igmguru:
    def function(self):
        print("function()")

def monkey_function(self):
    print("monkey_function()")

igmguru.function = monkey_function

obj = igmguru()
obj.function()

Output:

monkey_function()

30. Which code can be used to display the current time?

Use this code:

import time

current_time = time.strftime("%H:%M:%S", time.localtime())
print("Current time is", current_time)

Output:

Current time is 15:47:32

Python Coding Interview Questions and Answers

31. Write a program to print this pattern.

The program is:

def alphapat(n):
    num = 65  # ASCII value of 'A'
    for i in range(n):  # Loop for rows
        for j in range(i + 1):  # Loop for columns
            print(chr(num), end="  ")  # Print character with double space
        num += 1  # Move to the next character
        print()  # Move to the next line

n = 5
alphapat(n)

32. Write a program to print this pattern.

The program is:

def myfunc(n):
    k = n - 1
    for i in range(0, n):
        for j in range(0, k):
            print(end=" ")
        k = k - 1
        for j in range(0, i + 1):
            print("* ", end="")
        print("\r")

n = 5
myfunc(n)

33. How can you find the GCD of two numbers?

We can use this program to find the GCD of two numbers:

def gcd(a, b):
    if (a == 0):
        return b
    if (b == 0):
        return a
    if (a == b):
        return a
    if (a > b):
        return gcd(a - b, b)
    return gcd(a, b - a)

a = 98
b = 56

if (gcd(a, b)):
    print('GCD of', a, 'and', b, 'is', gcd(a, b))
else:
    print('not found')

In real-world Python applications, the preferred and more efficient approach is to use the built-in math.gcd() function.

34. How can we check if the provided strings are anagrams or not?

We can find it with the given program:

def check(s1, s2):
    if (sorted(s1) == sorted(s2)):
        print("The strings are anagrams.")
    else:
        print("The strings aren't anagrams.")

s1 = input("Enter string1: ")
# input1: "listen"

s2 = input("Enter string2: ")
# input2: "silent"

check(s1, s2)
# Output: The strings are anagrams.

Output:

Enter string1: listen
Enter string2: silent
The strings are anagrams.

35. Is it possible to find whether the given number is negative or positive?

Yes, it is possible by using the right program.

num = float(input("Enter a number: "))
# Input: 1.2

if num > 0:
    print("Positive number")
elif num == 0:
    print("Zero")
else:
    print("Negative number")

Output:

Enter a number: 1.2
Positive number

36. Which program can be used to print a right-angled triangle asterisk pattern?

Here's the code:

n = 5
for i in range(1, n + 1):
    for j in range(i):
        print("*", end=" ")
    print()

Output:

*
**
***
****
*****

37. Give a simple example of how a file can be read using Python.

Here's a simple example of file handling in Python (reading file):

file = open('sample.txt', 'r')
print(file.read())
file.close()

Output:

igmGuru is a globally recognized
ed-tech giant

38. Which program can remove duplicates from a list?

The program to remove duplicates from a list is quite simple:

dupl = [1, 1, 0, 0, 1, 0, 2, 0, 3, 2, 2, 4, 4, 2, 3]
dupl = set(dupl)
dupl = list(dupl)
print(dupl)

Output:

[0, 1, 2, 3, 4]

39. Write a function for creating a queue and displaying all the sizes and members of it.

Here is a sample code you can use:

import queue

q = queue.Queue()

for x in range(4):
    q.put(x)

print("Members of the queue:")
y = z = q.qsize()

for n in list(q.queue):
    print(n, end=" ")

print("\nSize of the queue:")
print(q.qsize())

Output:

Members of the queue:
0 1 2 3
Size of the queue:
4

40. How to rename columns in Pandas?

The rename() function can be used to rename columns in Pandas. Any column in a data frame can be renamed with this.

import pandas as pd

# Sample DataFrame
customers = pd.DataFrame({
    'user_id_number': [101, 102, 103],
    'customer_phone': ['1234567890', '0987654321', '1122334455']
})

# Rename columns
customers = customers.rename(
    columns={
        'user_id_number': 'user_id',
        'customer_phone': 'phone'
    }
)

print(customers)

Output-

   user_id        phone
0      101     1234567890
1      102     0987654321
2      103     1122334455

41. Which program can be used to check whether a string is a palindrome or not?

The program to check if a string is a palindrome or not is:

def is_palindrome(string):
    reversed_string = string[::-1]
    return string == reversed_string

# Test the function
word = "madam"

if is_palindrome(word):
    print(f"{word} is a palindrome")
else:
    print(f"{word} is not a palindrome")

Output:

madam is a palindrome

42. How can a string be reversed?

A string can be reversed with this program:

def reverse_string(string):
    return string[::-1]

# Test the function
text = "Hello, World!"
reversed_text = reverse_string(text)
print(reversed_text)

Output -

!dlroW ,olleH

43. How can we check whether a number is prime or not?

We can use this program to check whether a number is prime or not:

def is_prime(number):
    if number < 2:
        return False
    for i in range(2, int(number**0.5) + 1):
        if number % i == 0:
            return False
    return True

# Test the function
num = 17

if is_prime(num):
    print(f"{num} is a prime number")
else:
    print(f"{num} is not a prime number")

Output -

17 is a prime number

44. How can we find the factorial of a number?

This program can be used:

def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n - 1)

# Test the function
number = 5
result = factorial(number)
print(f"The factorial of {number} is {result}")

Output:

The factorial of 5 is 120

45. Which program can be used to count the frequency of every single element in a list?

This program can be used:

def count_frequency(numbers):
    frequency = {}
    for num in numbers:
        if num in frequency:
            frequency[num] += 1
        else:
            frequency[num] = 1
    return frequency

# Test the function
nums = [1, 2, 3, 2, 1, 3, 2, 4, 5, 4]
frequency_count = count_frequency(nums)
print(frequency_count)

Output:

{1: 2, 2: 3, 3: 2, 4: 2, 5: 1}

Python Technical Interview Questions

46. How would you implement a stack using only queues in Python? Explain the approach.

To implement a stack using queues, we can use two queues (q1 and q2). The idea is to make the push() operation costly and the pop() operation efficient.

from collections import deque

class StackUsingQueues:
    def __init__(self):
        self.q1 = deque()
        self.q2 = deque()
    
    def push(self, x):
        self.q2.append(x)
        while self.q1:
            self.q2.append(self.q1.popleft())
        self.q1, self.q2 = self.q2, self.q1

    def pop(self):
        if not self.q1:
            return None
        return self.q1.popleft()

# Example usage
s = StackUsingQueues()
s.push(10)
s.push(20)
s.push(30)

print(s.pop())  # Output: 30
print(s.pop())  # Output: 20

47. Explain how you would detect a cycle in a directed graph using Python.

To detect a cycle in a directed graph, we can use Depth-First Search (DFS) along with a recursion stack to keep track of visited nodes in the current path.

def is_cyclic_util(v, visited, rec_stack, graph):
    visited[v] = True
    rec_stack[v] = True

    for neighbour in graph[v]:
        if not visited[neighbour]:
            if is_cyclic_util(neighbour, visited, rec_stack, graph):
                return True
        elif rec_stack[neighbour]:
            return True

    rec_stack[v] = False
    return False


def is_cyclic(graph, num_vertices):
    visited = [False] * num_vertices
    rec_stack = [False] * num_vertices

    for node in range(num_vertices):
        if not visited[node]:
            if is_cyclic_util(node, visited, rec_stack, graph):
                return True
    return False


# Example usage
graph = {
    0: [1],
    1: [2],
    2: [0],
    3: [4],
    4: []
}

print(is_cyclic(graph, 5))  # Output: True

48. How would you implement a Trie (Prefix Tree) in Python and what is it used for?

A Trie is a tree-like data structure used to efficiently store and search strings, especially for tasks like autocomplete or spell-checking.

class TrieNode:
    def __init__(self):
        self.children = {}
        self.is_end = False


class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, word):
        node = self.root
        for char in word:
            node = node.children.setdefault(char, TrieNode())
        node.is_end = True

    def search(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                return False
            node = node.children[char]
        return node.is_end


# Example usage
trie = Trie()
trie.insert("apple")
trie.insert("app")

print(trie.search("apple"))  # Output: True
print(trie.search("app"))    # Output: True
print(trie.search("appl"))   # Output: False

49. How would you find the majority element in a list using the Boyer-Moore Voting Algorithm?

The Boyer-Moore Voting Algorithm is used to find the majority element in linear time and constant space.

def majority_element(nums):
    count = 0
    candidate = None

    for num in nums:
        if count == 0:
            candidate = num
        count += (1 if num == candidate else -1)

    return candidate


# Example usage
nums = [3, 3, 4, 2, 3, 3, 5]
print(majority_element(nums))  # Output: 3

50. Describe how you would flatten a nested list using recursion in Python.

Flattening a nested list means converting something like [1, [2, [3, 4]], 5] into [1, 2, 3, 4, 5].

def flatten_list(nested):
    flat = []
    for item in nested:
        if isinstance(item, list):
            flat.extend(flatten_list(item))
        else:
            flat.append(item)
    return flat


# Example usage
nested_list = [1, [2, [3, 4]], 5]
print(flatten_list(nested_list))  # Output: [1, 2, 3, 4, 5]

51. What features does Python provide?

The following are the features of this programming language -

• Interpreted language

• Dynamically typed

• Object-oriented programming support

• Extensive libraries

• Platform independence

• Simple and readable syntax

52. What data types are used in Python?

int, float, list, tuple, str, bool, dict and set are the common data types used in Python.

53. What is a Decorator? (Most Asked Python Interview Questions)

A decorator is a function that uses another function to create a new function with some additional functionalities.

54. What are the common uses of Python?

The common usages of this programming language are -

• Data analysis

• Machine learning

• Scripting and utility software

• Building desktop applications

• Developing websites and software.

Related Articles:

• Python for Machine Learning
• Data Analysis with Python
• Python for Machine Learning and AI

55.  What are Python lists, and how are they different from tuples?

Python lists are mutable, ordered collections that allow modifications like adding, removing, or changing elements. They are created using square brackets, e.g., [1, 2, 3].

Tuples, on the other hand, are immutable, meaning their elements cannot be changed after creation, making them faster and memory-efficient. They are created using parentheses, e.g., (1, 2, 3).

56.  What is a Python dictionary? (Most Asked Python Interview Questions)

A Python dictionary is an unordered collection of key-value pairs, where keys are unique.

Example: my_dict = {"name": "Alice", "age": 25, "city": "New York"}.

57. What is a lambda function?

A lambda function is an anonymous (nameless) function that is defined using the lambda keyword. It can have multiple arguments but only one expression, which is evaluated and returned. Its syntax is lambda arguments: expression.

Example-

# Lambda function to add two numbers
add = lambda x, y: x + y

# Using the lambda function
print(add(5, 3))

Output-

8

58. What do you understand about the Python Switch statement?

Python does not support a traditional switch-case statement like C or Java, but Python 3.10 introduced the match-case syntax for structural pattern matching. Instead, Python 3.10 introduced Structural Pattern Matching using the match-case syntax, which provides similar functionality in a more powerful and expressive way.

Example:

def check_day(day):
    if day == 1:
        return "Monday"
    elif day == 2:
        return "Tuesday"
    elif day == 3:
        return "Wednesday"
    else:
        return "Invalid day"

print(check_day(2))

Output:

Tuesday

59. Explain the Walrus Operator in Python.

The Walrus operator is referred to as an assignment expression, which was introduced in Python 3.8. It is denoted by :=. It allows for the assignment of a value to a variable within a larger expression, effectively combining assignment and evaluation into a single step. This function does not support the previous versions (before 3.8). For instance:

numbers = [1, 2, 3, 4, 5]

while (n := len(numbers)) > 0:
    print(numbers.pop())

60. What are unit tests in Python?

Unit tests in Python are a software testing method focused on verifying the correctness of individual, isolated units of source code. Think of them as the smallest testable parts of any application like functions, methods or classes.

It helps to ensure that each unit of code performs as expected and produces the correct output for a given input, independently of other parts of the system. This isolation helps in quickly identifying and fixing bugs early in the development cycle, as well as facilitating code refactoring and maintenance.

Python Developer Interview Questions

61. What is PEP 696 in Python??

PEP 696 introduces default type parameters for generic classes and functions. It allows type variables to have default values, reducing boilerplate while maintaining type safety. This feature improves the usability of generics in type-heavy Python codebases.

Example:

from typing import TypeVar, Generic

T = TypeVar('T', default=int)

class Box(Generic[T]):
    def __init__(self, value: T):
        self.value = value

# Without specifying type
b = Box(42)        # T defaults to int
print(b.value)    # Output: 42

# With explicit type
b2 = Box[str]("hello")  # Overrides default
print(b2.value)        # Output: hello

This reduces boilerplate in generic code and improves type safety by providing sensible defaults while still allowing overrides. It's especially useful in libraries and type-heavy projects.

62. What is the Global Interpreter Lock (GIL)? How does it affect multithreading in CPython?

The Global Interpreter Lock is a mechanism in CPython that ensures only one thread executes Python bytecode at a time. It is primarily used to make memory management safe by protecting internal objects, especially reference counting.

The GIL also causes lack of true parallelism in multithreading in CPython for CPU-bound tasks. This means multiple threads may exist but only one can execute Python code at any given moment.

However, the GIL is released during I/O operations such as file handling or network calls. This allows other threads to run while one thread is waiting, which makes multithreading effective for I/O-bound workloads.

To overcome GIL limitations for CPU-intensive tasks, Python developers typically use multiprocessing, C extensions that release the GIL or asynchronous programming.

63. What is reference counting?

Python primarily uses reference counting to manage memory. Every object here maintains a count of how many references point to it. Whenever a new reference to an object is created, the reference count increases and when a reference is removed, the count decreases. When an object’s reference count reaches zero, it immediately deallocates the memory used by that object.

However, reference counting alone cannot handle circular references, where two or more objects reference each other but are no longer accessible from the program. To address this, this programming language includes an additional garbage collection mechanism.

Example:

import sys

a = [1, 2, 3]

# Get reference count of object
print(sys.getrefcount(a))

# Create another reference
b = a
print(sys.getrefcount(a))

# Delete one reference
del b
print(sys.getrefcount(a))

Output:

2
3
2

64. What is Python bytecode, and how is source code executed in CPython?

Python bytecode is a low-level representation of its source code. When a Python program is executed in CPython, the source code is first parsed and compiled into bytecode, which is a set of instructions understood by the Python Virtual Machine (PVM). The execution process in CPython follows these steps.

• The Python source code is tokenized and converted into an abstract syntax tree.
• This is then compiled into bytecode, which is platform-independent.
• The bytecode is either stored in memory or cached as .pyc files inside the __pycache__ directory to speed up future executions.
• The Python Virtual Machine interprets and executes the bytecode instruction by instruction.
• Each instruction performs a simple operation such as loading values, calling functions, or performing arithmetic.

65. What are common strategies to optimize Python code performance?

Python performance optimization should start with algorithmic improvements, as they provide the biggest impact. Choosing a more efficient algorithm or data structure can reduce time complexity significantly. For example, replacing a nested loop with a hash-based lookup or using built-in data structures like sets and dictionaries for faster access.

Once the algorithm is optimal, micro-optimizations can be applied to fine-tune performance. These include minimizing function calls inside loops, using local variables instead of global lookups, preferring built-in functions and comprehensions over manual loops, and avoiding unnecessary object creation. While micro-optimizations improve speed, their impact is usually small compared to algorithmic changes.

66. What are __slots__ and when should you use them?

__slots__ is a special class attribute of this programming language that restricts the creation of instance attributes to a fixed set of names. Generally, Python stores instance attributes in a dynamic dictionary called __dict__, which offers flexibility but consumes extra memory.

By defining __slots__, Python avoids creating __dict__ for each instance and instead stores attributes in a more compact internal structure. This can significantly reduce memory usage and slightly improve attribute access speed, especially when creating a large number of objects.

__slots__ should be used in scenarios where many instances of a class are created, and the set of attributes is known and fixed, such as in data models or performance-sensitive applications. However, it reduces flexibility because new attributes cannot be added dynamically, and it can complicate inheritance if not designed carefully.

Example:

class Student:
    __slots__ = ['name', 'age']
    
    def __init__(self, name, age):
        self.name = name
        self.age = age

s = Student("igmGuru", 5)

print(s.name)
print(s.age)

# This will raise an error because 'grade' is not in __slots__
# s.grade = "A"

Output:

igmGuru
5

67. Explain memory leaks in Python? What are the common causes, and how to find them?

A memory leak happens when objects that are no longer needed are not released from memory because they are still referenced somewhere in the program. Even though this programming language has automatic memory management, leaks can occur when references prevent the garbage collector from freeing memory. This leads to steadily increasing memory usage in long-running applications.

Common causes of memory leaks include:

• Circular references (especially when objects define __del__ methods)
• Global variables holding references longer than required
• Unbounded data structures like lists or dictionaries that keep growing
• Cached objects without eviction policies, closures or callbacks capturing large objects
• Memory issues in third-party C extensions that do not properly free resources.

Finding them requires using memory profiling and inspection tools. Python’s tracemalloc module helps track memory allocations over time, while the gc module can identify unreachable objects and reference cycles. Tools like objgraph visualize object relationships, and monitoring memory usage in production helps detect abnormal growth patterns.

68. How does the Python import system work?

When a module is imported in Python, the import system first checks whether the module is already loaded in sys.modules. If it is present, Python reuses the existing module and does not reload it.

If the module is not found, Python searches for it in locations defined by sys.meta_path and then along the directories listed in sys.path. These locations include the current working directory, installed packages, and standard library paths.

Once found, Python loads the module, executes its top-level code, and creates a module object. The loaded module object is then cached in sys.modules so that future imports are fast and consistent. This caching behavior ensures that a module is initialized only once per interpreter session.

69. What is the purpose of .pyc files and __pycache__?

.pyc files are compiled bytecode files generated by Python to speed up program startup. When a Python module is imported, CPython first compiles the source code (.py) into bytecode. If this compilation succeeds, the bytecode is stored as a .pyc file.

The __pycache__ directory is used to store these .pyc files in an organized way. Each .pyc file is tagged with the Python version to ensure compatibility between different interpreter versions. On subsequent imports, it checks the timestamp of the source file and, if it has not changed, loads the bytecode directly from the .pyc file instead of recompiling the source.

70. Explain asyncio event loop. How does it work?

The asyncio event loop is the core execution engine of Python’s asynchronous programming model. It is responsible for scheduling, running and coordinating multiple asynchronous tasks within a single thread using non-blocking I/O.

The event loop works by continuously monitoring tasks and I/O operations. When a coroutine performs an await on an I/O operation, it yields control back to the event loop instead of blocking. The loop then switches to another ready task.

Once the I/O operation completes, the event loop resumes the paused coroutine. This cooperative multitasking allows many tasks to make progress efficiently without using multiple threads.

Python Data Engineer Interview Questions

71. What are await, async def, and async for?

async def is used to define a coroutine function. Unlike a normal function, calling an async def function returns a coroutine object that is executed by an event loop. Inside such functions, execution can be paused and resumed without blocking the thread.

await is used inside an async function to pause execution until an awaited asynchronous operation completes. While waiting, control is returned to the event loop so other coroutines can run. This enables efficient non-blocking concurrency.

async for is used to iterate over asynchronous iterators. This allows iteration over data that is produced asynchronously, such as streaming network responses, without blocking the event loop.

72. What is the difference between __str__ and __repr__ methods in Python?

The __str__ method is used to define a readable and user-friendly string representation of an object. It is mainly intended for end users and is automatically called by the print() function.

The __repr__ method, on the other hand, is meant for developers. It returns a detailed and unambiguous representation of the object, ideally one that could recreate the object if passed to eval(). If __str__ is not defined, Python falls back to __repr__.

73. How does Python handle garbage collection?

Python uses automatic memory management based primarily on reference counting. Each object keeps track of how many references point to it, and when this count drops to zero, the memory is released immediately.

However, reference counting alone cannot handle circular references. To solve this, Python includes a cyclic garbage collector that periodically detects groups of objects referencing each other but no longer reachable by the program.

74. What are metaclasses in Python?

Metaclasses are classes that define how other classes behave. Just as a class is a blueprint for objects, a metaclass is a blueprint for classes.

They are rarely used in everyday development but are powerful tools in frameworks, ORMs, and libraries where class creation needs to be controlled, validated, or modified dynamically.

75. How does Python support function overloading?

Python does not support traditional function overloading based on method signatures like some other languages. Defining multiple functions with the same name simply overrides the previous definition.

Instead, Python achieves similar flexibility using default arguments, *args, **kwargs, or conditional logic inside a single function.

76. What is the difference between bytes and bytearray?

Both bytes and bytearray represent sequences of bytes used for handling binary data. The key difference is that bytes objects are immutable, while bytearray objects are mutable.

This makes bytearray more suitable when binary data needs to be modified in place, such as during file processing or network communication.

77. What is the difference between compile-time and runtime errors?

Compile-time errors occur before program execution and usually result from syntax issues. These errors prevent the program from running.

Runtime errors occur while the program is executing. They often arise due to invalid operations such as division by zero or accessing missing keys and are handled using exceptions.

78. What are Python descriptors?

Descriptors are objects that manage attribute access using special methods like __get__(), __set__(), and __delete__().

They form the underlying mechanism for properties, methods, and class variables and are commonly used in advanced object-oriented designs.

79. What is the difference between staticmethod and classmethod?

A static method does not receive any implicit arguments and behaves like a regular function placed inside a class for logical grouping.

A class method receives the class itself as the first argument and is often used to modify or access class-level data or to implement alternative constructors.

80. What is Method Resolution Order (MRO) in Python?

MRO defines the order in which Python searches for methods and attributes in a class hierarchy, especially when multiple inheritance is involved.

Python follows the C3 linearization algorithm to ensure a predictable and consistent lookup order while avoiding ambiguity.

Python Data Analyst Interview Questions

81. Does Python optimize tail recursion?

Tail recursion occurs when a function calls itself as its final action. Although some languages optimize this pattern, Python does not perform tail call optimization.

As a result, deep recursion in Python can still lead to stack overflow errors.

82. What is the difference between None and False?

None represents the absence of a value, while False represents a boolean condition. They are distinct objects and should not be used interchangeably.

83. What is the use of the contextlib module?

The contextlib module simplifies the creation of context managers. It allows developers to create context managers using generator syntax instead of writing full classes.

84. What is lazy evaluation in Python?

Lazy evaluation means values are generated only when they are needed. This helps save memory and improve performance.

Generators are a common example, as they produce items one at a time instead of storing everything in memory.

85. What is the difference between sys.exit() and exit()?

sys.exit() raises a SystemExit exception and is suitable for use in scripts and production code.

exit() is intended mainly for interactive sessions and should be avoided in real-world applications.

86. What is pickling in Python?

Pickling is Python’s process of serializing objects into a byte stream so they can be stored or transferred and later reconstructed.

87. What are Python wheel files?

Wheel files (.whl) are built distributions that allow faster installation of Python packages without compilation.

88. What is the difference between synchronous and asynchronous execution?

Synchronous execution blocks program flow until a task finishes, while asynchronous execution allows other tasks to run while waiting for long operations such as I/O.

89. What is a traceback in Python?

A traceback is a detailed error report that shows the sequence of function calls leading to an exception, helping developers locate the source of errors.

90. Why is logging preferred over print statements?

Logging provides structured, configurable, and severity-based output, making it suitable for debugging and monitoring production applications.

Python for Data Science Interview Questions

91. What is the warnings module used for?

The warnings module displays non-critical alerts about deprecated features or potential issues without stopping program execution.

92. What is the site-packages directory?

site-packages is the directory where third-party Python libraries installed via package managers like pip are stored.

93. What is monkey typing?

Monkey typing refers to dynamically modifying classes or objects at runtime, often to change or extend behavior without altering original source code.

94. What is the difference between os.system() and subprocess?

The subprocess module provides more control, better security, and detailed output handling compared to os.system().

95. What is heap memory in Python?

Heap memory stores dynamically allocated Python objects and is managed automatically by Python’s memory manager.

Python OOPs Interview Questions and Answers

96. What is code profiling?

Code profiling measures execution time and resource usage to identify performance bottlenecks. Tools like cProfile are commonly used.

97. What is the difference between global and nonlocal?

global refers to variables defined at the module level, while nonlocal refers to variables defined in an enclosing function scope.

98. What is the argparse module?

argparse is used to build command-line interfaces by parsing arguments, validating input, and generating help messages.

99. What is the difference between pip and conda?

pip manages Python packages only, while conda manages packages, dependencies, and environments across multiple languages.

100. What is Python’s hash function used for?

The hash function generates hash values used internally by dictionaries and sets to enable fast data lookup.

101. What is a frozenset?

A frozenset is an immutable version of a set. Since it cannot be modified, it can be used as a dictionary key or stored inside another set.

Python Scenario-Based Interview Questions and Answers

The following are some commonly asked Python scenario-based interview questions that help evaluate practical problem-solving and real-world development knowledge.

102. A Python application suddenly becomes slow after handling large datasets. How would you identify and fix the issue?

I would first profile the application using tools like cProfile, timeit or memory_profiler to identify bottlenecks. Then I would optimize inefficient loops, reduce unnecessary object creation and use efficient data structures such as sets or dictionaries. If memory usage is high, I would also check for memory leaks and optimize data loading techniques.

103. Your API is handling thousands of requests, but response times are increasing. What approach would you take in Python?

I would use asynchronous programming with asyncio or frameworks like FastAPI to handle concurrent requests efficiently. Caching, database query optimization and load balancing can also improve performance. For CPU-intensive tasks, multiprocessing can help bypass the GIL limitations.

104. You need to process a very large CSV file that does not fit into memory. How would you handle it?

I would process the file in chunks instead of loading it entirely into memory. Libraries like Pandas support chunk-based reading using the chunksize parameter. Generators can also be used to read and process data line by line efficiently.

105. A multithreaded Python program is not utilizing multiple CPU cores effectively. What could be the reason?

This usually happens because of the Global Interpreter Lock (GIL) in CPython, which allows only one thread to execute Python bytecode at a time. For CPU-bound tasks, I would use multiprocessing instead of multithreading to achieve true parallel execution.

106. Your production application crashes because of unexpected exceptions. How would you improve reliability?

I would implement proper exception handling using try-except blocks, logging mechanisms and centralized error tracking tools. Unit testing and validation checks would also help detect issues early before deployment.

107. A database query in your Python application is taking too much time. How would you optimize it?

I would first analyze the query execution plan and optimize indexes in the database. In Python, I would reduce unnecessary queries, use connection pooling and fetch only the required data. ORM optimizations and caching can further improve performance.

108. You are asked to design a feature that sends emails to millions of users. What strategy would you follow?

I would use asynchronous task queues like Celery with message brokers such as Redis or RabbitMQ. Emails would be processed in batches instead of sending them synchronously to avoid blocking the application and overwhelming the mail server.

109. A Python script works fine locally but fails in production because of dependency conflicts. How would you solve it?

I would use virtual environments and dependency management tools such as pip freeze or Poetry to maintain consistent package versions. Containerization using Docker can also help create identical development and production environments.

110. Your Python application consumes excessive memory over time. How would you investigate it?

I would monitor memory usage using tracemalloc, objgraph or memory_profiler. Then I would check for circular references, large cached objects or global variables retaining unnecessary references. Optimizing object lifecycle management would help reduce memory usage.

111. A real-time application requires handling thousands of concurrent WebSocket connections. Which Python approach would you choose?

I would prefer asynchronous frameworks like FastAPI, aiohttp or Tornado because they efficiently manage non-blocking I/O operations. Async programming allows handling many concurrent connections with lower resource consumption compared to traditional thread-based approaches.

Top 10 Python Multiple Choice Questions (MCQs)

Wrapping Up: Python Interview Questions

As you can see, preparing for a Python interview is not just about memorizing answers; it is about truly understanding the concepts and knowing how to apply them in real scenarios. From basic questions to advanced topics and coding challenges, every section you go through builds your confidence step by step.

The key is to stay consistent with your practice, revise important concepts regularly, and focus on improving your problem-solving approach. Interviews often test how you think, not just what you know. So, take your time to understand the logic behind each question instead of rushing through answers.

Whether you are a fresher starting your journey or an experienced professional aiming for better opportunities, this guide gives you a strong foundation to move forward. Keep practicing, stay curious, and most importantly, believe in your preparation. With the right mindset and strategy, cracking a Python interview becomes much more achievable than it seems.

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FAQs (Python Interview Questions)

Q1. What types of Python interview questions are generally asked in the interviews?

Interviews on the Python programming language involve a variety of questions, ranging from very basics to advanced concepts. A candidate often faces questions on the basis of their expertise level. You can refer to this article, as it lists them all.

Q2. Is it right to start a career with Python?

Of course it is. It is among the easiest yet powerful and demanding programming languages. It can help to get amazing job opportunities with fruitful salary packages.

Q3. What type of job can I go for after learning Python?

Learning python can open various opportunities for you. You can choose any of them based on your areas of expertise and interests. These are:

• Python Developer

• Full Stack Developer

• Data Scientist, Data Analyst or Data Engineer

• Machine Learning Engineer

• Product Manager

• Performance Marketer

Note: If you want to start a career in this field, you can follow a roadmap on how to become a Python developer.

Q4. What basic skills are needed to learn Python?

Basic Python skills include understanding syntax, variables, data types, loops, functions and working with files and libraries.