Introduction

Python has become the go-to programming language for IT and data science roles in India, with top MNCs like TCS, Infosys, Amazon, and Google prioritizing it for its versatility and ease of use. Whether you’re a fresher aiming for your first IT job or an experienced professional eyeing data science roles, mastering Python coding questions is crucial to stand out in interviews. In August 2025, as India’s IT sector grows at a CAGR of 10.3% and data science roles surge, companies are testing candidates on logical reasoning, problem-solving, and efficient coding. This article dives into the must-know Python coding questions—covering fundamentals, logic-based problems, and function-based operations—to help you ace your next interview and land your dream job.

Why Python Skills Are Critical in 2025

Python’s popularity stems from its applications in web development, data science, AI, and automation, making it a staple in MNC interviews. According to LinkedIn’s 2025 job trends, over 30% of IT job postings in India list Python as a required skill, especially for roles like software developer, data analyst, and machine learning engineer. Interviewers focus not just on correct code but also on your problem-solving approach, code efficiency, and ability to explain your logic. Mastering key Python concepts can set you apart, whether you’re interviewing for a remote role at Amazon or an onsite position at Infosys.

Must-Know Python Coding Questions for Interviews

Below is a curated list of essential Python coding questions, grouped into categories, with sample solutions and tips to impress interviewers. These are tailored for freshers and professionals targeting IT and data science roles in 2025.

1. Python Fundamentals

Why It Matters: Interviewers test your grasp of Python basics to ensure you can handle core programming tasks.

Key Questions:

• Reverse a List: Write a program to reverse a list using slicing and iteration. def reverse_list(lst): return lst[::-1] # Slicing method # Alternative: Iterative method reversed_lst = [] for i in range(len(lst)-1, -1, -1): reversed_lst.append(lst[i]) return reversed_lst print(reverse_list([1, 2, 3, 4])) # Output: [4, 3, 2, 1] Tip: Explain both methods (slicing for simplicity, iteration for clarity) and mention time complexity (O(n) for both).
• List Comprehension: Convert a list of numbers to their squares using list comprehension. numbers = [1, 2, 3, 4] squares = [x**2 for x in numbers] print(squares) # Output: [1, 4, 9, 16] Tip: Highlight list comprehension’s efficiency over loops and its readability.

Pro Tip: Practice explaining syntax like range() and slicing. Interviewers may ask you to write code without running it, so practice on platforms like HackerRank.

2. Logic-Based Problems

Why It Matters: These test your problem-solving skills, crucial for data science and software development roles.

Key Questions:

• Check Palindrome: Write a function to check if a string is a palindrome. def is_palindrome(s): s = s.lower().replace(" ", "") return s == s[::-1] print(is_palindrome("A man a plan a canal Panama")) # Output: True Tip: Discuss preprocessing (e.g., handling spaces, case sensitivity) and time complexity (O(n)).
• Fibonacci Series: Generate the Fibonacci series up to n terms. def fibonacci(n): fib = [0, 1] for i in range(2, n): fib.append(fib[i-1] + fib[i-2]) return fib[:n] print(fibonacci(6)) # Output: [0, 1, 1, 2, 3, 5] Tip: Explain iterative vs. recursive approaches; iterative is more efficient (O(n) vs. O(2^n)).
• Armstrong Number: Check if a number is an Armstrong number (e.g., 153 = 1³ + 5³ + 3³). def is_armstrong(num): digits = str(num) return num == sum(int(d) ** len(digits) for d in digits) print(is_armstrong(153)) # Output: True Tip: Break down the logic step-by-step and discuss edge cases (e.g., single-digit numbers).

3. Essential Operations

Why It Matters: These test your ability to manipulate data, a key skill in IT roles.

Key Questions:

• String-to-Integer Conversion: Convert a string to an integer without using built-in functions. def str_to_int(s): result = 0 for char in s: result = result * 10 + (ord(char) - ord('0')) return result print(str_to_int("123")) # Output: 123 Tip: Explain handling negative numbers or invalid inputs as follow-up questions.
• Unique List: Remove duplicates from a list. def unique_list(lst): return list(dict.fromkeys(lst)) # Preserves order # Alternative: return list(set(lst)) # Faster but unordered print(unique_list([1, 2, 2, 3, 3, 4])) # Output: [1, 2, 3, 4] Tip: Compare set (O(n)) vs. dict.fromkeys for maintaining order.
• Factorial: Calculate factorial using recursion. def factorial(n): if n == 0 or n == 1: return 1 return n * factorial(n-1) print(factorial(5)) # Output: 120 Tip: Discuss iterative vs. recursive approaches and stack overflow risks for large inputs.

4. Function-Based Questions

Why It Matters: Functions like lambda, map, and filter are common in data science for efficient data processing.

Key Questions:

• Lambda Function: Create a lambda function to square numbers in a list. numbers = [1, 2, 3, 4] squares = list(map(lambda x: x**2, numbers)) print(squares) # Output: [1, 4, 9, 16] Tip: Explain lambda as an anonymous function and its use in functional programming.
• Filter Function: Filter even numbers from a list. numbers = [1, 2, 3, 4, 5, 6] evens = list(filter(lambda x: x % 2 == 0, numbers)) print(evens) # Output: [2, 4, 6] Tip: Highlight filter for data cleaning in data science tasks.

5. Quick Checks

Why It Matters: These test your ability to write concise, efficient code for common tasks.

Key Questions:

• Leap Year Check: Determine if a year is a leap year. def is_leap_year(year): return year % 4 == 0 and (year % 100 != 0 or year % 400 == 0) print(is_leap_year(2024)) # Output: True Tip: Explain the logic (divisible by 4, not 100 unless 400).
• Vowel or Consonant: Check if a character is a vowel or consonant. def check_vowel(char): return char.lower() in 'aeiou' print(check_vowel('a')) # Output: True print(check_vowel('b')) # Output: False Tip: Discuss case handling and input validation.

Tips to Ace Python Interviews in 2025

1. Practice Regularly: Use platforms like LeetCode, HackerRank, or CodeChef to solve at least 5 problems daily. Focus on explaining your thought process aloud.
2. Optimize Code: Discuss time and space complexity (e.g., O(n) for list reversal, O(1) for leap year checks). Interviewers value efficiency.
3. Explain Logic: Use the whiteboard approach—break down problems step-by-step, even if coding on paper.
4. Know the Company: Tailor preparation to the company’s tech stack. For example, Amazon emphasizes Python for backend and data roles.
5. Build Projects: Create small projects (e.g., a data visualization tool using Pandas) to showcase practical skills.
6. Mock Interviews: Use platforms like PrepInsta or Naukri Campus for mock Python interviews to simulate real scenarios.

Why These Questions Matter

These questions cover the core skills MNCs test in 2025: syntax mastery, logical reasoning, and functional programming. For example, palindrome and Fibonacci problems assess problem-solving, while lambda and map questions test data manipulation skills critical for data science. According to Glassdoor, 70% of IT interview candidates in India face at least one Python coding question, making preparation essential.

Resources to Boost Your Preparation

• Online Platforms: LeetCode (Python-specific challenges), HackerRank (data science tracks), CodeChef (competitive coding).
• Courses: Free Python courses on Coursera or Udemy; Google’s Python Automation course.
• Books: “Automate the Boring Stuff with Python” for practical applications; “Cracking the Coding Interview” for problem-solving strategies.
• Communities: Join LinkedIn groups or Reddit’s r/learnpython for tips and updates on 2025 interview trends.

Conclusion

Acing a Python interview in August 2025 requires mastering key coding questions, from fundamentals like list comprehension to logic-based problems like palindromes and function-based operations like lambda. By practicing these questions, optimizing your code, and explaining your logic clearly, you can stand out in IT and data science interviews with top MNCs. Start coding daily on LeetCode, build a portfolio, and prepare to showcase your problem-solving skills. Don’t go into any Python interview without these skills—your dream job at companies like TCS, Infosys, or Amazon awaits!