Top 25 Python Coding Interview Questions and How to Solve Them

Contents

    Sure! Here’s a comprehensive, step-by-step guide on how to prepare for and solve the Top 25 Python Coding Interview Questions — designed to maximize your understanding and boost your performance.

    Step 1: Understand the Scope of Python Coding Interview Questions

    What Are These Questions?

    • Typically focus on:

      • Data Structures: Arrays, strings, linked lists, trees, graphs, stacks, queues, heaps.

      • Algorithms: Sorting, searching, recursion, dynamic programming.

      • Python-specific features: List comprehensions, itertools, generators, sets, dictionaries.

      • Problem-solving skills: Algorithmic thinking, optimizing time/space complexity.

    Why Are They Important?

    • Help examiners test both your coding skills and problem-solving capabilities.

    • Commonly asked in FAANG and other tech company interviews.

    Step 2: Setup Your Environment and Tools

    1. Python Version: Use Python 3.x for the latest features.

    2. Code Editor: VSCode, PyCharm, or even an online editor like LeetCode/CodeSignal.

    3. Practice Platforms:

      • LeetCode

      • HackerRank

      • Codewars

    4. Additional Tools: Jupyter Notebook for experimenting.

    Step 3: Master Core Concepts

    Before jumping into problems, make sure you thoroughly grasp the basics:

    • Variables & Data Types (list, dict, set, tuple)

    • Control structures (if, for, while)

    • Functions & Lambda expressions

    • Recursion

    • Object-oriented programming basics (classes, inheritance)

    Step 4: Understand Common Patterns and Techniques

    Many interview questions follow specific problem-solving patterns:

    • Sliding Window

    • Two Pointers

    • Fast & Slow Pointers

    • Divide and Conquer

    • Dynamic Programming (Memoization & Tabulation)

    • Backtracking

    • Bit Manipulation

    Step 5: Top 25 Python Coding Interview Questions and How to Solve Them

    Here’s a curated list of the 25 typical questions with solution strategies and example code snippets.

    1. Two Sum

    Problem: Given an array of integers and a target, find indices of the two numbers that add up to the target.

    How to solve:

    • Use a hashmap/dictionary to store visited numbers and their indices.

    • For each element, check if target – element exists in hashmap.

    python
    def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
    diff = target – num
    if diff in seen:
    return [seen[diff], i]
    seen[num] = i
    return []

    2. Reverse a String

    Problem: Reverse a string without using built-in reverse functions.

    python
    def reverse_string(s):
    return s[::-1]

    Or using a loop:

    python
    def reverse_string(s):
    reversed_s = []
    for char in s:
    reversed_s.insert(0, char)
    return ”.join(reversed_s)

    3. Valid Parentheses

    Problem: Given a string containing ‘(‘, ‘)’, ‘{‘, ‘}’, ‘[‘ and ‘]’, determine if the string is valid (open brackets closed in correct order).

    Solution:

    • Use a stack.

    • Push opening brackets.

    • For closing brackets, check stack top for match.

    python
    def is_valid(s):
    stack = []
    mapping = {‘)’: ‘(‘, ‘}’: ‘{‘, ‘]’: ‘[‘}
    for char in s:
    if char in mapping.values():
    stack.append(char)
    elif char in mapping:
    if not stack or stack.pop() != mapping[char]:
    return False
    else:
    continue
    return not stack

    4. Merge Two Sorted Lists

    Problem: Merge two sorted linked lists and return the sorted list.

    Approach:

    • Use pointers to traverse both lists.

    • Compare and merge nodes accordingly.

    (Assuming ListNode class is defined)

    python
    def merge_two_lists(l1, l2):
    dummy = ListNode(0)
    current = dummy
    while l1 and l2:
    if l1.val < l2.val:
    current.next, l1 = l1, l1.next
    else:
    current.next, l2 = l2, l2.next
    current = current.next
    current.next = l1 or l2
    return dummy.next

    5. Maximum Subarray (Kadane’s Algorithm)

    Problem: Find the contiguous subarray with the largest sum.

    Solution: Track current max and global max while iterating.

    python
    def max_subarray(nums):
    max_current = max_global = nums[0]
    for n in nums[1:]:
    max_current = max(n, max_current + n)
    max_global = max(max_global, max_current)
    return max_global

    6. Climbing Stairs

    Problem: You can climb 1 or 2 steps at a time. How many distinct ways to climb to step n?

    Solution: Fibonacci-like DP.

    python
    def climbstairs(n):
    if n <= 2:
    return n
    a, b = 1, 2
    for     in range(3, n + 1):
    a, b = b, a + b
    return b

    7. Valid Anagram

    Problem: Check if two strings are anagrams.

    Solution:

    • Sort both and compare or

    • Count frequency of characters.

    python
    from collections import Counter

    def is_anagram(s, t):
    return Counter(s) == Counter(t)

    8. Contains Duplicate

    Problem: Check if array contains duplicates.

    python
    def contains_duplicate(nums):
    return len(nums) != len(set(nums))

    9. Symmetric Tree

    Problem: Check if a binary tree is a mirror of itself.

    Approach: Recursively compare left and right subtrees.

    python
    def is_symmetric(root):
    def is_mirror(t1, t2):
    if not t1 and not t2:
    return True
    if not t1 or not t2:
    return False
    return t1.val == t2.val and is_mirror(t1.left, t2.right) and is_mirror(t1.right, t2.left)
    return is_mirror(root, root)

    10. Best Time to Buy and Sell Stock

    Problem: Maximize profit from one buy-sell transaction.

    Solution: Track min price and max profit.

    python
    def max_profit(prices):
    min_price = float(‘inf’)
    max_profit = 0
    for price in prices:
    min_price = min(min_price, price)
    max_profit = max(max_profit, price – min_price)
    return max_profit

    11. Intersection of Two Arrays

    python
    def intersection(nums1, nums2):
    return list(set(nums1) & set(nums2))

    python
    def binary_search(arr, target):
    left, right = 0, len(arr) – 1
    while left <= right:
    mid = (left + right) // 2
    if arr[mid] == target:
    return mid
    elif arr[mid] < target:
    left = mid + 1
    else:
    right = mid – 1
    return -1

    13. FizzBuzz

    python
    def fizz_buzz(n):
    result = []
    for i in range(1, n + 1):
    if i % 15 == 0:
    result.append("FizzBuzz")
    elif i % 3 == 0:
    result.append("Fizz")
    elif i % 5 == 0:
    result.append("Buzz")
    else:
    result.append(str(i))
    return result

    14. Product of Array Except Self

    python
    def product_except_self(nums):
    length = len(nums)
    answer = [1] * length

    left = 1
    
for i in range(length):
    
    answer[i] = left
    
    left *= nums[i]
    right = 1
    
for i in reversed(range(length)):
    
    answer[i] *= right
    
    right *= nums[i]
    return answer

    15. Find the Duplicate Number

    python
    def find_duplicate(nums):
    slow = fast = nums[0]
    while True:
    slow = nums[slow]
    fast = nums[nums[fast]]
    if slow == fast:
    break
    slow = nums[0]
    while slow != fast:
    slow = nums[slow]
    fast = nums[fast]
    return slow

    16. Maximum Depth of Binary Tree

    python
    def max_depth(root):
    if not root:
    return 0
    return 1 + max(max_depth(root.left), max_depth(root.right))

    17. Merge Intervals

    python
    def merge(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = []
    for interval in intervals:
    if not merged or merged[-1][1] < interval[0]:
    merged.append(interval)
    else:
    merged[-1][1] = max(merged[-1][1], interval[1])
    return merged

    18. Number of Islands

    python
    def num_islands(grid):
    if not grid:
    return 0
    count = 0
    rows, cols = len(grid), len(grid[0])

    def dfs(r, c):
    
    if r<0 or c<0 or r>=rows or c>=cols or grid[r][c] == '0':
    
        return
    
    grid[r][c] = '0'  # Mark visited
    
    dfs(r+1, c)
    
    dfs(r-1, c)
    
    dfs(r, c+1)
    
    dfs(r, c-1)
    for r in range(rows):
    
    for c in range(cols):
    
        if grid[r][c] == '1':
    
            count += 1
    
            dfs(r, c)
    
return count

    19. Reverse Linked List

    python
    def reverse_list(head):
    prev = None
    current = head
    while current:
    nxt = current.next
    current.next = prev
    prev = current
    current = nxt
    return prev

    20. House Robber

    python
    def rob(nums):
    prev = curr = 0
    for num in nums:
    prev, curr = curr, max(curr, prev + num)
    return curr

    21. Longest Consecutive Sequence

    python
    def longest_consecutive(nums):
    num_set = set(nums)
    longest = 0
    for num in num_set:
    if num – 1 not in num_set:
    length = 1
    while num + length in num_set:
    length += 1
    longest = max(longest, length)
    return longest

    22. Top K Frequent Elements

    python
    from collections import Counter
    import heapq

    def top_k_frequent(nums, k):
    count = Counter(nums)
    return heapq.nlargest(k, count.keys(), key=count.get)

    23. Number of Connected Components in an Undirected Graph

    Using Union-Find or DFS.

    24. Course Schedule (Detect Cycle in Graph)

    Use DFS to detect cycle in directed graph.

    25. Find Median from Data Stream

    Use two heaps (max heap and min heap) to balance numbers.

    Step 6: Practice Each Question

    • First, understand the problem.

    • Try to solve it on paper without coding.

    • Write your code in your editor.

    • Test with different edge cases.

    • Optimize and analyze time/space complexity.

    Step 7: Analyze Your Solutions

    • Use Big-O notation to discuss efficiency.

    • Look for edge cases:

      • Empty inputs

      • Large inputs

      • Single-element inputs

    • Read others’ solutions on platforms to learn alternate approaches.

    Step 8: Repeat and Reinforce

    • Revisit tough problems multiple times.

    • Implement variations or related problems.

    • Focus on weak areas.

    Step 9: Prepare for Behavioral and System Design Questions

    Remember interviews often combine coding with soft skills and system knowledge.

    Bonus Tips

    • Practice explaining your thought process aloud.

    • Write clean, modular, and well-documented code.

    • Use helpful Python libraries but also know how to implement basics manually.

    • Time yourself for realistic practice.

    Mastering these 25 problems will build a strong foundation in Python programming and problem-solving skills essential for coding interviews. Approach each question with patience, break down the problem, code carefully, and analyze the solution for completeness.

    If you want, I can also provide solutions for any specific question from this list in more detail with explanations! Just ask.

    Updated on June 3, 2025
    Was this article helpful?

    Related Articles

    Leave a Reply

    Your email address will not be published. Required fields are marked *

    Contents