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💪 Interview Prep

Python Coding Challenges – Interview Problems

Technical interviews test algorithmic problem-solving under pressure. Practice these common Python challenges to build pattern recognition.

⏱️ 20 min read🎯 Interview Prep📅 Updated 2026

Two Sum (Hash Map)

Python
# O(n) time, O(n) space
def two_sum(nums: list[int], target: int) -> list[int]:
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

print(two_sum([2, 7, 11, 15], 9))  # [0, 1]

Valid Palindrome (Two Pointers)

Python
def is_palindrome(s: str) -> bool:
    left, right = 0, len(s) - 1
    while left < right:
        while left < right and not s[left].isalnum():
            left += 1
        while left < right and not s[right].isalnum():
            right -= 1
        if s[left].lower() != s[right].lower():
            return False
        left += 1; right -= 1
    return True

print(is_palindrome("A man, a plan, a canal: Panama"))  # True

Fibonacci (Dynamic Programming)

Python
# Bottom-up DP: O(n) time, O(1) space
def fib(n):
    if n <= 1: return n
    a, b = 0, 1
    for _ in range(2, n + 1):
        a, b = b, a + b
    return b

# Memoization with lru_cache
from functools import lru_cache
@lru_cache(maxsize=None)
def fib_memo(n):
    if n <= 1: return n
    return fib_memo(n-1) + fib_memo(n-2)

print(fib(10))  # 55
Python
def binary_search(nums: list[int], target: int) -> int:
    left, right = 0, len(nums) - 1
    while left <= right:
        mid = (left + right) // 2
        if nums[mid] == target:
            return mid
        elif nums[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1

print(binary_search([1, 3, 5, 7, 9], 7))  # 3

Valid Anagram

Python
from collections import Counter

def is_anagram(s: str, t: str) -> bool:
    return Counter(s) == Counter(t)

print(is_anagram("anagram", "nagaram"))  # True
print(is_anagram("rat", "car"))          # False
Tip: Most interview problems follow 14 patterns: sliding window, two pointers, binary search, tree BFS/DFS, dynamic programming. Practice recognizing patterns, not memorizing solutions.

🏋️ Practical Exercise

Reinforce the patterns above:

  1. Re-solve Two Sum without looking, then state its time and space complexity.
  2. Implement Valid Palindrome ignoring case and non-alphanumeric characters.
  3. Write both a recursive and an iterative Fibonacci and compare them.
  4. Implement binary search and test it on a sorted list, including the not-found case.

🔥 Challenge Exercise

Pick three of these problems and, for each, write a clean solution, add edge-case tests (empty input, duplicates, not found), and analyze the Big-O time and space complexity. Then try to improve at least one brute-force solution to a more optimal one (e.g. nested loop → hash map). Bonus: explain your approach out loud as you would in an interview.

📋 Summary

  • These classic problems cover hash maps, two pointers, dynamic programming, and binary search.
  • Two Sum uses a hash map to reach O(n) time instead of O(n²).
  • Two-pointer techniques solve palindrome and array problems efficiently.
  • Memoization/DP turns exponential recursion into linear time.
  • Binary search requires sorted input and runs in O(log n).
  • In interviews, explain your reasoning, handle edge cases, and state complexity.

Interview Questions on Coding Challenges

  • How does the hash-map approach make Two Sum O(n)?
  • What is the two-pointer technique and when is it useful?
  • Why is naive recursive Fibonacci exponential, and how does DP fix it?
  • What are the requirements for binary search to work?
  • How do you check if two strings are anagrams efficiently?
  • How do you analyze the time and space complexity of a solution?
  • How do you talk through a problem in a coding interview?
❓ Interview Questions 🏋️ Coding Exercises 🗂️ Dictionaries 🔁 Recursion 📚 Lists

FAQ

How should I approach a coding challenge in an interview? +

Clarify the problem and constraints, work through a small example, state a brute-force idea, then refine it. Code cleanly while explaining your reasoning, test with edge cases, and finish by analyzing time and space complexity.

Why is the hash-map version of Two Sum faster? +

Instead of checking every pair (O(n²)), you store each number’s complement in a hash map and look it up in O(1). One pass yields O(n) time at the cost of O(n) extra space.

Why is recursive Fibonacci slow? +

It recomputes the same subproblems exponentially many times. Caching results with memoization (or building up iteratively) reduces it to O(n) time.

What does binary search require? +

A sorted sequence. It repeatedly halves the search range, achieving O(log n) time. On unsorted data you must sort first or use a linear scan.

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