--- tags: - devops - l2 - flashcard-deck - python-algorithms --- [Portal](../../../../library/portal/index.md) | **Level:** [L2: Operations](../../../../library/portal/levels.md) | **Topics:** [Python Automation](../../../../library/portal/topics.md) | **Domain:** DevOps & Tooling id category difficulty tags question answer source_path python-algo/cbad80a34d45 python-algo easy python, algorithms, hash-set, two-pointer Find all pairs in an array that sum to k Use a set to track complements in O(n) time.\n\ndef two_sum_pairs(arr, k):\n seen = set()\n pairs = []\n for x in arr:\n if k - x in seen:\n pairs.append((k - x, x))\n seen.add(x)\n return pairs\n\nAlternative: sort + two-pointer approach runs O(n log n) but uses O(1) extra space. The hash-set approach is preferred for interviews unless space is constrained. interview-qa-gap-analysis python-algo/00fa0ebde156 python-algo easy python, algorithms, hash-map, string Find the first non-repeating character in a string Use collections.Counter to count frequencies, then iterate to find the first with count 1.\n\nfrom collections import Counter\ndef first_unique(s):\n counts = Counter(s)\n for ch in s:\n if counts[ch] == 1:\n return ch\n return None\n\nTime: O(n), Space: O(k) where k is alphabet size. Two-pass approach — first pass counts, second finds. interview-qa-gap-analysis python-algo/cfe950389277 python-algo medium python, algorithms, array, prefix Product array — compute products of all elements except self Build prefix and suffix product arrays, then multiply.\n\ndef product_except_self(nums):\n n = len(nums)\n result = [1] * n\n prefix = 1\n for i in range(n):\n result[i] = prefix\n prefix *= nums[i]\n suffix = 1\n for i in range(n - 1, -1, -1):\n result[i] *= suffix\n suffix *= nums[i]\n return result\n\nO(n) time, O(1) extra space (output array not counted). No division needed — handles zeros correctly. interview-qa-gap-analysis python-algo/80ca56343568 python-algo medium python, algorithms, linked-list, two-pointer Find the middle element of a linked list in one pass Use slow/fast pointer technique (Floyd's tortoise and hare).\n\ndef find_middle(head):\n slow = fast = head\n while fast and fast.next:\n slow = slow.next\n fast = fast.next.next\n return slow\n\nWhen fast reaches the end, slow is at the middle. O(n) time, O(1) space. For even-length lists, this returns the second middle node. interview-qa-gap-analysis python-algo/fd8df7eef84e python-algo medium python, algorithms, linked-list, two-pointer Detect a cycle in a linked list Floyd's cycle detection: use slow (1 step) and fast (2 step) pointers.\n\ndef has_cycle(head):\n slow = fast = head\n while fast and fast.next:\n slow = slow.next\n fast = fast.next.next\n if slow is fast:\n return True\n return False\n\nTo find the cycle start: when slow == fast, reset one pointer to head and advance both by 1 — they meet at the cycle entry. O(n) time, O(1) space. interview-qa-gap-analysis python-algo/60278b9a309c python-algo medium python, algorithms, graph, bfs, dfs Implement DFS and BFS for a graph DFS uses a stack (or recursion), BFS uses a queue.\n\nfrom collections import deque\n\ndef bfs(graph, start):\n visited, queue = set(), deque([start])\n visited.add(start)\n while queue:\n node = queue.popleft()\n for neighbor in graph[node]:\n if neighbor not in visited:\n visited.add(neighbor)\n queue.append(neighbor)\n return visited\n\ndef dfs(graph, start, visited=None):\n if visited is None: visited = set()\n visited.add(start)\n for neighbor in graph[start]:\n if neighbor not in visited:\n dfs(graph, neighbor, visited)\n return visited\n\nBFS finds shortest path in unweighted graphs. DFS is better for detecting cycles and topological sorting. interview-qa-gap-analysis python-algo/79b461f2a595 python-algo hard python, algorithms, sorting, divide-and-conquer Implement QuickSort Divide-and-conquer: pick a pivot, partition, recurse.\n\ndef quicksort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n mid = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quicksort(left) + mid + quicksort(right)\n\nAverage O(n log n), worst O(n^2) with bad pivot choice. In-place variant uses Lomuto or Hoare partitioning. Python's built-in sorted() uses Timsort (O(n log n) guaranteed). interview-qa-gap-analysis python-algo/a81d84835f3c python-algo hard python, algorithms, trie, data-structure Implement a Trie (prefix tree) A tree where each node represents a character prefix.\n\nclass TrieNode:\n def __init__(self):\n self.children = {}\n self.is_end = False\n\nclass Trie:\n def __init__(self):\n self.root = TrieNode()\n\n def insert(self, word):\n node = self.root\n for ch in word:\n if ch not in node.children:\n node.children[ch] = TrieNode()\n node = node.children[ch]\n node.is_end = True\n\n def search(self, word):\n node = self._find(word)\n return node is not None and node.is_end\n\n def starts_with(self, prefix):\n return self._find(prefix) is not None\n\nUsed for autocomplete, spell-check, IP routing. O(m) per operation where m is word length. interview-qa-gap-analysis python-algo/735bf864eec4 python-algo medium python, algorithms, string, hash-map Check anagram removal count for two strings Count character frequencies and sum absolute differences.\n\nfrom collections import Counter\ndef anagram_removals(s1, s2):\n c1, c2 = Counter(s1), Counter(s2)\n return sum((c1 - c2).values()) + sum((c2 - c1).values())\n\nThis gives the total characters to remove from both strings to make them anagrams. Counter subtraction only keeps positive counts, so we sum both directions. O(n + m) time. interview-qa-gap-analysis python-algo/49a22cba228e python-algo medium python, algorithms, prefix-sum, hash-set Determine if a zero-sum subarray exists Track prefix sums — if a prefix sum repeats, a zero-sum subarray exists.\n\ndef has_zero_sum_subarray(arr):\n prefix_sums = set([0])\n running = 0\n for x in arr:\n running += x\n if running in prefix_sums:\n return True\n prefix_sums.add(running)\n return False\n\nKey insight: if prefix_sum[j] == prefix_sum[i], then sum(arr[i+1:j+1]) == 0. Include 0 in initial set to catch subarrays starting at index 0. O(n) time and space. interview-qa-gap-analysis python-algo/1c28dcb2155d python-algo medium python, algorithms, sorting, greedy Generate the largest number from a list of integers Custom sort: compare concatenated strings.\n\nfrom functools import cmp_to_key\ndef largest_number(nums):\n strs = list(map(str, nums))\n strs.sort(key=cmp_to_key(lambda a, b: (1 if a+b < b+a else -1 if a+b > b+a else 0)))\n result = ''.join(strs)\n return '0' if result[0] == '0' else result\n\nCompare '9'+'34' vs '34'+'9' -> '934' > '349' so 9 comes first. Edge case: all zeros -> return '0'. O(n log n) time. interview-qa-gap-analysis python-algo/190d1d976248 python-algo medium python, algorithms, stack, monotonic Calculate the stock span problem Use a monotonic decreasing stack to find previous greater elements.\n\ndef stock_span(prices):\n spans = []\n stack = [] # (price, index)\n for i, price in enumerate(prices):\n while stack and stack[-1][0] <= price:\n stack.pop()\n span = i + 1 if not stack else i - stack[-1][1]\n spans.append(span)\n stack.append((price, i))\n return spans\n\nThe span for day i is the number of consecutive days before it (including itself) where price was <= price[i]. Classic monotonic stack pattern. O(n) amortized. interview-qa-gap-analysis python-algo/2c12deebea7d python-algo medium python, algorithms, linked-list, data-structure Implement a linked list with insert, find, delete class Node:\n def __init__(self, val, nxt=None):\n self.val = val\n self.next = nxt\n\nclass LinkedList:\n def __init__(self):\n self.head = None\n\n def insert(self, val):\n self.head = Node(val, self.head)\n\n def find(self, val):\n curr = self.head\n while curr:\n if curr.val == val: return curr\n curr = curr.next\n return None\n\n def delete(self, val):\n if not self.head: return\n if self.head.val == val:\n self.head = self.head.next; return\n curr = self.head\n while curr.next:\n if curr.next.val == val:\n curr.next = curr.next.next; return\n curr = curr.next\n\nInsert: O(1) at head. Find/Delete: O(n). Use a sentinel/dummy head node to simplify edge cases. interview-qa-gap-analysis python-algo/438a53129d2d python-algo easy python, algorithms, string Reverse words in a sentence Split, reverse, rejoin.\n\ndef reverse_words(s):\n return ' '.join(s.split()[::-1])\n\nsplit() without args handles multiple spaces. For in-place reversal (interview follow-up): reverse entire string, then reverse each word. Python strings are immutable, so true in-place requires a list of chars. interview-qa-gap-analysis python-algo/cfb8623761f9 python-algo medium python, algorithms, recursion, backtracking Generate all permutations of a string Use backtracking or itertools.permutations.\n\ndef permutations(s):\n if len(s) <= 1:\n return [s]\n result = []\n for i, ch in enumerate(s):\n rest = s[:i] + s[i+1:]\n for perm in permutations(rest):\n result.append(ch + perm)\n return result\n\nOr simply: from itertools import permutations as p; list(p('abc'))\n\nTime: O(n! * n). For deduplication with repeated chars, use a set or sort + skip approach. interview-qa-gap-analysis python-algo/f7079dd1e359 python-algo medium python, algorithms, two-pointer, array Two-pointer technique for sorted array problems Two pointers converging from both ends of a sorted array.\n\ndef two_sum_sorted(arr, target):\n left, right = 0, len(arr) - 1\n while left < right:\n s = arr[left] + arr[right]\n if s == target:\n return (left, right)\n elif s < target:\n left += 1\n else:\n right -= 1\n return None\n\nO(n) time, O(1) space. Works because the array is sorted. Also used for: container with most water, remove duplicates, three-sum (fix one + two-pointer on rest). interview-qa-gap-analysis python-algo/055d28025060 python-algo easy python, algorithms, binary-search, divide-and-conquer Implement binary search Halve the search space each iteration.\n\ndef binary_search(arr, target):\n lo, hi = 0, len(arr) - 1\n while lo <= hi:\n mid = (lo + hi) // 2\n if arr[mid] == target:\n return mid\n elif arr[mid] < target:\n lo = mid + 1\n else:\n hi = mid - 1\n return -1\n\nO(log n) time, O(1) space. Use bisect module in production: bisect.bisect_left(arr, target). interview-qa-gap-analysis python-algo/41d0d44874ce python-algo easy python, algorithms, merge, two-pointer Merge two sorted arrays Two-pointer merge in O(n + m) time.\n\ndef merge_sorted(a, b):\n result = []\n i = j = 0\n while i < len(a) and j < len(b):\n if a[i] <= b[j]:\n result.append(a[i]); i += 1\n else:\n result.append(b[j]); j += 1\n result.extend(a[i:])\n result.extend(b[j:])\n return result\n\nThis is the merge step of merge sort. O(n + m) time and space. In Python, heapq.merge(a, b) does this lazily. interview-qa-gap-analysis python-algo/05e7107353f4 python-algo easy python, algorithms, string, two-pointer Detect palindrome Compare string to its reverse, or use two pointers.\n\ndef is_palindrome(s):\n return s == s[::-1]\n\n# Two-pointer (better for follow-ups):\ndef is_palindrome_tp(s):\n left, right = 0, len(s) - 1\n while left < right:\n if s[left] != s[right]:\n return False\n left += 1; right -= 1\n return True\n\nFor alphanumeric-only: filter with isalnum() and lower() first. Extends to 'valid palindrome II' (remove at most one char). interview-qa-gap-analysis python-algo/f90d36cc0ddb python-algo hard python, algorithms, deque, sliding-window Sliding window maximum Use a monotonic deque to track max in O(n).\n\nfrom collections import deque\ndef max_sliding_window(nums, k):\n dq = deque() # indices of useful elements\n result = []\n for i, num in enumerate(nums):\n while dq and dq[0] < i - k + 1:\n dq.popleft()\n while dq and nums[dq[-1]] <= num:\n dq.pop()\n dq.append(i)\n if i >= k - 1:\n result.append(nums[dq[0]])\n return result\n\nThe deque stores indices in decreasing order of values. Front is always the max for the current window. O(n) total. interview-qa-gap-analysis --- ## Wiki Navigation ### Prerequisites - Python Flashcards *(CLI)* (flashcard_deck, L1) ### Related Content - Perl Flashcards *(CLI)* (flashcard_deck, L1) — Python Automation - [Python Async & Concurrency](../../../../library/topics/python-async-concurrency/index.md) (Topic Pack, L2) — Python Automation - Python Concepts Flashcards *(CLI)* (flashcard_deck, L0) — Python Automation - [Python Debugging](../../../../library/topics/python-debugging/index.md) (Topic Pack, L1) — Python Automation - [Python Drills](../../../../library/drills/python_drills.md) (Drill, L0) — Python Automation - Python Exercises (Quest Ladder) *(CLI)* (Exercise Set, L0) — Python Automation - Python Flashcards *(CLI)* (flashcard_deck, L1) — Python Automation - Python OOP Flashcards *(CLI)* (flashcard_deck, L2) — Python Automation - [Python Packaging](../../../../library/topics/python-packaging/index.md) (Topic Pack, L2) — Python Automation - [Python for Infrastructure](../../../../library/topics/python-infra/index.md) (Topic Pack, L1) — Python Automation