Data Structures & Algorithms Interview Questions

Idan R. - " read_dir(path: str) -> list[str] returns the full path of all files and sub- directories of a given directory. is_file(path: str) -> bool: returns true if the path points to a regular file. is_dir(path: str) -> bool: returns true if the path points to a directory. read_file(path: str) -> str: reads and returns the content of the file. The algorithm: notice that storing all the file contents' is too space intensive, so we can't read all the files' contents to store and compare with each"See full answer

Gabriele G. - "Using a DFS approach, computing all the distances from typing import List from collections import deque def shortestCellPath(grid: List[List[int]], sr: int, sc: int, tr: int, tc: int) -> int: if sr == tr and sc == tc: return 0 nRows = len(grid) nCols = len(grid[0]) distances = [] stack = deque([(sr, sc, 0)]) visitedSet = set() while stack: nodeR, nodeC, nodeDist = stack.pop() if gridnodeR == 0 or (nodeR, nodeC) in visited"See full answer

Sravanthi M. - "public static char getRepeatingCharacterInGivenString(String str){ char result = '0'; HashSet set = new HashSet(); for(int i=0;i<str.length();i++){ char c = str.charAt(i); if(!set.contains(c)){ set.add(c); } else{ result= c; break; } } return result; }"See full answer

Rohit B. - "class Solution: def missingNumber(self, nums: list[int]) -> int: Sorting approach n = len(nums) s = n*(n+1)//2 r = s - sum(nums) return self.r l = [3,0,1] print(missingNumber(l))"See full answer

Parth S. - "Assuming that trades will have information like trade_type buy or sell trade_price with these tuples, one can iterate over each trade while maintaining a stack which maintains all the open buy trades. If we encounter a sell trade then we pop one element make it a buy/sell pair and calculate the profit/loss for that pair. Moreover, keep adding pair-wise profit/loss to calculate overall profit as we continue iterating over trades. At the end print pairs and their profit/loss along with"See full answer

Reno S. - "func isMatch(text: String, pattern: String) -> Bool { // Convert strings to arrays for easier indexing let s = Array(text.characters) let p = Array(pattern.characters) guard !s.isEmpty && !p.isEmpty else { return true } // Create DP table: dpi represents if s[0...i-1] matches p[0...j-1] var dp = Array(repeating: Array(repeating: false, count: p.count + 1), count: s.count + 1) // Empty pattern matches empty string dp[0]["See full answer

Jalpa S. - "def containSubString(mainString, SubString): s1 = "hello world" # main String s2 = "hello" s3 = "world" s4 = "Nothing" answer1 = containSubString(s1, s2) answer2 = containSubString(s1, s3) answer3 = containSubString(s1, s4) print(answer1 , answer2, answer) "See full answer

Anonymous Owl - "def flatten_dictionary(dictionary): \# return a flattened dictionary - int/string/another dictionary values \# if the key is empty, exclude from the output \# concat using a "." btwn them \# add to res which is { "key.a.b.etc": "value" } \# iterate through the key value pairs \# while there is a key value pair in the value \# continue going through that, until the value is an int/string flatDic = {} flatDicHelper("", dictionary, flatDic) print(flatDic) return flatDic def flatDicHelper(initialKey"See full answer

Areeba M. - "create a queue push all cells with 0 into queue Mark all 1s as unvisited (-1 or large value) Run BFS for each cell, explore 4 directions If neighbor is unvisited:distance = current + 1 push into queue "See full answer

Anonymous Unicorn - "input = [ {"topic": 1, "chapter": 1, "section": 1}, {"topic": 2, "chapter": 2, "section": 1}, {"topic": 3, "chapter": 2, "section": 2}, {"topic": 4, "chapter": 1, "section": 1}, {"topic": 5, "chapter": 1, "section": 1}, {"topic": 6, "chapter": 2, "section": 2}, {"topic": 7, "chapter": 2, "section": 2}, {"topic": 8, "chapter": 2, "section": 3}, ] expected_output = [ {'chapter': 1, 'sections': [ {'section': 1, 'topics': [ {'top"See full answer

Nicholas S. - "Definitely nice to think of this without memorization, but there is a well known algorithm for this problem, which is the Levenshtein Distance. Lev(a,b) = len(a) if len(b) == 0 = len(b) if len(a) == 0 = lev(a[1:], b[1:] if a[0] == b[0] = 1 + min (lev(a, b[1:]), lev(a[1:], b), lev(a[1:], b[1:])) https://en.wikipedia.org/wiki/Levenshtein_distance I'm sure some optimizations could be made with heuristic."See full answer

Jonathan michael J. - "add two strings `"See full answer

Rishabh R. - "sum of continuous subarray and keep checking if arr[i]==arr[j]. if true increase count;"See full answer

Farhan -. - "Sorting is a technique to arrange data in either increasing order or decreasing order, and, the function that implements this functionality is called sort function"See full answer