Data Structures Algorithm DSA | Python+Javascript LEETCODE

Practice right-rotating an array by k steps with examples, and emphasize clarifying questions and test cases for edge cases and rotation counts.

Rotate an array by k to the right using brute force and a modulo approach with a constant space reverse method, and analyze time and space complexity.

Demonstrates an in-place rotate algorithm for an array using a helper reverse function, detailing two-part reversal and swap steps, with O(n) time and O(1) space complexity.

Learn how to rotate an array to the right by k in Python, with a concrete example, edge-case handling, and a temp array approach achieving O(n) time and O(k) space.

Learn to rotate an array in Python in place using method 2, reversing segments to achieve right rotation with O(1) space and O(n) time.

Learn to identify two vertical lines in a height array that form a container with the x axis, maximizing the water area.

Explore method 1 brute force for the container with most water, computing the max area by checking pairs and deriving the time complexity as O(n^2) with constant space.

Implement a brute force solution for the max area problem by iterating pairs and calculating height as the minimum of two lines. Update the maximum area as you compare results.

Walk through a brute force solution to find the maximum area in an array, computing height and width, and analyze time complexity o(n^2) and space complexity o(1).

Learn a Python method to compute the maximum area by evaluating all index pairs with nested loops, taking the minimum of two values and multiplying by their index distance.

Use the two-pointer method to maximize the water area in an array, computing area as min(left, right) times width, and move the smaller pointer; achieves O(n) time and O(1) space.

Hash tables store key-value pairs and map keys to array indices with a hashing function, handling collisions via a linked list. Access, insert, and delete run in average constant time.

Examine the brute force approach to finding a pair that adds up to the target value, using nested checks, and analyze its O(n^2) time and O(1) space.

Code the brute-force two-sum solution with nested loops to find two array elements that add to the target, returning their indices or an empty array, and verify with test cases.

Walks through a brute-force two-sum solution by checking pairs to find indices that sum to the target, and notes O(n^2) time with O(1) space.

Walks through a Python two-sum style solution using nested loops to check all pairs for a target, illustrating O(n^2) time and identifying indices 1 and 2.

Develop and implement a two-sum solution using a hash table to find two numbers that add up to the target. Return their indices or empty result when no pair exists.

Walk through an optimal hash-table solution to find a pair that adds to target, tracing a case, updating the hash table, and returning indices while explaining time and space complexity.

Write a Python solution that finds a pair of numbers adding to a target using a dictionary as a hash table, iterating once and returning the two indices.

Explains method and big O analysis for string isomorphy by comparing brute force O(n^2) and a linear-time hash map approach that checks length and mappings.

Learn to implement a check iso morphic function that decides if two strings are iso morphic using hash tables for forward and reverse mappings, with constant space and linear time.

Explore solving Fibonacci with recursion, tracing base cases and two recursive calls, and analyze exponential time and call-stack space; introduce dynamic programming as an optimization.

Master a recursive Fibonacci function with a base case n <= 1 returning n and a recursive step Fibonacci(n-1) + Fibonacci(n-2), verified by console tests up to eight.

Explore a memoized fibonacci implementation using a hash table to store computed values, showing F(n) = F(n-1) + F(n-2) with cache hits and O(n) time and space.

Implement an iterative fibonacci function in Python and JavaScript, using previous and current variables, a next value, and a while loop with a counter; test with sample inputs.

Explains the iterative fibonacci-like solution using previous, current, and next to compute the sequence up to n, in constant space and linear time.

Explains a Python function that computes the Fibonacci series using two variables and a loop, showing time complexity of the order of n and space complexity of one.

Use a recursive function to traverse nested arrays, sum integers, and raise each level's total to the corresponding power, then analyze time and space complexity.

Learn to implement a recursive power-sum function that traverses nested arrays, adds integers, and raises the accumulated sum by increasing powers with depth, validated by multiple examples.

Walk through a recursive solution that sums integers and nested arrays, applying a power parameter to subarrays, and review time and space complexity.

Explore permutations by listing all possible orderings of a distinct integer array, with examples for [1,2,3], [7,8], and edge cases—one element and empty arrays—plus interviewing tips and test case ideas.

Learn how to generate all permutations of an array using a swap-based backtracking method. Analyze the time and space complexity, including factorial growth and big-O implications.

Watch a recursive backtracking code walkthrough that generates all permutations by swapping array elements, exploring swaps via a helper function, and pushing each complete permutation to the results.

Explore the powerset of a given array by generating all unique subsets, including the empty set, with no duplicates and in any order; test cases like [1,7] and [1,2,3] illustrate.

Explore generating all subsets (the power set) of an array with a recursive approach, compare with iterative construction, and analyze time and space complexity as two to the power n.

Trace a depth-first recursive walk that builds all subsets of an array using a helper function and backtracking, collecting outputs and analyzing time and space complexity.

Explore a Python implementation of the power set using a recursive helper function, generating all subsets of a given array and analyzing time and space complexity.

Explore strings as data structures stored as arrays of integers, enabling constant-time index access and costs of append and concatenate in languages like C++, JavaScript, Python, and Java.

Learn to find the index of the first non repeating character in a mixed string of letters and digits, handle edge cases, case sensitivity, and design interview test cases.

Apply a hash table to optimize a string problem by counting character occurrences in a first pass and finding unique ones in a second pass, reducing brute-force O(n^2).

Discover how to find the first non repeating character by building a hash table of character counts and traversing the string again to return its index.

Follow a code walkthrough that builds a hash table to find the first non repeating character in a string, returning its index with O(n) time and constant space.

Python code finds the first non repeating character by counting with a dictionary and scanning for a count of one, using O(n) time and O(1) space.

builds a palindrome check function that uses a for loop to traverse the string from end to start, builds a reversed string, and returns true or false with case sensitivity.

Build a reversed string by iterating from the end; note that in JavaScript, strings are immutable, so each append creates a string, yielding O(n^2) time and O(n) space.

Walk through a code walkthrough that builds a reversed string by pushing chars from right to left and joining them to compare with original; note O(n) time and O(n) space.

Python code checks palindromes by building a reversed array of characters, appending each in reverse order, then joining to compare with the original string, in O(n) time and O(n) space.

Apply the two-pointer method from both ends to test palindrome, moving inward and comparing characters; this O(n) time, O(1) space solution is optimal among the three methods.

Learn how a Python function checks if a string is a palindrome using two pointers, comparing characters and returning true or false with O(n) time and O(1) space.

Learn to determine the length of the longest substring without repeating characters in a string, with xyzxp, beep, and a r-to-w case; note to ask clarifying questions in interviews.

Learn to compare brute force substring enumeration with an efficient hash-table method that updates the start index to find longest substring with unique characters, and analyze time and space complexity.

Walk through a code walkthrough that explains how to find the longest substring with unique characters using a hash table and index tracking, with time and space complexity analysis.

This lecture shows a Python function to find the longest substring with unique characters using a seen dictionary and a moving start to skip duplicates.

Group anagrams from an array of lowercase strings by rearranging letters to use all original letters exactly once, and return the grouped anagrams in any order.

Group anagrams by sorting each string, map sorted forms with a hash table, and return grouped results, while analyzing time and space complexity in terms of input size.

Implement a group anagrams function that takes an array of strings, sorts each string's characters to form keys, builds a hash table, and returns grouped anagram arrays.

Master binary search on sorted data, whether numbers or strings, using a divide-and-conquer approach to locate a target's index in log n time.

Understand how binary search finds a target in a sorted array by halving search space, computing the middle index, and adjusting left and right pointers with iterative and recursive implementations.

Implement iterative binary search on an integer array using left, right, and mid; compare to target, adjust bounds, and return index or -1, with O(log n) time and O(1) space.

Perform recursive binary search on an array of integers using a helper with left and right pointers, returning the target index or minus one.

Explore how to implement an iterative binary search in Python, using left and right pointers to find 87 in a sorted array with O(log n) time and O(1) space.

Master recursive binary search in Python, using a helper function with left and right pointers, mid calculations, and base-case checks to locate a target in a sorted array.

Explore searching in a rotated sorted array using a binary-search variation to find a target in log n time, handling rotation pivots and returning the index or -1.

Implement an iterative search in a rotated sorted array using left, right, and middle indices to identify sorted side and narrow the search with O(log n) time and O(1) space.

Implement an iterative Python function to search a target in a rotated array. Use binary search, identify sorted half, and return the index with log n time and constant space.

Learn to find the first and last indices of a target in a sorted non decreasing array using a binary search variation, achieving O(log n) time.

Explore a two-phase modified binary search to find the left and right extremes of a target in a sorted array, achieving logarithmic time with constant space.

Execute an iterative solution to find the target range in a sorted array by performing two modified binary searches for left and right extremes.

Discover an iterative Python solution to find the target range in a non-decreasing array using left and right extreme searches. This approach yields log n time and constant space.

Learn to search a matrix efficiently where each row is sorted left to right and each row's first element exceeds the previous row's last, returning true if the target exists.

Use two binary searches to locate a target in a sorted matrix: find the row, then search that row. Achieves log m + log n time with O(1) space.

Implement the bubble sort algorithm by repeatedly comparing adjacent elements, swapping when out of order, and performing passes until the array is sorted, illustrated with test cases.

Explore bubble sort, swapping adjacent elements to move the largest to the right in each pass, and analyze time complexity O(n^2) and space O(1).

Implement bubble sort in a function that repeatedly passes through an array, uses a sorted flag and a counter to stop early, swaps in place, and tests with example arrays.

Walk through a bubble sort on a sample array, showing how adjacent elements swap during each pass and how the algorithm achieves O(n^2) time with O(1) space.

Walk through a Python bubble sort implementation, tracing swaps, passes, and index operations on an array. Learn why the algorithm runs in O(n^2) time with O(1) space.

Learn insertion sort by building a sorted portion and inserting each element into its correct position, with best case O(n), worst and average case O(n^2), and constant space.

Learn to implement the insertion sort by moving elements between the sorted and unsorted parts, inserting at the correct position, and noting its quadratic time with constant space.

Learn Python code for insertion sort, inserting each unsorted element into the correct position in the sorted left part, using temp and index shifts; note O(n^2) time and O(1) space.