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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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 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.

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.

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.

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.