- Distilled set of LeetCode problems for learning data structures and algorithms solved using Python 3.
- Refer Python Tips and Tricks for an review of data structure concepts and their implementations with Python 3.
- Note that recursive (top-down) implementations store call-stacks (and hence are not \(O(1)\) space complexity). For \(O(1)\) space complexity, the computations must be performed iteratively (bottom-up) without using call-stacks.
- Here’s a list of the full problem set on LeetCode.
- These tips are from Sean Prasad’s LeetCode Patterns Github repo.
If input array is sorted, then - Binary search If input array is not necessarily sorted, then - Sliding window - Two pointers If asked for all permutations/combinations/subsets, then - Backtracking If given a tree/graph/grid, then - DFS - BFS If given a linked list then - Two pointers If recursion is banned then - Stack If must solve in-place then - Swap corresponding values - Store one or more different values in the same pointer If asked for maximum/minimum subarray/subset/options then - Dynamic programming If asked for top/least K items then - Heap If asked for common strings then - Map - Trie Else - Map/Set for O(1) time & O(n) space - Sort input for O(nlogn) time and O(1) space - A subarray or substring will always be contiguous, but a subsequence need not be contiguous, i.e., subsequences are not required to occupy consecutive positions within the original sequences.
- Check out LeetCode patterns sorted by companies/difficulty level/patterns here.
- Two Pointers
- Sliding Window
- Binary Search
- Hash Table
- Topological Sort
- Python for Interviewing: An Overview of the Core Data Structures
- NeetCode 150/Blind 75
- Blind 50
- Blind 75; mirror
- Grind 75
- LeetCode Solutions
- Sean Prasad: Leetcode Patterns