Grokking the Coding Interview: Patterns in Python

Master the two-pointer technique for solving problems involving sorted arrays, pair sums, and in-place manipulation with O(n) time complexity.

Use two pointers moving at different speeds to detect cycles, find midpoints, and identify structural properties in linked lists and arrays.

Efficiently process contiguous subarrays and substrings by maintaining a dynamic window that expands and contracts instead of recomputing from scratch.

Solve scheduling, calendar, and range-overlap problems by sorting intervals and merging or inserting them with a sweep-line approach.

Sort arrays containing numbers in a known range in O(n) by placing each element at its correct index, and reverse linked list substructures without extra space.

Navigate binary trees and n-ary trees using level-order BFS (queue-based) and path-tracking DFS (stack/recursion) to solve a broad class of tree problems.

Use min-heaps and max-heaps to efficiently track medians, running statistics, and the K most extreme elements in dynamic data streams.

Generate all subsets, permutations, and combinations using BFS-style expansion or recursive backtracking, and extend binary search beyond simple lookup to rotated arrays and boundary problems.

Efficiently merge multiple sorted streams using a heap-based tournament, and leverage XOR's self-inverse property to find missing or unique elements in O(1) space.

Represent and traverse graphs with BFS and DFS, detect cycles, and produce topological orderings of directed acyclic graphs using Kahn's algorithm.

Recognize overlapping subproblems and optimal substructure across five DP families — 0/1 Knapsack, Unbounded Knapsack, Fibonacci, Palindromic Subsequences, and Longest Common Substring.

Apply all 16 patterns under simulated interview conditions — practice pattern recognition from problem statements, optimize solutions, and communicate your thought process clearly.