Hashing: A Study Guide
Hash tables and hash sets: how hashing works, operations and Big-O, a Python example, collisions and other pitfalls, and the days that cover hashing.
Sets
Hash Maps and Sets: The Most Underrated FAANG Topic
Why hash maps and sets deserve more interview prep time than trees or dynamic programming. Explains the O(1) lookup superpower that turns quadratic brute-force solutions into linear ones, then covers the three patterns behind most hash map interview questions: frequency counting (valid anagram, top-K elements with Counter), the two-sum complement lookup family (two sum, subarray sum equals K with prefix sums), and grouping by canonical key (group anagrams with defaultdict). Includes five fully-solved Python problems, a pattern recognition cheat sheet, complexity analysis, and the follow-up questions interviewers ask about collisions and worst-case behavior.
Practical Applications of Dictionaries and Sets
Moves beyond syntax to show five real scenarios where dictionaries and sets solve problems concisely: a word frequency counter that normalizes and tallies every word in a block of text, duplicate removal by converting a list to a set and back, a student grades database that computes per-student averages, a voter participation analysis using set intersection and difference to identify who voted in both elections versus only one, and an enhanced phone book with add, update, search, and display operations. Closes with a product inventory system exercise.
Dictionaries and Sets: Efficient Data Retrieval
Introduces Python dictionaries as key-value stores, covering creation, access, mutation (add, update, delete), and built-in methods like keys(), values(), and items(). Explains nested dictionaries for representing complex records. Then covers sets as unordered collections of unique elements, demonstrating union, intersection, and difference operations. Ties both together in a phone book exercise that uses a dictionary to store contacts and set semantics to enforce uniqueness, reinforcing when to reach for each data structure.