Hash Tables —
O(1) lookup. Explained.
Hash tables look like magic until you see the internals. A hash function maps every key to a memory slot — enabling near-instant lookups, insertions, and deletions. LearnBug makes the hashing process visible, step by step, so you understand exactly why it's so fast.
What is a Hash Table?
A hash table (called a dictionary in Python) stores data as key-value pairs. It uses a hash function to map keys to memory locations, enabling near-instant lookups, insertions, and deletions. Hash tables power databases, caches, frequency counters, and two-sum style problems.
Why visualization helps
The logic of hashing is invisible when you're just reading code. Seeing which key maps to which slot, how collisions are handled, and how lookups happen in real time makes the concept click instantly. LearnBug shows the hash function at work behind every dict operation.
Hash table patterns, visualized
Two Sum Visualization
From O(n²) brute force to O(n) hash map. Watch the complement lookup happen in real time and see why this is the canonical hash table problem.
Frequency Counter
Build a frequency map and watch counts update with each element. The pattern behind anagram detection, majority element, and more.
Anagram Check
Are two strings anagrams? Watch frequency maps build for each string and see the character-by-character comparison at every step.
Group Anagrams
Sort-based key hashing. Watch words group themselves into buckets by their sorted character signature — a clever hash key trick.
Subarray Sum Equals K
Prefix sum + hash map. Watch the running sum and complement lookups combine to find all subarrays summing to k in a single pass.
Run your hash table code on LearnBug
Watch your dict operations execute step by step — and see every key-value pair appear in real time.