data-structureslisted

# Data Structure Patterns This skill provides recognition signals, core ideas, Python templates, and pitfall guidance for seven advanced data structure patterns. Use it when a problem's constraints or access patterns suggest a specialized structure beyond basic arrays, hash maps, or linked lists. Each pattern section follows a consistent format: when to reach for it, how it works, a clean implementation template, and the mistakes that cost time in practice. --- ## Pattern Recognition Table | Trigger Signals | Technique | Typical Complexity | |---|---|---| | "k-th largest/smallest", "top K", "merge K sorted" | Heap / Priority Queue | O(n log k) | | "next greater/smaller element", "sliding window max/min" | Monotonic Stack / Queue | O(n) | | "prefix matching", "autocomplete", "word search in grid" | Trie | O(L) per operation | | "range query + point update", "range min/max/sum" | Segment Tree | O(log n) per query/update | | "prefix sums with updates", "count of elements less than X" | Fenwick Tree (BIT) | O(log n) per query/update | | "balanced parentheses", "evaluate expression", "nested structures" | Stack-Based Parsing | O(n) | | "rank of element", "k-th smallest in dynamic set", "floor/ceiling" | Ordered Set (SortedList) | O(log n) per operation | --- ## Constraint-to-Technique Mapping When the problem statement does not name a structure directly, use constraints to narrow the choice: - **n <= 10^5 with repeated range queries** -- Segment Tree or Fenwick Tree. Prefe