# Hash Vs Tree

> Choose between hash tables and tree structures for lookups, counting, and grouping. Use when deciding between O(1) hash operations and O(log n) ordered operations.

- Skill: `knoopx/hash-vs-tree` (Agent Skill)
- Install (CLI): `npx skillmds@latest add knoopx/hash-vs-tree`
- Raw SKILL.md: https://api.skillmd.com/api/skills/knoopx/hash-vs-tree/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: knoopx (https://skillmd.com/u/knoopx)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/knoopx/hash-vs-tree

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## When to use

Use dict/set (hash table, O(1) avg lookup) for: membership testing, frequency counting, deduplication, grouping by key.

## Rules

- Use collections.Counter for frequency counts, defaultdict(list) for grouping
- When you need ordered keys or range queries, use sorted containers or bisect on a sorted list
- For "find if X exists" or "count occurrences," ALWAYS reach for a set or dict first
- NEVER scan a list repeatedly when a set/dict would work
- If the problem involves pairs summing to a target, use a set to check complements in O(n) instead of O(n^2) nested loops

## Complexity

Hash table: O(1) avg lookup. Sorted containers: O(log n) lookup.

## Example

"Two sum" → store complements in a set: `for x in nums: if target - x in seen: return True; seen.add(x)`. O(n) vs O(n²) nested loops.