leetcode/docs/(data-structures)/02-hash-tables.mdx

---
title: 'Hash Tables'
description: 'In algorithms, arrays and strings are very similar. They are both ordered collections of elements. The difference is that arrays are mutable, while strings are immutable.'
---

## The idea

A hash table is like a helpful grocery clerk.
You say "avocado". She tells you the price at once.
She does not walk the aisles. She just *knows*.

How? A hash function turns your key into a slot number.
Same key → same slot, every time. Lookup is O(1).

## The picture

```mermaid
flowchart LR
    K1["'apple'"] --> H["Hash<br/>function"]
    K2["'milk'"] --> H
    K3["'avocado'"] --> H
    H --> S0["Slot 0: milk → 1.49"]
    H --> S1["Slot 1: apple → 0.67"]
    H --> S2["Slot 2: avocado → 1.99"]

    style H fill:#1565c0,color:#fff
```

## Three jobs it does

1. **Membership** — "have I seen this before?"
2. **Frequency** — "how many times?"
3. **Mapping** — "what goes with this?"

## Membership (LC 217 — Contains Duplicate)

```python
def contains_duplicate(nums: list[int]) -> bool:
    seen = set()
    for n in nums:
        if n in seen:
            return True
        seen.add(n)
    return False
```

## Frequency (LC 242 — Valid Anagram)

```python
from collections import Counter

def is_anagram(s: str, t: str) -> bool:
    if len(s) != len(t):
        return False
    return Counter(s) == Counter(t)
```

## Key design (LC 49 — Group Anagrams)

Sometimes the trick is *what you use as the key*.
"eat", "tea", "ate" all sort to "aet". Sorted string = group key.

```mermaid
flowchart LR
    A["eat"] --> K["key: 'aet'"]
    B["tea"] --> K
    C["ate"] --> K
    K --> G["group: [eat, tea, ate]"]

    style K fill:#1565c0,color:#fff
```

```python
from collections import defaultdict

def group_anagrams(strs: list[str]) -> list[list[str]]:
    groups = defaultdict(list)
    for s in strs:
        key = "".join(sorted(s))
        groups[key].append(s)
    return list(groups.values())
```

## Complexity

| | Average | Worst |
|---|---|---|
| Lookup / insert / delete | O(1) | O(n) |

Worst case comes from collisions. Interviews treat it as O(1).

## Close-out ritual

Before you submit, say out loud:
1. Time and space complexity.
2. One edge case trace: empty input, all same element, or key collision on your key design.

## Plan problems

**A-set:** LC 217 · 242 · 1 · 49 · 347
**B-set:** LC 383 · 205 · 128 · 290