Frequency Map (Using HashMap for Counting)

A frequency map is literally Map<Key, int>: the default interview representation because it handles sparse keys and arbitrary strings gracefully.

Why this shows up in the real world

Feature stores map user ids to event counts; compilers map symbols to usage counts for warnings.

Core idea (explained for students)

Prefer Counter for brevity or explicit dict for didactic clarity. Serialize to sorted items when you need deterministic comparison.

Try this in Python

from collections import Counter


def letter_freq(s: str) -> dict[str, int]:
    return dict(Counter(c for c in s.lower() if c.isalpha()))


print(letter_freq('Hello, World!'))

Common mistakes

• Using lists where O(1) membership is required—set vs dict confusion.
• Integer keys vs string digits ('1' vs 1).

Key takeaways

• dict.get avoids KeyError on reads; Counter never raises on missing keys for read.
• For nested frequencies use defaultdict(Counter).