Hash Map Counting And Frequency Analysis

What's being tested

These problems test frequency analysis: converting raw inputs into counts, then using those counts to answer grouping, mode, subset, and top-K queries efficiently. Interviewers are probing whether you can choose the right hash map, avoid double-counting, and reason about ordering/tie-breaking under realistic constraints.

Patterns & templates

• Hash map counting with `dict` / `HashMap` — build value -> count in O(n) time; initialize with `defaultdict(int)` or `getOrDefault`.

• Feature extraction before counting — map each item to digits, letters, coordinates, or keys; count features, not necessarily original values.

• Set deduplication per item — for digit-sharing subsets, count each digit once per number; 112 contributes once to digit 1, not twice.

• Mode tracking during traversal — update maxFreq while visiting tree nodes; avoid a second pass unless simpler and memory allows.

• Top-K selection using heap or bucket sort — O(n log k) with heap; bucket works when frequencies are bounded by n.

• Composite ordering — implement comparator carefully for (frequency desc, distance asc, id asc); tie-breaking bugs are common in top-K queries.

• Grid/component counting — use visited plus DFS/BFS; frequency maps may combine with traversal for island sizes or target-word multiset counts.

Common pitfalls

Pitfall: Counting repeated features within the same element, e.g. treating digit 7 twice in 707 when the subset condition only needs membership.

Pitfall: Sorting the entire frequency table for top-K when k is small; a size-`k` heap is usually cleaner and faster.

Pitfall: Ignoring ties for mode or top-K; Google interviewers often expect deterministic output or an explicit tie policy.

Practice these

The practice cards below cover the canonical variants — solve all of them and time yourself.