Hash Map Frequency Table Errors LeetCode Beginners

Counting with hash maps should be easy, yet many submissions fail because of tiny mistakes. This article shows how to build and verify frequency tables step by step so you stop leaking points on easy questions.

TL;DR

• A frequency table maps items to counts; interviews use it for anagrams, windows, and duplicate checks.
• Errors happen when keys are missing, counts go negative, or state leaks between test cases.
• Core steps: initialize clean storage, increment safely, decrement with guards, and validate after each operation.
• Beginners often mutate the map while iterating or forget to reset between runs.
• You'll learn a reusable helper, a checklist, visual cues, and common pitfalls to avoid.

Beginner-Friendly Explanations

What a Frequency Table Does

It tracks how many times each value appears. In strings, keys are characters; in arrays, keys are numbers. This supports quick lookups during the AI prep primer.

Why Interviews Care

Frequency accuracy underpins anagram checks, sliding windows, and hashmap-based duplicates. Small counting errors can invalidate the whole algorithm.

Step-by-Step Learning Guidance

1) Start With a Clean Map

Always initialize a new Map or object inside the function. Reusing global maps leads to stale counts.

2) Use Safe Increment Helpers

Wrap updates to avoid undefined keys:

function bumpCount(map: Map<string, number>, key: string, delta: number) {
  map.set(key, (map.get(key) || 0) + delta);
}

3) Validate After Mutations

If a decrement drops below zero, you likely removed too much. Log the offending key and state.

4) Keep Windows Balanced

For sliding windows, increment when a char enters and decrement when it leaves. Compare the map to a target template each step.

5) Reset Between Test Cases

Before reusing the helper, clear the map: map.clear(). Forgetting this is a silent bug.

Visualizable Example: Anagram Window Check

function findAnagramStarts(s: string, p: string): number[] {
  const need = new Map<string, number>();
  for (const ch of p) bumpCount(need, ch, 1);

  const window = new Map<string, number>();
  const result: number[] = [];
  let matched = 0;

  for (let i = 0; i < s.length; i++) {
    const inChar = s[i];
    bumpCount(window, inChar, 1);
    if (window.get(inChar) === need.get(inChar)) matched++;

    if (i >= p.length) {
      const outChar = s[i - p.length];
      if (window.get(outChar) === need.get(outChar)) matched--;
      bumpCount(window, outChar, -1);
      if (window.get(outChar) === 0) window.delete(outChar);
    }

    if (matched === need.size) result.push(i - p.length + 1);
  }

  return result;
}

The helper avoids undefined keys, and the delete step prevents stale zero entries that can skew size-based comparisons.

Practical Preparation Strategies

Build a Reuseable Template

Keep a snippet for bumping counts and clearing maps. Paste it into new problems to reduce cognitive load.

Compare Against a Baseline

After writing your logic, run a tiny case (s = "ab", p = "a") and print both maps each step. This mirrors the pacing of the learning tools page without over-logging.

Watch for Iteration Side Effects

If you iterate keys while mutating counts, copy keys first. This prevents skipped entries on deletion.

Add Lightweight Assertions

Throw if any count is negative. Tools like LeetCopilot can surface these assertions quickly during practice.

Common Mistakes to Avoid

Off-by-One Window Moves

Shifting the window by two indices instead of one breaks alignment. Ensure you add before removing in each iteration.

Missing Key Initialization

Using map.get(key)! + 1 when the key is missing yields NaN. Default to zero before adding.

Not Clearing Zero Counts

Leaving zero entries inflates map size comparisons. Delete them so size reflects active keys.

Ignoring Input Case

Upper and lower case characters are distinct. Normalize if the problem statement allows it.

FAQ

How do I know the frequency table is correct?
After each update, sum the counts and compare to the processed substring length. Mismatches reveal bugs.

What should I practice before sliding windows?
Start with single-pass counting (e.g., anagram check) to internalize increments and decrements.

Can I use objects instead of Map?
Yes, but remember objects default undefined for missing keys. Be explicit when incrementing.

Is hashing order important?
Order rarely matters for counts, but mutating during iteration can skip keys. Copy keys first if you must delete.

Conclusion

Hash map frequency tables are a small tool with outsized interview impact. By initializing cleanly, using safe increments, and validating windows, you prevent silent failures. Brief, focused practice—and occasional guardrails from tools like LeetCopilot—turns counting from a liability into a strength.