LeetCode #1399 — EASY

Count Largest Group

Build confidence with an intuition-first walkthrough focused on hash map fundamentals.

The Problem

Problem Statement

You are given an integer n.

We need to group the numbers from 1 to n according to the sum of its digits. For example, the numbers 14 and 5 belong to the same group, whereas 13 and 3 belong to different groups.

Return the number of groups that have the largest size, i.e. the maximum number of elements.

Example 1:

Input: n = 13
Output: 4
Explanation: There are 9 groups in total, they are grouped according sum of its digits of numbers from 1 to 13:
[1,10], [2,11], [3,12], [4,13], [5], [6], [7], [8], [9].
There are 4 groups with largest size.

Example 2:

Input: n = 2
Output: 2
Explanation: There are 2 groups [1], [2] of size 1.

Constraints:

1 <= n <= 10⁴

Step 01

Brute Force Baseline

Problem summary: You are given an integer n. We need to group the numbers from 1 to n according to the sum of its digits. For example, the numbers 14 and 5 belong to the same group, whereas 13 and 3 belong to different groups. Return the number of groups that have the largest size, i.e. the maximum number of elements.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Hash Map · Math

Example 1

Example 2

Step 02

Core Insight

What unlocks the optimal approach

Count the digit sum for each integer in the range and find out the largest groups.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #1399: Count Largest Group
class Solution {
    public int countLargestGroup(int n) {
        int[] cnt = new int[40];
        int ans = 0, mx = 0;
        for (int i = 1; i <= n; ++i) {
            int s = 0;
            for (int x = i; x > 0; x /= 10) {
                s += x % 10;
            }
            ++cnt[s];
            if (mx < cnt[s]) {
                mx = cnt[s];
                ans = 1;
            } else if (mx == cnt[s]) {
                ++ans;
            }
        }
        return ans;
    }
}

// Accepted solution for LeetCode #1399: Count Largest Group
func countLargestGroup(n int) (ans int) {
	cnt := [40]int{}
	mx := 0
	for i := 1; i <= n; i++ {
		s := 0
		for x := i; x > 0; x /= 10 {
			s += x % 10
		}
		cnt[s]++
		if mx < cnt[s] {
			mx = cnt[s]
			ans = 1
		} else if mx == cnt[s] {
			ans++
		}
	}
	return
}

# Accepted solution for LeetCode #1399: Count Largest Group
class Solution:
    def countLargestGroup(self, n: int) -> int:
        cnt = Counter()
        ans = mx = 0
        for i in range(1, n + 1):
            s = 0
            while i:
                s += i % 10
                i //= 10
            cnt[s] += 1
            if mx < cnt[s]:
                mx = cnt[s]
                ans = 1
            elif mx == cnt[s]:
                ans += 1
        return ans

// Accepted solution for LeetCode #1399: Count Largest Group
impl Solution {
    pub fn count_largest_group(n: i32) -> i32 {
        let mut cnt = vec![0; 40];
        let mut ans = 0;
        let mut mx = 0;

        for i in 1..=n {
            let mut s = 0;
            let mut x = i;
            while x > 0 {
                s += x % 10;
                x /= 10;
            }
            cnt[s as usize] += 1;
            if mx < cnt[s as usize] {
                mx = cnt[s as usize];
                ans = 1;
            } else if mx == cnt[s as usize] {
                ans += 1;
            }
        }

        ans
    }
}

// Accepted solution for LeetCode #1399: Count Largest Group
function countLargestGroup(n: number): number {
    const cnt: number[] = Array(40).fill(0);
    let mx = 0;
    let ans = 0;
    for (let i = 1; i <= n; ++i) {
        let s = 0;
        for (let x = i; x; x = Math.floor(x / 10)) {
            s += x % 10;
        }
        ++cnt[s];
        if (mx < cnt[s]) {
            mx = cnt[s];
            ans = 1;
        } else if (mx === cnt[s]) {
            ++ans;
        }
    }
    return ans;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n × log n)

Space

O(log n)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

For each element, scan the rest of the array looking for a match. Two nested loops give n × (n−1)/2 comparisons = O(n²). No extra space since we only use loop indices.

HASH MAP

O(n) time

O(n) space

One pass through the input, performing O(1) hash map lookups and insertions at each step. The hash map may store up to n entries in the worst case. This is the classic space-for-time tradeoff: O(n) extra memory eliminates an inner loop.

Shortcut: Need to check “have I seen X before?” → hash map → O(n) time, O(n) space.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.