Off-by-one on range boundaries
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Build confidence with an intuition-first walkthrough focused on array fundamentals.
You are given an integer array nums and an integer k.
Return an integer denoting the sum of all elements in nums whose frequency is divisible by k, or 0 if there are no such elements.
Note: An element is included in the sum exactly as many times as it appears in the array if its total frequency is divisible by k.
Example 1:
Input: nums = [1,2,2,3,3,3,3,4], k = 2
Output: 16
Explanation:
So, the total sum is 2 + 2 + 3 + 3 + 3 + 3 = 16.
Example 2:
Input: nums = [1,2,3,4,5], k = 2
Output: 0
Explanation:
There are no elements that appear an even number of times, so the total sum is 0.
Example 3:
Input: nums = [4,4,4,1,2,3], k = 3
Output: 12
Explanation:
So, the total sum is 4 + 4 + 4 = 12.
Constraints:
1 <= nums.length <= 1001 <= nums[i] <= 1001 <= k <= 100Problem summary: You are given an integer array nums and an integer k. Return an integer denoting the sum of all elements in nums whose frequency is divisible by k, or 0 if there are no such elements. Note: An element is included in the sum exactly as many times as it appears in the array if its total frequency is divisible by k.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: Array · Hash Map
[1,2,2,3,3,3,3,4] 2
[1,2,3,4,5] 2
[4,4,4,1,2,3] 3
Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #3712: Sum of Elements With Frequency Divisible by K
class Solution {
public int sumDivisibleByK(int[] nums, int k) {
Map<Integer, Integer> cnt = new HashMap<>();
for (int x : nums) {
cnt.merge(x, 1, Integer::sum);
}
int ans = 0;
for (var e : cnt.entrySet()) {
int x = e.getKey(), v = e.getValue();
if (v % k == 0) {
ans += x * v;
}
}
return ans;
}
}
// Accepted solution for LeetCode #3712: Sum of Elements With Frequency Divisible by K
func sumDivisibleByK(nums []int, k int) (ans int) {
cnt := map[int]int{}
for _, x := range nums {
cnt[x]++
}
for x, v := range cnt {
if v%k == 0 {
ans += x * v
}
}
return
}
# Accepted solution for LeetCode #3712: Sum of Elements With Frequency Divisible by K
class Solution:
def sumDivisibleByK(self, nums: List[int], k: int) -> int:
cnt = Counter(nums)
return sum(x * v for x, v in cnt.items() if v % k == 0)
// Accepted solution for LeetCode #3712: Sum of Elements With Frequency Divisible by K
fn sum_divisible_by_k(nums: Vec<i32>, k: i32) -> i32 {
nums.into_iter()
.fold(std::collections::HashMap::new(), |mut acc, n| {
*acc.entry(n).or_insert(0) += 1;
acc
})
.into_iter()
.fold(
0,
|acc, (n, v)| {
if v % k == 0 {
acc + (n * v)
} else {
acc
}
},
)
}
fn main() {
let nums = vec![1, 2, 2, 3, 3, 3, 3, 4];
let ret = sum_divisible_by_k(nums, 2);
println!("ret={ret}");
}
#[test]
fn test() {
{
let nums = vec![1, 2, 2, 3, 3, 3, 3, 4];
let ret = sum_divisible_by_k(nums, 2);
assert_eq!(ret, 16);
}
{
let nums = vec![1, 2, 3, 4, 5];
let ret = sum_divisible_by_k(nums, 2);
assert_eq!(ret, 0);
}
{
let nums = vec![4, 4, 4, 1, 2, 3];
let ret = sum_divisible_by_k(nums, 3);
assert_eq!(ret, 12);
}
}
// Accepted solution for LeetCode #3712: Sum of Elements With Frequency Divisible by K
function sumDivisibleByK(nums: number[], k: number): number {
const cnt = new Map();
for (const x of nums) {
cnt.set(x, (cnt.get(x) || 0) + 1);
}
let ans = 0;
for (const [x, v] of cnt.entries()) {
if (v % k === 0) {
ans += x * v;
}
}
return ans;
}
Use this to step through a reusable interview workflow for this problem.
Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.
Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.
Review these before coding to avoid predictable interview regressions.
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
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.