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Leetcode #1297: Maximum Number of Occurrences of a Substring

In this guide, we solve Leetcode #1297 Maximum Number of Occurrences of a Substring in Python and focus on the core idea that makes the solution efficient.

You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.

Leetcode

Problem Statement

Given a string s, return the maximum number of occurrences of any substring under the following rules: The number of unique characters in the substring must be less than or equal to maxLetters. The substring size must be between minSize and maxSize inclusive.

Quick Facts

  • Difficulty: Medium
  • Premium: No
  • Tags: Hash Table, String, Sliding Window

Intuition

Fast membership checks and value lookups are the heart of this problem, which makes a hash map the natural choice.

By storing what we have already seen (or counts/indexes), we can answer the question in one pass without backtracking.

Approach

Scan the input once, using the map to detect when the condition is satisfied and to update state as you go.

This keeps the solution linear while remaining easy to explain in an interview setting.

Steps:

  • Initialize a hash map for seen items or counts.
  • Iterate through the input, querying/updating the map.
  • Return the first valid result or the final computed value.

Example

Input: s = "aababcaab", maxLetters = 2, minSize = 3, maxSize = 4 Output: 2 Explanation: Substring "aab" has 2 occurrences in the original string. It satisfies the conditions, 2 unique letters and size 3 (between minSize and maxSize).

Python Solution

class Solution: def maxFreq(self, s: str, maxLetters: int, minSize: int, maxSize: int) -> int: ans = 0 cnt = Counter() for i in range(len(s) - minSize + 1): t = s[i : i + minSize] ss = set(t) if len(ss) <= maxLetters: cnt[t] += 1 ans = max(ans, cnt[t]) return ans

Complexity

The time complexity is O(n×m)O(n \times m)O(n×m), and the space complexity is O(n×m)O(n \times m)O(n×m). The space complexity is O(n×m)O(n \times m)O(n×m).

Edge Cases and Pitfalls

Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.

Summary

This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.


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