Leetcode #2068: Check Whether Two Strings are Almost Equivalent
In this guide, we solve Leetcode #2068 Check Whether Two Strings are Almost Equivalent 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.
Problem Statement
Two strings word1 and word2 are considered almost equivalent if the differences between the frequencies of each letter from 'a' to 'z' between word1 and word2 is at most 3. Given two strings word1 and word2, each of length n, return true if word1 and word2 are almost equivalent, or false otherwise.
Quick Facts
- Difficulty: Easy
- Premium: No
- Tags: Hash Table, String, Counting
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: word1 = "aaaa", word2 = "bccb"
Output: false
Explanation: There are 4 'a's in "aaaa" but 0 'a's in "bccb".
The difference is 4, which is more than the allowed 3.
Python Solution
class Solution:
def checkAlmostEquivalent(self, word1: str, word2: str) -> bool:
cnt = Counter(word1)
for c in word2:
cnt[c] -= 1
return all(abs(x) <= 3 for x in cnt.values())
Complexity
The time complexity is and the space complexity is . The space complexity is .
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.