Maximum Difference Between Even and Odd Frequency II - Problem
You're given a string s and an integer k. Your challenge is to find the maximum difference between character frequencies in any substring of length at least k.
Specifically, you need to find freq[a] - freq[b] where:
- The substring has at least k characters
- Character
aappears an odd number of times - Character
bappears a non-zero even number of times
Think of it as finding the most "unbalanced" pair of characters in a valid substring!
Example: In substring "aaabbc", character 'a' appears 3 times (odd) and 'b' appears 2 times (even), giving a difference of 3 - 2 = 1.
Input & Output
example_1.py ā Basic Case
$
Input:
s = "aaabbc", k = 3
āŗ
Output:
1
š” Note:
In substring 'aaabbc' (length 6 ā„ k=3), character 'a' appears 3 times (odd) and 'b' appears 2 times (even). The difference is 3-2 = 1.
example_2.py ā Multiple Options
$
Input:
s = "abccba", k = 4
āŗ
Output:
1
š” Note:
In substring 'abccba' (length 6 ā„ k=4), character 'c' appears 2 times (even) and 'a' or 'b' appear 2 times each (even). We need odd-even pair, so we look at shorter valid substrings where we can find the best difference of 1.
example_3.py ā Edge Case
$
Input:
s = "aab", k = 3
āŗ
Output:
1
š” Note:
The only valid substring is 'aab' itself. Character 'a' appears 2 times (even) and 'b' appears 1 time (odd). Since we need odd-even difference, it's 1-2 = -1, but we want max, so difference is 1.
Visualization
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Understanding the Visualization
1
Identify Valid Segments
Find all continuous segments of length at least k characters
2
Count Character Frequencies
For each segment, count how many times each character appears
3
Classify Odd vs Even
Separate characters by whether their frequency is odd or even
4
Calculate Maximum Difference
Find the largest gap: max(odd_frequency) - min(even_frequency)
Key Takeaway
šÆ Key Insight: Use sliding window with frequency maps to efficiently track character counts and find optimal odd-even pairs without redundant calculations, reducing time complexity from O(n³) to O(n²).
Time & Space Complexity
Time Complexity
O(n²)
O(n) possible starting positions, each with O(n) possible ending positions, but with optimized frequency tracking
ā Quadratic Growth
Space Complexity
O(1)
Frequency map size is bounded by alphabet size (constant)
ā Linear Space
Constraints
- 1 ⤠k ⤠s.length ⤠105
- s consists of lowercase English letters only
- Must find substring of length at least k
- Character with odd frequency and character with even frequency must both exist in the chosen substring
š”
Explanation
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// Output will appear here after running code