5 Best Ways to Find the Largest Substring Between Two Equal Characters in Python

💡 Problem Formulation: Finding the largest substring enclosed by two identical characters in a string is a common coding problem. Given an input string, the goal is to extract the longest substring where the first and last characters are the same. For instance, in the string “abcdaefghiad”, the largest such substring would be “bcdaefghia” because it starts and ends with ‘a’.

Method 1: Brute Force Approach

This method iterates through the string to check every possible substring that starts and ends with the same character. It maintains the longest such substring found. Simple to understand and implement, this method is best suited for smaller strings due to its time complexity.

Here’s an example:

def largest_substring(s):
    max_len = 0
    max_sub = ""
    for i in range(len(s)):
        for j in range(i+1, len(s)):
            if s[i] == s[j]:
                if max_len < j - i - 1:
                    max_len = j - i - 1
                    max_sub = s[i+1:j]
    return max_sub

print(largest_substring("abcdaefghiad"))

Output: bcdaefghia

This brute force function, largest_substring, iterates through all possible substrings in the given string and records the length and value of the largest valid substring. It’s a straightforward, but not performance-optimized, method to solve the problem.

Method 2: Using a Dictionary

This method utilizes a dictionary to track the first occurrence of a character and calculates the length of the substring whenever the same character is encountered again. It is faster than a brute force approach for large strings with higher time complexity.

Here’s an example:

def largest_substring(s):
    char_index = {}
    max_sub = ""
    for i, char in enumerate(s):
        if char in char_index:
            if len(max_sub) < i - char_index[char] - 1:
                max_sub = s[char_index[char]+1:i]
        else:
            char_index[char] = i
    return max_sub

print(largest_substring("abcdaefghiad"))

Output: bcdaefghia

The largest_substring function uses a dictionary named char_index to store the first index of each character. This method efficiently computes the largest substring and is especially effective for longer input strings.

Method 3: Sliding Window Technique

The sliding window technique can be applied if the string contains a bounded set of characters. It uses a dynamic window to maintain the substring, adjusting its size as the scan progresses. It is highly efficient when the character set is small and predefined.

Here’s an example:

def largest_substring(s):
    # This method assumes that only a-z characters are present in the string.
    last_seen = [-1] * 26  # Tracks the last index of each letter.
    max_sub = ""
    start_window = 0

    for i, char in enumerate(s):
        if last_seen[ord(char) - ord('a')] >= 0:
            start_window = max(start_window, last_seen[ord(char) - ord('a')] + 1)
        if len(max_sub) < i - start_window:
            max_sub = s[start_window:i+1]
        last_seen[ord(char) - ord('a')] = i

    return max_sub

print(largest_substring("abcdaefghiad"))

Output: bcdaefghia

The function largest_substring implements the sliding window technique. The integer array last_seen tracks the last occurrence of a-z characters. This method is known for its efficiency in specific conditions, like when handling limited character sets.

Method 4: Using Regular Expressions (RegEx)

Regular expressions can be used to search for patterns within the string. This approach compiles a pattern that finds substrings enclosed by the same character and iterates through the matches to find the longest one. It’s powerful but can be less readable and more computationally intensive on very large strings.

Here’s an example:

import re

def largest_substring(s):
    pattern = r"(.).+?\1"
    matches = re.finditer(pattern, s)
    max_sub = max((m.group()[1:-1] for m in matches), key=len, default="")
    return max_sub

print(largest_substring("abcdaefghiad"))

Output: bcdaefghia

The function largest_substring leverages the Python RegEx module to search for substrings. This approach is concise and powerful, but for larger strings or more complex patterns, it can become increasingly resource-intensive.

Bonus One-Liner Method 5: List Comprehension with RegEx

A more concise version of the RegEx method using list comprehension can condense the logic into a one-liner. This method combines the power of regular expression pattern matching with Python’s list comprehension syntax for brevity.

Here’s an example:

import re

largest_substring = lambda s: max((match.group()[1:-1] for match in re.finditer(r'(.).+?\\1', s)), key=len, default="")
print(largest_substring("abcdaefghiad"))

Output: bcdaefghia

The one-liner uses a lambda function alongside a list comprehension to find the largest substring between two identical characters. It provides the same functionality as the previous method, but in a more succinct form. However, it may be less readable to those unfamiliar with Python’s more advanced features.

Summary/Discussion

• Method 1: Brute Force Approach. Straightforward and simple. High time complexity makes it inefficient for large strings.
• Method 2: Using a Dictionary. More efficient than brute force. Utilizes memory to significantly reduce time complexity.
• Method 3: Sliding Window Technique. Scalable and efficient for limited character sets. Not ideal when the character set is large or unknown beforehand.
• Method 4: Using Regular Expressions (RegEx). Highly flexible can potentially cause performance issues for very large strings or complex patterns.
• Bonus Method 5: List Comprehension with RegEx. Compact and pythonic, at the expense of readability for beginners or those not familiar with RegEx.