Longest Common Prefix Leetcode Problem 14 [Python Solution]

When it comes to solving coding problems, understanding the problem statement and finding an efficient solution is crucial.

In this guide, we'll tackle the Longest Common Prefix problem, a common question in coding interviews.

We'll provide a Python solution that is both easy to understand and efficient.

So, let's dive in!

Problem Overview

The problem is quite simple: you are given a list of strings, and your task is to find the longest common prefix among these strings.

A prefix is a sequence of characters at the beginning of a string.

If there's no common prefix, you should return an empty string.

Let's illustrate this with an example.

Example 1:

Input: strs = ["flower","flow","flight"]
Output: "fl"

In this example, the longest common prefix among the given strings is "fl" because all three strings start with these characters.

Example 2:

Input: strs = ["dog","racecar","car"]
Output: ""

Here, there is no common prefix among the input strings, so we return an empty string.

Constraints:

• 1 <= strs.length <= 200
• 0 <= strs[i].length <= 200
• strs[i] consists of only lowercase English letters.

Understanding Constraints

Before we dive into the solution, let's discuss the constraints given in the problem.

These constraints are essential because they help us determine the maximum input size and any potential performance bottlenecks.

In this case:

• The input list strs can contain a maximum of 200 strings.
• Each string in strs can have a length of up to 200 characters.
• The characters in the strings are limited to lowercase English letters.

Longest Common Prefix LeetCode Problem Solution

Now, let's move on to solving the problem.

We'll provide a Python solution that's both efficient and easy to understand.

We'll explore two approaches: a brute-force approach and a more efficient one.

First, let's take a look at the brute-force approach.

1. Bruteforce Approach

The brute-force approach involves iterating through the characters of the strings and comparing them one by one.

Here's the Python code for this approach:

def longestCommonPrefix(self, strs: List[str]) -> str:
    result = ""
    for i in range(len(strs[0])):
        for s in strs:
            if i == len(s) or s[i] != strs[0][i]:
                return result
        result += strs[0][i]
    return result

In this code:
– We initialize an empty string result to store the longest common prefix.
– We use a for loop to iterate through the characters of the first string in the input list strs[0].
– Inside the loop, we have another loop that iterates through all the strings in the input list.
– We compare the current character of the first string with the corresponding character in each string.
– If the characters don't match or if we reach the end of any string, we return the result as it is.

We've found the longest common prefix.
– If the characters match for all strings, we add the character to the result and continue.

This code provides a straightforward way to find the longest common prefix.

However, it may not be the most efficient solution.

Let's explore a more optimized approach.

2. Efficient Approach

The efficient approach reduces the number of character comparisons.

We'll achieve this by comparing characters of just one string with the other strings.

This approach ensures that we don't perform unnecessary comparisons.

Python Code for the Efficient Approach (Horizontal Scanning):

def longestCommonPrefix(self, strs: List[str]) -> str:
    if not strs:
        return ""

    # Sort the list of strings to bring the shortest to the front
    strs.sort()

    first = strs[0]
    last = strs[-1]
    common_prefix = []

    for i in range(len(first)):
        if i < len(last) and first[i] == last[i]:
            common_prefix.append(first[i])
        else:
            break

    return "".join(common_prefix)

Here's how the efficient approach works:
– We start by checking if the input list strs is empty.

If it is, there can't be any common prefix, so we return an empty string.
– We sort the list of strings.

Sorting the strings will ensure that the shortest string comes to the front and the longest string goes to the end.

This is a crucial step for optimization.
– We initialize variables first with the first string and last with the last string after sorting.
– We iterate through the characters of the first string.

If the character matches the corresponding character in the last string, we add it to the common_prefix list.

If not, we break out of the loop because we've found the longest common prefix.
– Finally, we join the characters in the common_prefix list and return the result as a string.

This efficient approach significantly reduces the number of character comparisons, making it more optimal.

Time and Space Complexity

Now, let's analyze the time and space complexity of our efficient solution:

Time Complexity:
– Sorting the list of strings takes O(n * m * log(m)) time, where n is the number of strings and m is the average length of the strings.
– The loop to find the common prefix runs in O(m) time.
– So, the overall time complexity is O(n * m * log(m) + m), where n is the number of strings and m is the average length of the strings.

Space Complexity:
– The space complexity is O(m), where m is the average length of the strings.

This space is used to store the common prefix.

Reasoning Behind Our Approach

The efficient approach works by taking advantage of the fact that the shortest string among the input strings will determine the maximum length of the common prefix.

By sorting the strings, we ensure that the shortest string comes first, and we only compare characters up to the length of this shortest string with the other strings.

This approach optimizes the process and reduces unnecessary character comparisons.

Related Interview Questions By Company:

• Squares Of A Sorted Array LeetCode
• Counting Bits LeetCode
• Binary Tree Inorder Traversal LeetCode

Related Interview Questions By Difficulty:

• Next Greater Element I LeetCode
• Maximum Product Of The Length Of Two Palindromic Subsequences
• Is Subsequence LeetCode

Related Interview Questions By Category:

• Find K Closest Elements LeetCode
• Number Of Sub Arrays Of Size K And Avg Greater Than Or Equal To Threshold
• Maximum Depth Of Binary Tree LeetCode

Conclusion

In this guide, we've tackled the Longest Common Prefix problem, a common coding question.

We've provided both a brute-force approach and an efficient solution in Python.

Understanding the problem, its constraints, and the reasoning behind the efficient approach is key to solving coding problems effectively.

If you're preparing for coding interviews or looking to improve your problem-solving skills, it's essential to practice different types of problems and explore various approaches.

The more you practice, the better you'll become at tackling coding challenges.

Feel free to comment, ask questions, make suggestions, and share this content with others.

Your engagement and feedback are valuable in helping us create more helpful coding resources.

Question Link on LeetCode

Happy coding!