Longest Common Prefix in Python

Longest Common Prefix in Python – 1000 Days of Python Coding Challenges

In your journey of mastering Python, solving coding challenges daily can drastically improve your problem-solving skills. One common problem you’ll encounter is finding the Longest Common Prefix (LCP) in a list of strings. Today, we’ll dive deep into this challenge with practical Python solutions.

What is Longest Common Prefix?

The Longest Common Prefix is the longest sequence of characters that appears at the start of all strings in a given list.

Example

Input:

["flower", "flow", "flight"]

Output:

"fl"

Here, "fl" is the common prefix for all strings.

Why is Longest Common Prefix Important?

Used in autocomplete systems
Essential in data compression
Helps in string matching problems
Frequently asked coding interview question

By mastering LCP, you can handle string manipulation efficiently — a critical skill for Python developers.

Approach 1: Horizontal Scanning

This is a simple and intuitive approach. Start with the prefix of the first string and compare it with the next string. Reduce the prefix until all strings match.


def longest_common_prefix(strs):
    if not strs:
        return ""
    
    prefix = strs[0]
    for string in strs[1:]:
        while not string.startswith(prefix):
            prefix = prefix[:-1]
            if not prefix:
                return ""
    return prefix

# Example usage
words = ["flower", "flow", "flight"]
print(longest_common_prefix(words))  # Output: "fl"

Time Complexity: O(S), where S is the sum of all characters in all strings.

Space Complexity: O(1), only storing the prefix.

Approach 2: Vertical Scanning

Instead of scanning string by string, we check each character index across all strings.


def longest_common_prefix_vertical(strs):
    if not strs:
        return ""
    
    for i in range(len(strs[0])):
        char = strs[0][i]
        for string in strs[1:]:
            if i >= len(string) or string[i] != char:
                return strs[0][:i]
    return strs[0]

# Example usage
words = ["dog", "racecar", "car"]
print(longest_common_prefix_vertical(words))  # Output: ""

This approach is efficient when strings are short and few.

Approach 3: Divide and Conquer

For larger datasets, divide the list of strings into two halves, find the LCP in each half, and merge results recursively.


def longest_common_prefix_divide(strs):
    if not strs:
        return ""
    
    def lcp(left, right):
        if left == right:
            return strs[left]
        else:
            mid = (left + right) // 2
            lcp_left = lcp(left, mid)
            lcp_right = lcp(mid + 1, right)
            min_length = min(len(lcp_left), len(lcp_right))
            for i in range(min_length):
                if lcp_left[i] != lcp_right[i]:
                    return lcp_left[:i]
            return lcp_left[:min_length]
    
    return lcp(0, len(strs) - 1)

# Example usage
words = ["interview", "internet", "internal"]
print(longest_common_prefix_divide(words))  # Output: "inte"

This approach is highly efficient for large lists of long strings.

Frequently Asked Questions (FAQ)

Q1. Can the list of strings be empty?

Yes, in that case, the LCP is simply "".

Q2. What if there’s no common prefix?

Return an empty string "".

Q3. Is this problem only for Python?

No, LCP is language-agnostic, but Python’s string methods make it simpler.

Q4. How is LCP used in real-world applications?

Autocomplete systems, DNA sequence analysis, and search optimization frequently use LCP.

1. The Classic LCP Problem

This is the most common format of the Longest Common Prefix (LCP) problem asked in coding interviews.

Problem Statement

Write a function to find the longest common prefix string amongst an array of strings. If there is no common prefix, return an empty string "".

Example

Input:

["flower","flow","flight"]

Output:

"fl"

What Interviewers Check

Your understanding of strings and arrays
How efficiently you find the common prefix
Edge case handling (empty strings, no common prefix, etc.)

2. Variants You May See in Interviews

Variant 1: LCP in Sorted Arrays

Given a list of strings, find the longest common prefix using the fact that the array is sorted alphabetically.

Example

Input:

["apple", "apricot", "banana"]

Output:

"ap"

Here, "ap" is the common prefix for the first two strings.

Tip:
You only need to compare the first and last strings in a sorted array. This is faster than comparing all strings.

Variant 2: Prefix Count

Count how many strings share the longest common prefix.

Example

Input:

["dog","dove","door"]

Output:

Here, the prefix "do" appears in "dog" and "dove".

Tip:
Interviewers want to see how you combine LCP logic with counting logic.

Variant 3: LCP in Matrix of Strings

Each row in a matrix is a string. Find the LCP vertically and horizontally.

Example

[
  "flower",
  "flow",
  "flight"
]

Output:

"fl"

The longest common prefix vertically is "fl".

Tip:
This variant tests your multi-dimensional thinking.

Variant 4: Real-Time LCP Updates

You are continuously receiving strings in a stream. Return the LCP after each new string.

Example

Stream:

["interview"]

Output:

"interview"

Next Input:

["internet"]

Output:

"inte"

Tip:
This tests efficient incremental updates. The horizontal scanning approach works very well here.

3. Interviewer Expectations

Handle Edge Cases

Empty list []
Empty strings ["","abc"]
No common prefix

Discuss Multiple Approaches

Horizontal scanning
Vertical scanning
Divide and conquer
Trie (advanced approach)

Analyze Time and Space Complexity

Simple solutions: O(S), where S is the sum of all characters
Trie-based solutions: O(N × M), where N is number of strings and M is average length

Explain Your Thought Process

Even if your code is correct, clearly explaining why your solution works leaves a strong impression on interviewers.

✅ Interviewer Tips

Always handle edge cases (empty strings, no prefix)
Discuss multiple approaches and justify your choice
Analyze time and space complexity clearly
Explain your thought process aloud
Optimize for large datasets using Trie or divide-and-conquer

💡 Pro Tip

Interviewers often start with a small example and then ask you to optimize it for large datasets. Always think about scalability and be ready to discuss optimizations.

Final Thoughts

Finding the Longest Common Prefix is a beginner-friendly yet powerful coding challenge. By practicing it regularly as part of your 1000 Days of Python Coding Challenges, you’ll strengthen your problem-solving, string manipulation, and algorithmic thinking skills.

Python’s simple syntax allows you to experiment with multiple approaches and choose the most efficient solution for your use case.

Longest Common Prefix in Python