Longest Common Prefix in DSA Python - Time & Space Complexity

Time Complexity: Longest Common Prefix

O(n * m)

Understanding Time Complexity

We want to know how the time needed to find the longest common prefix grows as we add more words or longer words.

How does the work increase when the input list or word length grows?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

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

This code finds the longest common prefix string among a list of strings by comparing and shortening the prefix as needed.

Identify Repeating Operations

• Primary operation: Checking if each string starts with the current prefix and shortening the prefix.
• How many times: For each string, the prefix may be shortened multiple times, up to the length of the prefix.

How Execution Grows With Input

As the number of strings (n) or the length of the prefix (m) grows, the work increases roughly by n times m.

Input Size (n strings, m length)	Approx. Operations
10 strings, prefix length 5	About 50 checks
100 strings, prefix length 10	About 1000 checks
1000 strings, prefix length 20	About 20,000 checks

Pattern observation: The operations grow roughly by multiplying the number of strings by the prefix length.

Final Time Complexity

Time Complexity: O(n * m)

This means the time grows proportionally with the number of strings and the length of the prefix we check.

Common Mistake

[X] Wrong: "The time is just O(n) because we only loop through the list once."

[OK] Correct: Each string comparison can take up to the length of the prefix, so the inner work depends on prefix length too.

Interview Connect

Understanding this helps you explain how nested checks affect performance and shows you can analyze combined factors, a key skill in interviews.

Self-Check

"What if we sorted the strings first and only compared the first and last strings? How would the time complexity change?"

Practice

1. What does the Longest Common Prefix (LCP) problem ask you to find given a list of strings?

easy

A. The total number of strings in the list

B. The longest starting substring common to all strings

C. The longest substring common to any two strings

D. The longest ending substring common to all strings

Solution

1. Step 1: Understand the problem definition

   The Longest Common Prefix means the longest substring at the start of all strings that is the same.

2. Step 2: Compare options with the definition

   Only The longest starting substring common to all strings matches this definition exactly.

3. Final Answer:

   The longest starting substring common to all strings -> Option B

4. Quick Check:

   Longest Common Prefix = longest starting substring [OK]

Hint: Focus on the start of strings, not anywhere else [OK]

Common Mistakes:

• Confusing prefix with suffix
• Thinking about any substring instead of prefix
• Counting total strings instead of prefix length

2. Which of the following Python code snippets correctly initializes a variable to store the longest common prefix as an empty string?

easy

A. lcp = None

B. lcp = []

C. lcp = ''

D. lcp = 0

Solution

1. Step 1: Identify the correct data type for prefix storage

   The longest common prefix is a string, so it should be initialized as an empty string.

2. Step 2: Match initialization with data type

   lcp = '' initializes lcp as an empty string, which is correct.

3. Final Answer:

   lcp = '' -> Option C

4. Quick Check:

   Prefix is a string, so empty string initialization [OK]

Hint: Use empty string '' to start prefix, not list or number [OK]

Common Mistakes:

• Using list [] instead of string
• Using None which is not a string
• Using 0 which is a number, not string

3. What will be the output of the following Python code?

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

print(longest_common_prefix(['flower', 'flow', 'flight']))

medium

A. fl

B. flow

C. f

D. flower

Solution

1. Step 1: Initialize prefix and iterate over strings

   Prefix starts as 'flower'. Check 'flow': 'flow' does not start with 'flower', so remove last char to 'flowe', then 'flow', which matches.

2. Step 2: Check next string 'flight'

   'flight' does not start with 'flow', remove last char to 'flo', no match; then 'fl', which matches.

3. Final Answer:

   fl -> Option A

4. Quick Check:

   Common start of all strings = 'fl' [OK]

Hint: Shrink prefix until all strings start with it [OK]

Common Mistakes:

• Returning first string without checking others
• Not shrinking prefix correctly
• Confusing substring anywhere with prefix

4. The following code is intended to find the longest common prefix but has a bug. What is the bug?

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

print(longest_common_prefix(['dog', 'racecar', 'car']))

medium

A. No return statement inside the loop

B. Loop starts from first string causing incorrect prefix update

C. Using s.startswith(prefix) instead of prefix.startswith(s)

D. Missing check for empty prefix causing infinite loop

Solution

1. Step 1: Analyze loop and prefix shrinking

   The code shrinks prefix until s starts with prefix, but does not check if prefix becomes empty.

2. Step 2: Identify infinite loop risk

   If no common prefix exists, prefix will shrink to empty string, then prefix[:-1] stays empty, causing infinite loop.

3. Final Answer:

   Missing check for empty prefix causing infinite loop -> Option D

4. Quick Check:

   Empty prefix check needed to stop loop [OK]

Hint: Always check if prefix is empty to avoid infinite loops [OK]

Common Mistakes:

• Not checking prefix empty condition
• Looping over all strings including first
• Confusing startswith arguments

5. Given a list of strings, how would you efficiently find the longest common prefix using a vertical scanning approach? Which of the following best describes the method?

hard

A. Compare characters column by column across all strings until a mismatch is found

B. Sort the strings and compare only the first and last strings completely

C. Use a hash map to count character frequencies at each position

D. Concatenate all strings and find the longest substring

Solution

1. Step 1: Understand vertical scanning

   Vertical scanning means checking each character position across all strings one by one.

2. Step 2: Identify stopping condition

   Stop when a mismatch or end of any string is found at that position.

3. Final Answer:

   Compare characters column by column across all strings until a mismatch is found -> Option A

4. Quick Check:

   Vertical scanning = column-wise character comparison [OK]

Hint: Check characters by columns, stop at first mismatch [OK]

Common Mistakes:

• Confusing vertical scanning with sorting
• Using hash maps unnecessarily
• Concatenating strings instead of comparing