How do you solve Unique Email Addresses on LeetCode?

Unique Email Addresses (LeetCode #929) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: The local part of an email can be modified by removing dots and ignoring characters after a plus sign.

What is the brute force solution for Unique Email Addresses?

The brute force approach for Unique Email Addresses is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we will check each email address individually and apply the rules for dots and plus signs. This will allow us to generate the canonical form of each email and count unique addresses. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Unique Email Addresses?

Unique Email Addresses uses the following concepts: Array, Hash Table, String. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Unique Email Addresses?

Always clarify the rules of email processing before jumping into coding. Consider edge cases, such as emails without a plus sign or those with multiple dots. Explain your thought process clearly while coding; it helps the interviewer understand your approach.

What are common mistakes when solving Unique Email Addresses?

Not handling the plus sign correctly, leading to incorrect local name processing. Forgetting to remove dots from the local part before adding to the set.

#929

Unique Email Addresses

Easy

ArrayHash TableStringHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

The optimal solution uses a HashSet to store unique email addresses directly, allowing us to efficiently check for duplicates as we process each email.

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Algorithm

5 steps

1Step 1: Initialize a HashSet to store unique email addresses.
2Step 2: For each email, split it into local and domain parts using '@'.
3Step 3: Remove all dots from the local part and ignore everything after the first plus sign.
4Step 4: Add the processed email directly to the HashSet.
5Step 5: Return the size of the HashSet as the count of unique email addresses.

solution.py9 lines

1def numUniqueEmails(emails):
2    unique_emails = set()
3    for email in emails:
4        local, domain = email.split('@')
5        local = local.replace('.', '')
6        if '+' in local:
7            local = local.split('+')[0]
8        unique_emails.add(local + '@' + domain)
9    return len(unique_emails)

ℹ

Complexity note: The complexity is O(n) because we process each email once and use a HashSet to store unique emails, which allows for average O(1) time complexity for insertions.

1The local part of an email can be modified by removing dots and ignoring characters after a plus sign.
2Using a HashSet allows for efficient storage and retrieval of unique email addresses.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.