How do you solve Delete Duplicate Emails on LeetCode?

Delete Duplicate Emails (LeetCode #196) 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: Using a HashMap allows for efficient tracking of duplicates.

What is the brute force solution for Delete Duplicate Emails?

The brute force approach for Delete Duplicate Emails is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking each email against all others to find duplicates. This is straightforward but inefficient, as it requires multiple passes through the data. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Delete Duplicate Emails?

Delete Duplicate Emails uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Delete Duplicate Emails?

Always clarify whether you need to keep the smallest id or just any one duplicate. Discuss your thought process out loud to show your understanding of the problem. Consider edge cases, such as all emails being unique or all being duplicates.

What are common mistakes when solving Delete Duplicate Emails?

Not using a HashMap and resorting to nested loops, leading to inefficiency. Failing to check for the smallest id when encountering duplicates.

#196

Delete Duplicate Emails

Easy

DatabaseHash 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 HashMap to track the smallest id for each email. This allows us to efficiently identify duplicates and keep the one with the smallest id.

⚙️

Algorithm

4 steps

1Step 1: Create a HashMap to store the smallest id for each email.
2Step 2: Iterate through the Person table and populate the HashMap with the email as the key and the smallest id as the value.
3Step 3: Create a list of ids to delete, which are those not in the HashMap's values.
4Step 4: Delete the rows with the ids collected in the previous step.

solution.py12 lines

1# Full working Python code
2import pandas as pd
3
4def delete_duplicates(person):
5    email_map = {}
6    for index, row in person.iterrows():
7        if row['email'] not in email_map:
8            email_map[row['email']] = row['id']
9        else:
10            email_map[row['email']] = min(email_map[row['email']], row['id'])
11    to_delete = person[~person['id'].isin(email_map.values())]
12    person.drop(index=to_delete.index, inplace=True)

ℹ

Complexity note: This complexity is due to the single pass through the Person table and the use of a HashMap for storage, which allows for efficient lookups.

1Using a HashMap allows for efficient tracking of duplicates.
2Always keep track of the smallest id when dealing with duplicates.

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