Pandas LeetCode Guide: Solving Duplicate Email Problems Efficiently

This article delves into solving the “Delete Duplicate Emails” problem on LeetCode, a common challenge for those honing their data manipulation skills. We’ll explore efficient solutions using Pandas, a powerful Python library indispensable for data analysis, and also touch upon a SQL approach for comparison. This problem is marked as ‘easy’ on LeetCode, making it an excellent starting point for mastering data operations with Pandas in a coding interview context.

The problem statement from LeetCode is straightforward:

Write a solution to delete all duplicate emails, keeping only one unique email with the smallest id.

For Pandas users, you are expected to modify the DataFrame Person in place.

The Person table schema is as follows:

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| id          | int     |
| email       | varchar |
+-------------+---------+
id is the primary key for this table.
Each row contains an email, with no uppercase letters.

Efficient Pandas Solution

Pandas is renowned for its intuitive and efficient functions for data manipulation. For this particular problem, the drop_duplicates() function is perfectly suited. The problem requires us to remove duplicate emails while retaining the entry with the lowest id. To achieve this using Pandas, we can follow these steps:

First, ensure the DataFrame is sorted by the id column. This step is crucial because we want to keep the row with the smallest id. By sorting in ascending order of id, the first occurrence of a duplicate email will naturally have the smallest id. We use sort_values() for this, and importantly, apply inplace=True as per the problem’s requirement to modify the DataFrame directly.

def delete_duplicate_emails(person: pd.DataFrame) -> None:
    person.sort_values(by='id', inplace=True)

Next, we utilize the drop_duplicates() function. We specify the subset as 'email' because we want to identify and remove duplicates based on email addresses. The keep='first' argument is key here. It instructs Pandas to keep the first occurrence of each unique email (which, due to our previous sorting, will be the one with the smallest id) and drop all subsequent duplicates. Again, inplace=True ensures the changes are applied directly to the person DataFrame.

    person.drop_duplicates(subset='email', keep='first', inplace=True)

Combining these two steps, the complete Pandas solution becomes concise and readable:

def delete_duplicate_emails(person: pd.DataFrame) -> None:
    person.sort_values(by='id', inplace=True)
    person.drop_duplicates(subset='email', keep='first', inplace=True)

This Pandas approach elegantly solves the LeetCode problem by leveraging built-in functionalities for sorting and duplicate removal, highlighting Pandas’ efficiency in data manipulation tasks.

SQL Solution for Comparison

While the focus here is on Pandas, understanding the SQL equivalent provides valuable context and reinforces the underlying logic. A common SQL approach to solve this problem involves using a DELETE statement with a self-join. The idea is to join the person table with itself based on matching email addresses and then delete rows where the id is greater, thus keeping the row with the smaller id for each unique email.

DELETE p1 FROM person p1, person p2
WHERE p1.email = p2.email AND p1.id > p2.id;

This SQL query effectively achieves the same result as the Pandas solution, demonstrating that the core logic of identifying and removing duplicates based on a condition (smaller id) is consistent across different data manipulation tools.

Conclusion

The “Delete Duplicate Emails” LeetCode problem serves as a great exercise for understanding data manipulation techniques in both Pandas and SQL. The Pandas solution, particularly using drop_duplicates(), showcases the library’s power and ease of use for such tasks. Mastering these fundamental operations is crucial for anyone working with data, and practicing on platforms like LeetCode is an excellent way to solidify these skills. Whether you prefer the conciseness of Pandas or the structured query approach of SQL, understanding both perspectives broadens your problem-solving toolkit for data-related challenges.