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Drop Duplicate Rows

Problem Description​

There are some duplicate rows in the DataFrame based on the email column. Write a solution to remove these duplicate rows and keep only the first occurrence. The result format is in the following example.

Examples​

Example 1:

Input:

+-------------+---------+---------------------+
| customer_id | name    | email               |
+-------------+---------+---------------------+
| 1           | Ella    | emily@example.com   |
| 2           | David   | michael@example.com |
| 3           | Zachary | sarah@example.com   |
| 4           | Alice   | john@example.com    |
| 5           | Finn    | john@example.com    |
| 6           | Violet  | alice@example.com   |
+-------------+---------+---------------------+

Output:

+-------------+---------+---------------------+
| customer_id | name    | email               |
+-------------+---------+---------------------+
| 1           | Ella    | emily@example.com   |
| 2           | David   | michael@example.com |
| 3           | Zachary | sarah@example.com   |
| 4           | Alice   | john@example.com    |
| 6           | Violet  | alice@example.com   |
+-------------+---------+---------------------+

Explanation:​

Alic (customer_id = 4) and Finn (customer_id = 5) both use john@example.com, so only the first occurrence of this email is retained.

Intuition​

I will use the pandas library to solve this problem. I will use the drop_duplicates method to remove the duplicate rows based on the email column and keep only the first occurrence.

Solution Code​

Written by @abhay
import pandas as pd

def dropDuplicateEmails(customers: pd.DataFrame) -> pd.DataFrame:
      customers.drop_duplicates(subset='email',keep='first',inplace = True)
      return  customers

References​