DataFrame customers
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| customer_id | int    |
| name        | object |
| email       | object |
+-------------+--------+

El formato del resultado se muestra en el siguiente ejemplo.

Ejemplo 1:

Entrada:

+-------------+---------+---------------------+
| 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   |
+-------------+---------+---------------------+

Salida:

+-------------+---------+---------------------+
| 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   |
+-------------+---------+---------------------+

Explicación:
- Alice (customer_id = 4) y Finn (customer_id = 5) comparten el mismo correo 'john@example.com', de manera que
  solo Alice se conserva en el resultado final.

Solución

import pandas as pd


def dropDuplicateEmails(customers: pd.DataFrame) -> pd.DataFrame:
    return customers.drop_duplicates('email',
                                     ignore_index=True)