Drop Duplicate Rows

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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.

## Solution

### Method 1 – pandas drop_duplicates

#### Intuition

To remove duplicate rows based on the `email` column and keep only the first occurrence, we can use the `drop_duplicates` method in pandas, specifying the `email` column and `keep='first'`.

#### Approach

1. Use `drop_duplicates` on the DataFrame, specifying `subset=['email']` and `keep='first'`.
2. Return the resulting DataFrame, sorted by `customer_id` if required.

#### Code


    

         
         
         
        
            
            
        
            
            
                 
                 
                
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import pandas as pd

def drop_duplicate_rows(customers: pd.DataFrame) -> pd.DataFrame:
    return customers.drop_duplicates(subset=['email'], keep='first')


        
        
        
         
        
        
    



#### Complexity

- ⏰ Time complexity: `O(n)`, where n is the number of rows in the DataFrame.
- 🧺 Space complexity: `O(n)`, for the output DataFrame.