Problem

Table: Employees

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| id            | int     |
| name          | varchar |
+---------------+---------+

id is the primary key (column with unique values) for this table. Each row of this table contains the id and the name of an employee in a company.

Table: EmployeeUNI

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| id            | int     |
| unique_id     | int     |
+---------------+---------+

(id, unique_id) is the primary key (combination of columns with unique values) for this table. Each row of this table contains the id and the corresponding unique id of an employee in the company.

Write a solution to show the unique ID of each user, If a user does not have a unique ID replace just show null.

Return the result table in any order.

The result format is in the following example.

Examples

Example 1:

Input: Employees table:

+----+----------+
| id | name     |
+----+----------+
| 1  | Alice    |
| 7  | Bob      |
| 11 | Meir     |
| 90 | Winston  |
| 3  | Jonathan |
+----+----------+

EmployeeUNI table:

+----+-----------+
| id | unique_id |
+----+-----------+
| 3  | 1         |
| 11 | 2         |
| 90 | 3         |
+----+-----------+

Output:

+-----------+----------+
| unique_id | name     |
+-----------+----------+
| null      | Alice    |
| null      | Bob      |
| 2         | Meir     |
| 3         | Winston  |
| 1         | Jonathan |
+-----------+----------+

Explanation: Alice and Bob do not have a unique ID, We will show null instead. The unique ID of Meir is 2. The unique ID of Winston is 3. The unique ID of Jonathan is 1.

Solution

Method 1 - Left Join

Code

SQL
SELECT unique_id, name FROM employees a
LEFT JOIN employeeUNI b on a.id = b.id
Python
  • Merge the DataFrames using the ‘id’ column and keep only necessary columns
  • Select the columns ‘unique_id’ and ’name’ from the merged DataFrame
import pandas as pd

def replace_employee_id(employees: pd.DataFrame, employee_uni: pd.DataFrame) -> pd.DataFrame:
    merged_df = pd.merge(employees, employee_uni, how='left', on='id')
    result_df = merged_df[['unique_id', 'name']]
    
    return result_df