Right join behavior in Pandas - Mini Project: Build & Apply

Understanding Right Join Behavior with pandas

📖 Scenario: You are working with two small tables representing employees and their departments in a company. You want to combine these tables to see all departments and the employees assigned to them, including departments that currently have no employees.

🎯 Goal: Build a pandas DataFrame merge using a right join to combine employee and department data, showing all departments and matching employees.

📋 What You'll Learn

Create a pandas DataFrame called employees with columns EmployeeID, Name, and DeptID using the exact data provided.

Create a pandas DataFrame called departments with columns DeptID and DeptName using the exact data provided.

Create a variable called how_join and set it to the string 'right' to specify the join type.

Use the pandas merge function to join employees and departments on the DeptID column using the how_join variable.

Store the result in a variable called right_joined.

💡 Why This Matters

🌍 Real World

Right joins are useful when you want to keep all records from a reference table (like departments) and add matching data from another table (like employees), even if some departments have no employees yet.

💼 Career

Data analysts and data scientists often use right joins to combine datasets for reporting and analysis, ensuring no important reference data is lost.

1

Create the employees DataFrame

Create a pandas DataFrame called employees with these exact rows and columns: EmployeeID with values 1, 2, 3; Name with values 'Alice', 'Bob', 'Charlie'; and DeptID with values 10, 20, 30.

Pandas

import pandas as pd
# Create the employees DataFrame with EmployeeID, Name, and DeptID columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where keys are column names and values are lists of column data.

2

Create the departments DataFrame

Create a pandas DataFrame called departments with these exact rows and columns: DeptID with values 10, 20, 30, 40; and DeptName with values 'HR', 'Finance', 'IT', 'Marketing'.

Pandas

import pandas as pd
employees = pd.DataFrame({
    'EmployeeID': [1, 2, 3],
    'Name': ['Alice', 'Bob', 'Charlie'],
    'DeptID': [10, 20, 30]
})
# Create the departments DataFrame with DeptID and DeptName columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary for the departments data.

3

Set the join type variable how_join

Create a variable called how_join and set it to the string 'right' to specify the type of join for merging.

Pandas

import pandas as pd
employees = pd.DataFrame({
    'EmployeeID': [1, 2, 3],
    'Name': ['Alice', 'Bob', 'Charlie'],
    'DeptID': [10, 20, 30]
})
departments = pd.DataFrame({
    'DeptID': [10, 20, 30, 40],
    'DeptName': ['HR', 'Finance', 'IT', 'Marketing']
})
# Create a variable how_join and set it to 'right'
# Your code here

Need a hint?

Assign the string 'right' to the variable how_join.

4

Perform the right join merge

Use the pandas merge function to join employees and departments on the DeptID column using the how_join variable. Store the result in a variable called right_joined.

Pandas

import pandas as pd
employees = pd.DataFrame({
    'EmployeeID': [1, 2, 3],
    'Name': ['Alice', 'Bob', 'Charlie'],
    'DeptID': [10, 20, 30]
})
departments = pd.DataFrame({
    'DeptID': [10, 20, 30, 40],
    'DeptName': ['HR', 'Finance', 'IT', 'Marketing']
})
how_join = 'right'
# Merge employees and departments on DeptID using how_join and store in right_joined
# Your code here

Need a hint?

Use pd.merge() with on='DeptID' and how=how_join.