Left and right joins in Data Analysis Python - Mini Project: Build & Apply

Left and Right Joins in Data Analysis

📖 Scenario: You work in a small store that keeps two lists: one for products and one for sales. You want to combine these lists to see which products sold and which did not.

🎯 Goal: Learn how to use left join and right join to combine two tables (dataframes) in Python using pandas. You will create two dataframes, set a join key, and then join them to see all products with their sales and all sales with their product details.

📋 What You'll Learn

Create two pandas DataFrames with exact data

Create a variable for the join key column name

Use pandas merge with how='left' and how='right' to join dataframes

Print the joined DataFrames to see the results

💡 Why This Matters

🌍 Real World

Stores and businesses often have separate lists for products and sales. Joining these lists helps understand which products sold and which did not.

💼 Career

Data analysts and scientists use joins to combine data from different sources to create meaningful reports and insights.

1

Create product and sales DataFrames

Import pandas as pd. Create a DataFrame called products with columns 'product_id' and 'product_name' containing these rows: (1, 'Apple'), (2, 'Banana'), (3, 'Carrot'). Create another DataFrame called sales with columns 'product_id' and 'quantity' containing these rows: (1, 10), (2, 5), (4, 7).

Data Analysis Python

import pandas as pd
# Create the products DataFrame
# Create the sales DataFrame

Hint

Use pd.DataFrame with a dictionary of lists to create each DataFrame.

2

Set the join key variable

Create a variable called join_key and set it to the string 'product_id'. This will be the column used to join the two DataFrames.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'product_id': [1, 2, 3],
    'product_name': ['Apple', 'Banana', 'Carrot']
})
sales = pd.DataFrame({
    'product_id': [1, 2, 4],
    'quantity': [10, 5, 7]
})
# Create the join_key variable

Hint

Just assign the string 'product_id' to the variable join_key.

3

Perform left and right joins

Use pd.merge to create a DataFrame called left_joined by joining products and sales on join_key with how='left'. Then create another DataFrame called right_joined by joining products and sales on join_key with how='right'.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'product_id': [1, 2, 3],
    'product_name': ['Apple', 'Banana', 'Carrot']
})
sales = pd.DataFrame({
    'product_id': [1, 2, 4],
    'quantity': [10, 5, 7]
})
join_key = 'product_id'
# Create left_joined using pd.merge with how='left'
# Create right_joined using pd.merge with how='right'

Hint

Use pd.merge() with the on parameter set to join_key and how set to 'left' or 'right'.

4

Print the joined DataFrames

Print the left_joined DataFrame. Then print the right_joined DataFrame.

Data Analysis Python

import pandas as pd
products = pd.DataFrame({
    'product_id': [1, 2, 3],
    'product_name': ['Apple', 'Banana', 'Carrot']
})
sales = pd.DataFrame({
    'product_id': [1, 2, 4],
    'quantity': [10, 5, 7]
})
join_key = 'product_id'
left_joined = pd.merge(products, sales, on=join_key, how='left')
right_joined = pd.merge(products, sales, on=join_key, how='right')
# Print left_joined
# Print right_joined

Hint

Use print() to show both DataFrames.