Grouping by multiple columns in Pandas - Mini Project: Build & Apply

Grouping Data by Multiple Columns with pandas

📖 Scenario: You work in a small store that sells different types of fruits in various cities. You have a list of sales records showing the city, fruit type, and quantity sold. You want to find out how many fruits were sold for each fruit type in each city.

🎯 Goal: Build a pandas DataFrame with sales data, then group the data by City and Fruit columns to find the total quantity sold for each group.

📋 What You'll Learn

Create a pandas DataFrame named sales_data with columns City, Fruit, and Quantity using the exact data provided.

Create a variable named group_columns that holds a list of the column names 'City' and 'Fruit'.

Use the groupby method on sales_data with group_columns and sum the Quantity for each group, saving the result in a variable named grouped_sales.

Print the grouped_sales DataFrame to display the total quantity sold for each fruit in each city.

💡 Why This Matters

🌍 Real World

Grouping data by multiple columns is common in sales analysis, customer segmentation, and many business reports where you want to summarize data by categories.

💼 Career

Data analysts and data scientists often use grouping to aggregate data and find insights from large datasets in fields like marketing, finance, and operations.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact rows and columns: City, Fruit, and Quantity. Use the data: ('New York', 'Apple', 10), ('New York', 'Banana', 5), ('Los Angeles', 'Apple', 7), ('Los Angeles', 'Banana', 3), ('New York', 'Apple', 4).

Pandas

import pandas as pd
# Create the sales_data DataFrame with the exact data
# Your code here

Need a hint?

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

2

Create the grouping columns list

Create a variable called group_columns and set it to a list containing the strings 'City' and 'Fruit'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'New York'],
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Quantity': [10, 5, 7, 3, 4]
})
# Create the group_columns list with 'City' and 'Fruit'
# Your code here

Need a hint?

Use square brackets to create a list with the two column names as strings.

3

Group the data and sum quantities

Use the groupby method on sales_data with group_columns and sum the Quantity column for each group. Save the result in a variable called grouped_sales.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'New York'],
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Quantity': [10, 5, 7, 3, 4]
})
group_columns = ['City', 'Fruit']
# Group sales_data by group_columns and sum Quantity, save as grouped_sales
# Your code here

Need a hint?

Use sales_data.groupby(group_columns)['Quantity'].sum() to group and sum.

4

Print the grouped sales result

Print the grouped_sales variable to display the total quantity sold for each fruit in each city.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'New York'],
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Quantity': [10, 5, 7, 3, 4]
})
group_columns = ['City', 'Fruit']
grouped_sales = sales_data.groupby(group_columns)['Quantity'].sum()
# Print grouped_sales to show the result
# Your code here

Need a hint?

Use print(grouped_sales) to display the grouped sums.