Iterating over groups in Pandas - Mini Project: Build & Apply

Iterating over groups

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You have a table of sales data with the bread type and the number of loaves sold each day. You want to analyze the sales by bread type.

🎯 Goal: Build a program that groups the sales data by bread type and iterates over each group to see the sales details.

📋 What You'll Learn

Create a pandas DataFrame with bread sales data

Create a grouping variable using groupby on the bread type

Use a for loop to iterate over the groups

Print the group name and the group data

💡 Why This Matters

🌍 Real World

Grouping data is common in sales, finance, and many fields to analyze subsets of data separately.

💼 Career

Data analysts and scientists often group data to summarize and understand patterns in business data.

1

Create the sales DataFrame

Import pandas as pd and create a DataFrame called sales with these exact columns and values: 'Bread' with values ['Sourdough', 'Baguette', 'Sourdough', 'Baguette', 'Rye'] and 'Loaves' with values [10, 15, 7, 20, 5].

Pandas

# Import pandas and create the sales DataFrame
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary to create the table.

2

Group the sales by bread type

Create a variable called grouped that groups the sales DataFrame by the 'Bread' column using groupby.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Bread': ['Sourdough', 'Baguette', 'Sourdough', 'Baguette', 'Rye'],
    'Loaves': [10, 15, 7, 20, 5]
})
# Group the sales DataFrame by the 'Bread' column
# Your code here

Need a hint?

Use sales.groupby('Bread') to group the data.

3

Iterate over the groups

Use a for loop with variables bread_type and group_data to iterate over grouped. Inside the loop, print the bread_type and the group_data DataFrame.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Bread': ['Sourdough', 'Baguette', 'Sourdough', 'Baguette', 'Rye'],
    'Loaves': [10, 15, 7, 20, 5]
})
grouped = sales.groupby('Bread')
# Iterate over grouped and print bread_type and group_data
# Your code here

Need a hint?

Use for bread_type, group_data in grouped: to loop through groups.

4

Display the grouped sales output

Run the program to print the bread type and the sales data for each group.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Bread': ['Sourdough', 'Baguette', 'Sourdough', 'Baguette', 'Rye'],
    'Loaves': [10, 15, 7, 20, 5]
})
grouped = sales.groupby('Bread')
for bread_type, group_data in grouped:
    print(bread_type)
    print(group_data)
# Run the program to see the output

Need a hint?

Check the console output to see each bread type and its sales rows.