Why grouping data matters in Pandas - See It in Action

Why grouping data matters

📖 Scenario: You work at a small bookstore. You have sales data for different books sold on different days. You want to find out how many books were sold in total for each book title.

🎯 Goal: Build a simple program that groups sales data by book title and sums the number of books sold for each title.

📋 What You'll Learn

Create a dictionary with book titles as keys and number of books sold as values for each sale.

Create a list of dictionaries representing sales records with keys 'title' and 'copies_sold'.

Use a variable to hold the sales data list.

Use pandas to convert the sales data into a DataFrame.

Group the DataFrame by book title and sum the copies sold.

Print the grouped and summed result.

💡 Why This Matters

🌍 Real World

Bookstores and many businesses track sales data and group it by product to see which items sell best.

💼 Career

Data analysts and scientists often group data to summarize and find insights from large datasets.

1

Create sales data list

Create a list called sales_data with these exact dictionaries: {'title': 'Python Basics', 'copies_sold': 5}, {'title': 'Data Science 101', 'copies_sold': 3}, {'title': 'Python Basics', 'copies_sold': 2}, {'title': 'Machine Learning', 'copies_sold': 4}

Pandas

# Create the sales_data list with the given dictionaries
# Your code here

Need a hint?

Use a list with dictionaries. Each dictionary has keys 'title' and 'copies_sold' with the exact values given.

2

Import pandas and create DataFrame

Import the pandas library as pd. Then create a DataFrame called df from the sales_data list.

Pandas

sales_data = [
    {'title': 'Python Basics', 'copies_sold': 5},
    {'title': 'Data Science 101', 'copies_sold': 3},
    {'title': 'Python Basics', 'copies_sold': 2},
    {'title': 'Machine Learning', 'copies_sold': 4}
]

# Import pandas as pd and create DataFrame df from sales_data
# Your code here

Need a hint?

Use import pandas as pd and then pd.DataFrame() with sales_data.

3

Group by title and sum copies sold

Create a variable called grouped_sales that groups df by the 'title' column and sums the 'copies_sold' for each group.

Pandas

import pandas as pd

sales_data = [
    {'title': 'Python Basics', 'copies_sold': 5},
    {'title': 'Data Science 101', 'copies_sold': 3},
    {'title': 'Python Basics', 'copies_sold': 2},
    {'title': 'Machine Learning', 'copies_sold': 4}
]

df = pd.DataFrame(sales_data)

# Group df by 'title' and sum 'copies_sold' into grouped_sales
# Your code here

Need a hint?

Use df.groupby('title')['copies_sold'].sum() to get total copies sold per title.

4

Print the grouped sales result

Print the grouped_sales variable to show total copies sold for each book title.

Pandas

import pandas as pd

sales_data = [
    {'title': 'Python Basics', 'copies_sold': 5},
    {'title': 'Data Science 101', 'copies_sold': 3},
    {'title': 'Python Basics', 'copies_sold': 2},
    {'title': 'Machine Learning', 'copies_sold': 4}
]

df = pd.DataFrame(sales_data)

grouped_sales = df.groupby('title')['copies_sold'].sum()

# Print grouped_sales
# Your code here

Need a hint?

Use print(grouped_sales) to display the result.