groupby() basics in Pandas - Mini Project: Build & Apply

Using groupby() Basics in pandas

📖 Scenario: You work in a small store. You have a list of sales with product names and amounts sold. You want to find out how many units were sold for each product.

🎯 Goal: Build a small program that groups sales by product name and sums the amounts sold for each product using groupby() in pandas.

📋 What You'll Learn

Create a pandas DataFrame with sales data

Create a variable for grouping by product

Use groupby() and sum() to get total sales per product

Print the grouped result

💡 Why This Matters

🌍 Real World

Grouping data by categories and summarizing is common in sales, finance, and many fields to understand totals or averages.

💼 Career

Data analysts and scientists often use <code>groupby()</code> in pandas to prepare data for reports and insights.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with two columns: 'product' and 'amount'. Use these exact rows: 'apple', 10, 'banana', 5, 'apple', 7, 'banana', 3, 'orange', 8.

Pandas

import pandas as pd
# Create the sales DataFrame with product and amount columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing the two columns and their values.

2

Set the grouping variable

Create a variable called group_column and set it to the string 'product'. This will be used to group the sales data by product.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'amount': [10, 5, 7, 3, 8]
})

# Create the group_column variable with value 'product'
# Your code here

Need a hint?

Just assign the string 'product' to the variable group_column.

3

Group and sum the sales

Create a variable called grouped_sales that groups the sales DataFrame by group_column and sums the amount for each product.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'amount': [10, 5, 7, 3, 8]
})
group_column = 'product'

# Group sales by group_column and sum the amount
# Your code here

Need a hint?

Use sales.groupby(group_column)['amount'].sum() to get total sales per product.

4

Print the grouped sales result

Print the grouped_sales variable to show the total amount sold for each product.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'amount': [10, 5, 7, 3, 8]
})
group_column = 'product'
grouped_sales = sales.groupby(group_column)['amount'].sum()

# Print the grouped_sales variable
# Your code here

Need a hint?

Use print(grouped_sales) to display the result.