Multiple aggregation functions in Pandas - Mini Project: Build & Apply

Using Multiple Aggregation Functions with pandas

📖 Scenario: You work in a small store and have a list of sales data for different products. You want to learn how to find useful summary information like the total sales, average sales, and the highest sale for each product.

🎯 Goal: Build a pandas DataFrame with sales data, then use multiple aggregation functions to find the total, average, and maximum sales for each product.

📋 What You'll Learn

Create a pandas DataFrame with product sales data

Create a list of aggregation functions

Use the agg() method with multiple aggregation functions on grouped data

Print the final aggregated DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize sales data to understand performance by product or category.

💼 Career

Data analysts and scientists use grouping and aggregation to prepare reports and find insights from data.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and rows:
Product: 'Apple', 'Banana', 'Apple', 'Banana', 'Apple'
Sales: 10, 15, 7, 20, 5

Pandas

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

Need a hint?

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

2

Create a list of aggregation functions

Create a list called agg_functions that contains the strings 'sum', 'mean', and 'max' to use for aggregation.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Sales': [10, 15, 7, 20, 5]
})
# Create the agg_functions list
# Your code here

Need a hint?

Make a list with the exact strings 'sum', 'mean', and 'max'.

3

Group by product and apply multiple aggregation functions

Use sales_data.groupby('Product')['Sales'].agg(agg_functions) and save the result in a variable called summary.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Sales': [10, 15, 7, 20, 5]
})
agg_functions = ['sum', 'mean', 'max']
# Group by 'Product' and aggregate 'Sales' using agg_functions
# Your code here

Need a hint?

Use groupby('Product') on sales_data, select the 'Sales' column, then call agg(agg_functions).

4

Print the aggregated summary

Print the variable summary to display the total, average, and maximum sales for each product.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Sales': [10, 15, 7, 20, 5]
})
agg_functions = ['sum', 'mean', 'max']
summary = sales_data.groupby('Product')['Sales'].agg(agg_functions)
# Print the summary DataFrame
# Your code here

Need a hint?

Use print(summary) to show the results.