Aggregation with agg() in Pandas - Mini Project: Build & Apply

Aggregation with agg()

📖 Scenario: You work in a small store that sells fruits. You have a list of sales data with fruit names, quantities sold, and prices. You want to find out some summary information like total quantity sold and average price for each fruit.

🎯 Goal: Build a program that uses pandas to group the sales data by fruit and then uses agg() to calculate total quantity sold and average price for each fruit.

📋 What You'll Learn

Create a pandas DataFrame with the given sales data

Create a grouping variable for the fruit column

Use agg() to calculate total quantity and average price per fruit

Print the resulting aggregated DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize sales data to understand which products sell the most and at what average price.

💼 Career

Data analysts and scientists use aggregation functions like <code>agg()</code> to quickly get insights from large datasets.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with these exact columns and values: fruit with ['apple', 'banana', 'apple', 'banana', 'orange'], quantity with [10, 5, 8, 7, 3], and price with [0.5, 0.3, 0.55, 0.35, 0.8].

Pandas

import pandas as pd
# Create the sales DataFrame with the given data
# Your code here

Need a hint?

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

2

Group the data by fruit

Create a variable called grouped that groups the sales DataFrame by the fruit column using groupby().

Pandas

import pandas as pd
sales = pd.DataFrame({
    'fruit': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
# Group the sales DataFrame by the fruit column
# Your code here

Need a hint?

Use sales.groupby('fruit') to group rows by fruit.

3

Aggregate total quantity and average price

Create a variable called summary that uses agg() on grouped to calculate the total of quantity using 'sum' and the average of price using 'mean'.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'fruit': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
grouped = sales.groupby('fruit')
# Use agg() to get total quantity and average price per fruit
# Your code here

Need a hint?

Use agg({'quantity': 'sum', 'price': 'mean'}) on the grouped object.

4

Print the aggregated summary

Write a print statement to display the summary DataFrame.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'fruit': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
grouped = sales.groupby('fruit')
summary = grouped.agg({'quantity': 'sum', 'price': 'mean'})
# Print the summary DataFrame
# Your code here

Need a hint?

Use print(summary) to show the aggregated results.