agg() for multiple aggregations in Data Analysis Python - Mini Project: Build & Apply

Using agg() for Multiple Aggregations in Data Science

📖 Scenario: You work in a small grocery store. You have sales data for different fruits sold in the last week. You want to find out some useful information like the total quantity sold, the average price, and the highest price for each fruit.

🎯 Goal: Build a program that uses the agg() function to calculate multiple summary statistics (total quantity, average price, maximum price) for each fruit in the sales data.

📋 What You'll Learn

Create a pandas DataFrame with fruit sales data including columns: 'Fruit', 'Quantity', and 'Price'.

Create a variable to hold the aggregation functions using agg().

Use groupby() on the 'Fruit' column and apply agg() to calculate total quantity, average price, and maximum price.

Print the resulting aggregated DataFrame.

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize sales or inventory data by categories to make decisions.

💼 Career

Data analysts and scientists use groupby and agg functions to quickly get insights from large datasets.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales with these exact columns and values: 'Fruit' with ['Apple', 'Banana', 'Apple', 'Banana', 'Orange'], 'Quantity' with [10, 5, 7, 3, 8], and 'Price' with [0.5, 0.3, 0.55, 0.35, 0.8].

Data Analysis Python

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

Hint

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

2

Create the aggregation configuration

Create a variable called agg_funcs that holds a dictionary to specify these aggregations: 'Quantity' should use 'sum', and 'Price' should use ['mean', 'max'].

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Orange'],
    'Quantity': [10, 5, 7, 3, 8],
    'Price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
# Create agg_funcs dictionary for Quantity sum and Price mean and max
# Your code here

Hint

Use a dictionary where keys are column names and values are aggregation functions or lists of functions.

3

Group by fruit and apply multiple aggregations

Use groupby() on the sales DataFrame by the 'Fruit' column and apply agg() with the agg_funcs dictionary. Store the result in a variable called summary.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Orange'],
    'Quantity': [10, 5, 7, 3, 8],
    'Price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
agg_funcs = {'Quantity': 'sum', 'Price': ['mean', 'max']}
# Group sales by 'Fruit' and apply agg_funcs, save to summary
# Your code here

Hint

Use sales.groupby('Fruit').agg(agg_funcs) to get the grouped summary.

4

Print the aggregated summary

Print the summary DataFrame to display the total quantity, average price, and maximum price for each fruit.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Orange'],
    'Quantity': [10, 5, 7, 3, 8],
    'Price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
agg_funcs = {'Quantity': 'sum', 'Price': ['mean', 'max']}
summary = sales.groupby('Fruit').agg(agg_funcs)
# Print the summary DataFrame
# Your code here

Hint

Use print(summary) to show the grouped aggregation results.