Why aggregation matters in NumPy - See It in Action

Why aggregation matters

📖 Scenario: Imagine you work in a small grocery store. You have daily sales data for different fruits. You want to understand the total sales for each fruit to know which ones sell the most.

🎯 Goal: You will create a NumPy array with sales data, set a threshold for high sales, calculate total sales for each fruit, and finally print the fruits that have total sales above the threshold.

📋 What You'll Learn

Use NumPy to handle sales data

Create a 2D NumPy array with exact sales numbers

Set a threshold variable for high sales

Calculate total sales per fruit using aggregation

Print the fruits with total sales above the threshold

💡 Why This Matters

🌍 Real World

Stores and businesses use aggregation to understand total sales, helping them decide what products to stock more.

💼 Career

Data analysts and scientists often aggregate data to summarize and find important insights quickly.

1

Create sales data array

Create a NumPy array called sales with these exact daily sales numbers for fruits (rows are days, columns are fruits): [[10, 5, 8], [7, 3, 6], [12, 4, 9]]

NumPy

import numpy as np
# Create the sales array with the exact values
# Your code here

Need a hint?

Use np.array() and pass the list of lists exactly as shown.

2

Set sales threshold

Create a variable called threshold and set it to 20 to represent high total sales.

NumPy

import numpy as np
sales = np.array([[10, 5, 8], [7, 3, 6], [12, 4, 9]])
# Set the threshold variable to 20
# Your code here

Need a hint?

Just assign the number 20 to the variable threshold.

3

Calculate total sales per fruit

Use NumPy's aggregation to create a variable called total_sales that sums sales for each fruit (sum over rows). Use np.sum with axis=0.

NumPy

import numpy as np
sales = np.array([[10, 5, 8], [7, 3, 6], [12, 4, 9]])
threshold = 20
# Calculate total sales per fruit
# Your code here

Need a hint?

Use np.sum with axis=0 to add sales vertically for each fruit.

4

Print fruits with high total sales

Create a list called fruits with these exact names: ["Apples", "Bananas", "Oranges"]. Then print the names of fruits whose total sales in total_sales are greater than threshold. Use a for loop with variables fruit and sale iterating over zip(fruits, total_sales). Print each fruit if sale > threshold.

NumPy

import numpy as np
sales = np.array([[10, 5, 8], [7, 3, 6], [12, 4, 9]])
threshold = 20
total_sales = np.sum(sales, axis=0)
# Create fruits list and print fruits with total sales above threshold
# Your code here

Need a hint?

Use a for loop with zip(fruits, total_sales) and print the fruit name if its sale is greater than threshold.