Aggregation along specific axes in NumPy - Mini Project: Build & Apply

Aggregation along specific axes

📖 Scenario: You work in a small bakery that tracks daily sales of three types of bread over a week. You want to analyze the total sales per bread type and per day to understand which bread is most popular and which day has the highest sales.

🎯 Goal: Build a program that creates a 2D array of sales data, then calculates the total sales per bread type and per day using aggregation along specific axes.

📋 What You'll Learn

Create a 2D numpy array called sales with exact values representing sales of three bread types over seven days.

Create a variable called axis_bread set to 0 to represent aggregation along bread types.

Use np.sum with axis=axis_bread to calculate total sales per day and store it in total_per_day.

Create a variable called axis_day set to 1 to represent aggregation along days.

Use np.sum with axis=axis_day to calculate total sales per bread type and store it in total_per_bread.

Print total_per_day and total_per_bread to show the results.

💡 Why This Matters

🌍 Real World

Aggregating sales data helps businesses understand trends and make decisions about inventory and marketing.

💼 Career

Data analysts and scientists often aggregate data along specific axes to summarize and interpret multi-dimensional datasets.

1

Create the sales data array

Create a 2D numpy array called sales with these exact values representing sales of three bread types over seven days:
[[10, 12, 9, 11, 13, 8, 7], [5, 7, 6, 8, 7, 5, 6], [3, 4, 2, 5, 4, 3, 2]]

NumPy

import numpy as np
# Create the 2D numpy array called sales with the exact values
# Your code here

Need a hint?

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

2

Set axis variables for aggregation

Create a variable called axis_bread and set it to 0 to represent aggregation along bread types (rows). Then create a variable called axis_day and set it to 1 to represent aggregation along days (columns).

NumPy

import numpy as np
sales = np.array([[10, 12, 9, 11, 13, 8, 7], [5, 7, 6, 8, 7, 5, 6], [3, 4, 2, 5, 4, 3, 2]])
# Create axis_bread = 0 and axis_day = 1
# Your code here

Need a hint?

Just assign the numbers 0 and 1 to the variables axis_bread and axis_day.

3

Calculate total sales per day and per bread type

Use np.sum with axis=axis_bread on sales to calculate total sales per day and store it in total_per_day. Then use np.sum with axis=axis_day on sales to calculate total sales per bread type and store it in total_per_bread.

NumPy

import numpy as np
sales = np.array([[10, 12, 9, 11, 13, 8, 7], [5, 7, 6, 8, 7, 5, 6], [3, 4, 2, 5, 4, 3, 2]])
axis_bread = 0
axis_day = 1
# Calculate total_per_day using np.sum with axis=axis_bread
# Calculate total_per_bread using np.sum with axis=axis_day
# Your code here

Need a hint?

Use np.sum with the correct axis variables to get sums along rows or columns.

4

Print the results

Print the variables total_per_day and total_per_bread to display the total sales per day and per bread type.

NumPy

import numpy as np
sales = np.array([[10, 12, 9, 11, 13, 8, 7], [5, 7, 6, 8, 7, 5, 6], [3, 4, 2, 5, 4, 3, 2]])
axis_bread = 0
axis_day = 1
total_per_day = np.sum(sales, axis=axis_bread)
total_per_bread = np.sum(sales, axis=axis_day)
# Print total_per_day and total_per_bread
# Your code here

Need a hint?

Use two print statements, one for each variable.