np.sum() and axis parameter in NumPy - Mini Project: Build & Apply

Summing Data with np.sum() and the axis Parameter

📖 Scenario: Imagine you work at a small store that tracks daily sales of different products. You have sales data for 3 products over 4 days. You want to find total sales for each product and total sales for each day.

🎯 Goal: You will create a 2D numpy array with sales data, then use np.sum() with the axis parameter to calculate total sales per product and per day.

📋 What You'll Learn

Create a numpy array called sales with the exact data given.

Create a variable called axis_0 set to 0 to represent summing over rows (days).

Use np.sum() with axis=axis_0 to find total sales per product.

Print the total sales per product.

Create a variable called axis_1 set to 1 to represent summing over columns (products).

Use np.sum() with axis=axis_1 to find total sales per day.

Print the total sales per day.

💡 Why This Matters

🌍 Real World

Stores and businesses often track sales data in tables and need to quickly find totals by product or by day to understand performance.

💼 Career

Data analysts and scientists use numpy and its sum function with axis to summarize and analyze multi-dimensional data efficiently.

1

Create the sales data array

Create a numpy array called sales with this exact 2D data:
[[5, 3, 2], [6, 7, 1], [4, 5, 3], [8, 2, 4]] representing sales of 3 products over 4 days.

NumPy

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

Need a hint?

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

2

Set axis variables for summing

Create a variable called axis_0 and set it to 0 to sum over rows (days). Then create a variable called axis_1 and set it to 1 to sum over columns (products).

NumPy

import numpy as np
sales = np.array([[5, 3, 2], [6, 7, 1], [4, 5, 3], [8, 2, 4]])
# Create axis_0 and axis_1 variables
# Your code here

Need a hint?

Remember, axis 0 means down the rows, axis 1 means across the columns.

3

Calculate total sales per product and per day

Use np.sum() with axis=axis_0 on sales to calculate total sales per product and save it in total_per_product. Then use np.sum() with axis=axis_1 on sales to calculate total sales per day and save it in total_per_day.

NumPy

import numpy as np
sales = np.array([[5, 3, 2], [6, 7, 1], [4, 5, 3], [8, 2, 4]])
axis_0 = 0
axis_1 = 1
# Calculate total_per_product and total_per_day
# Your code here

Need a hint?

Use np.sum() with the correct axis variable for each total.

4

Print the total sales results

Print total_per_product and then print total_per_day to show the sums.

NumPy

import numpy as np
sales = np.array([[5, 3, 2], [6, 7, 1], [4, 5, 3], [8, 2, 4]])
axis_0 = 0
axis_1 = 1
total_per_product = np.sum(sales, axis=axis_0)
total_per_day = np.sum(sales, axis=axis_1)
# Print the totals
# Your code here

Need a hint?

Use two print() statements, one for each total array.