Why reshaping arrays matters in NumPy - See It in Action

Why reshaping arrays matters

📖 Scenario: Imagine you work in a bakery that tracks daily sales of different types of bread. The sales data is collected as a single list of numbers, but you want to organize it by days and bread types to understand patterns better.

🎯 Goal: You will reshape a one-dimensional array of sales data into a two-dimensional array to see daily sales per bread type clearly.

📋 What You'll Learn

Create a NumPy array called sales with exactly 12 values representing sales.

Create a variable called days and set it to 4.

Use the reshape method on sales to create a new array called daily_sales with shape (days, 3).

Print the daily_sales array to see the reshaped data.

💡 Why This Matters

🌍 Real World

Reshaping arrays helps organize raw data into meaningful tables, like daily sales per product, making it easier to analyze and understand.

💼 Career

Data scientists often reshape data to prepare it for analysis, visualization, or machine learning models.

1

Create the sales data array

Create a NumPy array called sales with these exact values: [10, 15, 7, 12, 18, 9, 14, 20, 11, 13, 17, 8].

NumPy

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

Need a hint?

Use np.array([...]) with the exact numbers inside the brackets.

2

Set the number of days

Create a variable called days and set it to 4.

NumPy

import numpy as np
sales = np.array([10, 15, 7, 12, 18, 9, 14, 20, 11, 13, 17, 8])
# Set days to 4
# Your code here

Need a hint?

Just assign the number 4 to the variable days.

3

Reshape the sales array

Use the reshape method on sales to create a new array called daily_sales with shape (days, 3).

NumPy

import numpy as np
sales = np.array([10, 15, 7, 12, 18, 9, 14, 20, 11, 13, 17, 8])
days = 4
# Reshape sales into daily_sales with shape (days, 3)
# Your code here

Need a hint?

Use sales.reshape(days, 3) to reshape the array.

4

Print the reshaped sales data

Print the daily_sales array to see the sales organized by day and bread type.

NumPy

import numpy as np
sales = np.array([10, 15, 7, 12, 18, 9, 14, 20, 11, 13, 17, 8])
days = 4
daily_sales = sales.reshape(days, 3)
# Print daily_sales
# Your code here

Need a hint?

Use print(daily_sales) to show the reshaped array.