reshape() for changing dimensions in NumPy - Mini Project: Build & Apply

Reshape Arrays with numpy.reshape()

📖 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 into a table showing sales per day and bread type.

🎯 Goal: You will create a numpy array from a list of sales numbers, then reshape it into a 2D array with rows as days and columns as bread types. Finally, you will print the reshaped array to see the organized sales data.

📋 What You'll Learn

Use numpy to create and reshape arrays

Create a 1D numpy array with exact sales data

Define a new shape as a tuple

Use numpy.reshape() to change the array dimensions

Print the reshaped array

💡 Why This Matters

🌍 Real World

Reshaping arrays is useful when you want to organize flat data into tables or grids, like sales data per day and product.

💼 Career

Data scientists often reshape data to prepare it for analysis or visualization, making this skill important for cleaning and structuring data.

1

Create the sales data array

Import numpy as np and create a 1D numpy array called sales with these exact values: 10, 15, 20, 25, 30, 35.

NumPy

# Import numpy and create the sales array
# Your code here

Need a hint?

Use np.array() to create the array from the list of numbers.

2

Define the new shape for the array

Create a variable called new_shape and set it to the tuple (2, 3) to represent 2 days and 3 bread types.

NumPy

import numpy as np
sales = np.array([10, 15, 20, 25, 30, 35])
# Define the new shape as (2, 3)
# Your code here

Need a hint?

A tuple is written with parentheses and commas, like (2, 3).

3

Reshape the sales array

Use np.reshape() with the sales array and new_shape to create a new array called reshaped_sales.

NumPy

import numpy as np
sales = np.array([10, 15, 20, 25, 30, 35])
new_shape = (2, 3)
# Reshape the sales array using np.reshape()
# Your code here

Need a hint?

Call np.reshape() with the array first, then the shape tuple.

4

Print the reshaped sales array

Print the variable reshaped_sales to display the sales data organized by days and bread types.

NumPy

import numpy as np
sales = np.array([10, 15, 20, 25, 30, 35])
new_shape = (2, 3)
reshaped_sales = np.reshape(sales, new_shape)
# Print the reshaped_sales array
# Your code here

Need a hint?

Use print(reshaped_sales) to show the array.