reshape() for changing dimensions in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of reshaping a 1D array to 2D

What is the output of this code?

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
reshaped = arr.reshape(2, 3)
print(reshaped)

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
reshaped = arr.reshape(2, 3)
print(reshaped)

A

[[1 2 3]
 [4 5 6]]

B

[[1 2]
 [3 4]
 [5 6]]

C

[[1 2 3 4]
 [5 6 0 0]]

D [1 2 3 4 5 6]

💡 Hint

Remember reshape changes the shape but keeps all elements in order.

The array of 6 elements is reshaped into 2 rows and 3 columns, filling rows first.

❓ data_output

intermediate

Shape after reshaping with -1

Given this code, what is the shape of the reshaped array?

import numpy as np
arr = np.arange(12)
reshaped = arr.reshape(3, -1)
print(reshaped.shape)

NumPy

import numpy as np
arr = np.arange(12)
reshaped = arr.reshape(3, -1)
print(reshaped.shape)

A (3, 4)

B (4, 3)

C (6, 2)

D (2, 6)

💡 Hint

The -1 lets numpy calculate the missing dimension automatically.

The array has 12 elements. Reshaping to 3 rows means columns = 12/3 = 4.

🔧 Debug

advanced

Error when reshaping incompatible size

What error does this code raise?

import numpy as np
arr = np.arange(10)
reshaped = arr.reshape(3, 4)

NumPy

import numpy as np
arr = np.arange(10)
reshaped = arr.reshape(3, 4)

A No error, outputs a 3x4 array

B ValueError: cannot reshape array of size 10 into shape (3,4)

C IndexError: index out of bounds

D TypeError: reshape() takes exactly 1 argument

💡 Hint

Check if total elements match new shape size.

The original array has 10 elements but 3x4=12 elements are needed, so reshape fails.

🚀 Application

advanced

Reshape for image data

You have a flat array of 3072 elements representing a 32x32 RGB image (3 color channels). Which reshape call correctly converts it to (32, 32, 3)?

A arr.reshape(32, 3, 32)

B arr.reshape(3, 32, 32)

C arr.reshape(32, 32, 3)

D arr.reshape(3072, 1)

💡 Hint

Think about height, width, and color channels order.

The image shape is height x width x channels, so (32, 32, 3) is correct.

🧠 Conceptual

expert

Effect of reshape on data order

When reshaping a numpy array, which statement is true about the order of elements in memory?

A Reshape randomly shuffles elements to fill the new shape.

B Reshape changes the order of elements in memory to fit the new shape.

C Reshape reverses the order of elements when changing dimensions.

D Reshape keeps the order of elements the same; only the shape changes.

💡 Hint

Think about whether reshape rearranges data or just views it differently.

Reshape only changes the shape metadata; the element order in memory stays the same.