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.

Practice

1. What does the reshape() function do in NumPy?

easy

A. Creates a new array with random values

B. Changes the shape of an array without changing its data

C. Sorts the elements of an array

D. Deletes elements from an array

Solution

1. Step 1: Understand the purpose of reshape()

   The reshape() function changes the shape (dimensions) of an existing array but keeps the data intact.

2. Step 2: Compare with other options

   Options A, B, and C describe different functions or actions unrelated to reshaping.

3. Final Answer:

   Changes the shape of an array without changing its data -> Option B

4. Quick Check:

   reshape() = change shape only [OK]

Hint: reshape() changes shape, data stays same [OK]

Common Mistakes:

• Thinking reshape() changes data values
• Confusing reshape() with sorting or deleting
• Assuming reshape() creates new random arrays

2. Which of the following is the correct syntax to reshape a NumPy array arr into 2 rows and 3 columns?

easy

A. arr.reshape(2[3])

B. arr.reshape{2, 3}

C. arr.reshape(2, 3)

D. reshape(arr, 2, 3)

Solution

1. Step 1: Recall correct reshape syntax

   The correct syntax is array.reshape(dim1, dim2, ...) with dimensions as separate arguments or a tuple.

2. Step 2: Evaluate options

   arr.reshape(2, 3) uses correct method call with separate arguments. arr.reshape(2[3]) has invalid syntax (missing comma). arr.reshape{2, 3} uses curly braces which is wrong syntax. reshape(arr, 2, 3) uses a function call without object which is invalid.

3. Final Answer:

   arr.reshape(2, 3) -> Option C

4. Quick Check:

   Use dot and parentheses with dims [OK]

Hint: Use dot and parentheses with dims, no brackets [OK]

Common Mistakes:

• Forgetting comma between dimensions
• Using curly braces instead of parentheses
• Calling reshape as a standalone function

3. What is the output shape of the following code?

import numpy as np
arr = np.arange(12)
new_arr = arr.reshape(3, 4)
print(new_arr.shape)

medium

A. (3, 4)

B. (4, 3)

C. (12,)

D. (2, 6)

Solution

1. Step 1: Understand the original array

   np.arange(12) creates a 1D array with 12 elements from 0 to 11.

2. Step 2: Reshape to (3, 4)

   The reshape changes the array to 3 rows and 4 columns, so the shape becomes (3, 4).

3. Final Answer:

   (3, 4) -> Option A

4. Quick Check:

   reshape(3,4) = (3,4) shape [OK]

Hint: reshape dims become shape tuple [OK]

Common Mistakes:

• Confusing rows and columns order
• Thinking reshape changes total elements
• Expecting original 1D shape after reshape

4. What error will occur when running this code?

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

medium

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

B. TypeError: reshape() takes exactly 1 argument

C. SyntaxError: invalid syntax

D. No error, reshaping works fine

Solution

1. Step 1: Check original array size

   np.arange(10) creates an array with 10 elements.

2. Step 2: Check reshape dimensions

   Trying to reshape into (3, 4) means 12 elements needed, but only 10 exist.

3. Step 3: Identify error type

   This mismatch causes a ValueError about incompatible shape.

4. Final Answer:

   ValueError: cannot reshape array of size 10 into shape (3,4) -> Option A

5. Quick Check:

   Elements must match for reshape [OK]

Hint: Total elements must match for reshape [OK]

Common Mistakes:

• Ignoring total element count mismatch
• Expecting reshape to auto-fill missing elements
• Confusing ValueError with SyntaxError

5. Given a 1D NumPy array arr = np.arange(24), which of the following reshapes it into a 3D array with shape (2, 3, 4) using automatic dimension calculation with -1 in the first position?

hard

A. arr.reshape(2, 3, 4)

B. arr.reshape(2, -1, 4)

C. arr.reshape(2, 3, -1)

D. arr.reshape(-1, 3, 4)

Solution

1. Step 1: Understand total elements and target shape

   The array has 24 elements. The target shape is 3D with dimensions (2, 3, 4), total 24 elements.

2. Step 2: Use -1 for automatic dimension

   Using -1 lets NumPy calculate that dimension automatically. For (2, 3, 4), if we replace one dimension with -1, NumPy calculates it as 24 divided by product of other dims.

3. Step 3: Check each option

   arr.reshape(2, 3, 4) is explicit (2,3,4) - works but no automatic calculation.
   arr.reshape(2, -1, 4): (2, -1, 4) means -1 = 24/(2*4) = 3, shape (2,3,4) correct.
   arr.reshape(-1, 3, 4): (-1, 3, 4) means -1 = 24/(3*4) = 2, shape (2,3,4) correct.
   arr.reshape(2, 3, -1): (2, 3, -1) means -1 = 24/(2*3) = 4, shape (2,3,4) correct.
   All B, C, D produce correct shape, but question asks for reshaping into (2,3,4) using automatic dimension calculation. Only arr.reshape(-1, 3, 4) uses -1 as first dimension, matching question wording.

4. Final Answer:

   arr.reshape(-1, 3, 4) -> Option D

5. Quick Check:

   -1 auto calculates dimension [OK]

Hint: Use -1 for one dimension to auto calculate [OK]

Common Mistakes:

• Using multiple -1 values in reshape
• Not matching total elements with new shape
• Confusing which dimension -1 replaces