Matrix transpose operations in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Transpose on 2D NumPy Array

What is the output of the following code that transposes a 2D NumPy array?

NumPy

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

A

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

B

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

C

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

D

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

💡 Hint

Remember that transpose flips rows and columns.

The transpose of a 2x3 matrix becomes a 3x2 matrix where rows become columns.

❓ data_output

intermediate

Shape After Transpose of 3D Array

Given a 3D NumPy array with shape (2, 3, 4), what is the shape after applying arr.transpose(1, 0, 2)?

NumPy

import numpy as np
arr = np.zeros((2, 3, 4))
result = arr.transpose(1, 0, 2)
print(result.shape)

A (3, 2, 4)

B (2, 3, 4)

C (4, 3, 2)

D (3, 4, 2)

💡 Hint

Transpose rearranges axes in the order you specify.

The axes are reordered from (0,1,2) to (1,0,2), so shape changes from (2,3,4) to (3,2,4).

🔧 Debug

advanced

Identify the Error in Transpose Usage

What error does this code raise when trying to transpose a 2D array with an invalid axis order? import numpy as np arr = np.array([[1, 2], [3, 4]]) result = arr.transpose(0, 2) print(result)

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
result = arr.transpose(0, 2)
print(result)

A No error, prints transposed array

B IndexError: axis 2 is out of bounds for array of dimension 2

C TypeError: transpose() takes no arguments

D ValueError: axes don't match array

💡 Hint

Check the number of dimensions in the array and the axes used.

The array has 2 dimensions (axes 0 and 1). Using axis 2 causes ValueError because axes don't match array.

🚀 Application

advanced

Using Transpose to Swap Rows and Columns in Data

You have a dataset as a NumPy array with shape (5, 10), where rows are samples and columns are features. You want to switch rows and columns to have features as rows and samples as columns. Which code correctly does this?

A All of the above

B np.transpose(data, (1, 0))

C np.swapaxes(data, 0, 1)

D data.T

💡 Hint

All these methods can swap axes 0 and 1 for 2D arrays.

For 2D arrays, .T, np.transpose with axes (1,0), and np.swapaxes(0,1) all swap rows and columns.

🧠 Conceptual

expert

Effect of Transpose on Memory Layout

Which statement best describes the effect of transposing a NumPy array on its memory layout?

A Transpose always creates a new copy of the array with contiguous memory.

B Transpose returns a view with changed strides, no data is copied.

C Transpose modifies the original array in-place to swap rows and columns.

D Transpose converts the array to a list of lists.

💡 Hint

Think about whether transpose copies data or just changes how data is accessed.

Transpose returns a view with modified strides, so no data is copied and memory layout changes logically.