Matrix transpose operations in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the transpose of a matrix do?

It flips the matrix over its diagonal, switching the row and column indices of each element.

beginner

How do you transpose a matrix using NumPy?

Use the .T attribute on a NumPy array, like matrix.T.

beginner

What is the shape of the transpose of a matrix with shape (3, 5)?

The transpose will have shape (5, 3), swapping rows and columns.

beginner

True or False: Transposing a matrix twice returns the original matrix.

True. Transposing twice flips the matrix back to its original form.

intermediate

How can you transpose a 3D NumPy array along specific axes?

Use np.transpose(array, axes=(...)) to reorder axes as needed.

What does matrix.T do in NumPy?

A Returns the matrix multiplied by 2

B Returns the inverse of the matrix

C Returns the determinant of the matrix

D Returns the transpose of the matrix

matrix.T flips the matrix over its diagonal, swapping rows and columns.

If a matrix has shape (4, 7), what is the shape of its transpose?

A (7, 4)

B (4, 7)

C (1, 28)

D (28, 1)

Transposing swaps rows and columns, so (4, 7) becomes (7, 4).

Which NumPy function allows transposing with custom axis order?

A np.transpose()

B np.reshape()

C np.flatten()

D np.dot()

np.transpose() lets you specify axes order for transposing arrays.

What happens if you transpose a matrix twice?

A You get the inverse matrix

B You get a zero matrix

C You get the original matrix

D You get a matrix with doubled values

Transposing twice returns the matrix to its original form.

Which of these is NOT true about matrix transpose?

A It swaps rows and columns

B It multiplies each element by -1

C It flips the matrix over its diagonal

D It changes the shape of the matrix

Transpose does not change element values, only their positions.

Explain how to transpose a 2D matrix in NumPy and describe what happens to its shape.

Think about flipping the matrix over its diagonal.

Describe how to transpose a 3D NumPy array along specific axes and why this might be useful.

Consider how axes represent dimensions in arrays.

Practice

1. What does the .T attribute do when applied to a NumPy matrix?

easy

A. It flips the rows and columns of the matrix (transpose).

B. It multiplies all elements by 2.

C. It returns the sum of all elements.

D. It flattens the matrix into a 1D array.

Solution

1. Step 1: Understand the .T attribute

   The .T attribute in NumPy returns the transpose of a matrix, which means rows become columns and columns become rows.

2. Step 2: Compare with other options

   Multiplying by 2, summing elements, or flattening are different operations and not related to .T.

3. Final Answer:

   It flips the rows and columns of the matrix (transpose). -> Option A

4. Quick Check:

   Transpose = flip rows and columns [OK]

Hint: Remember: .T flips rows and columns [OK]

Common Mistakes:

• Confusing transpose with flattening
• Thinking .T multiplies elements
• Assuming .T sums elements

2. Which of the following is the correct syntax to transpose a NumPy array named arr?

easy

A. arr.transpose

B. arr.T()

C. transpose(arr)

D. arr.T

Solution

1. Step 1: Recall NumPy transpose syntax

   In NumPy, .T is an attribute, not a method, so it does not use parentheses.

2. Step 2: Evaluate each option

   arr.transpose() uses transpose() method which exists but is a method, so requires parentheses. arr.T() incorrectly uses .T() as a method. transpose(arr) is not valid syntax. arr.T correctly uses .T attribute without parentheses.

3. Final Answer:

   arr.T -> Option D

4. Quick Check:

   Transpose attribute = arr.T [OK]

Hint: Use .T without parentheses to transpose [OK]

Common Mistakes:

• Using .T() as if it were a method
• Confusing transpose() method with .T attribute
• Trying to call transpose(arr) without import

3. What is the output of the following code?

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

medium

A. [[1 3]
[2 4]]

B. [[1 2]
[3 4]]

C. [[4 3]
[2 1]]

D. [[1 4]
[2 3]]

Solution

1. Step 1: Understand the original array

   The array arr is a 2x2 matrix: [[1, 2], [3, 4]].

2. Step 2: Apply transpose operation

   Transposing swaps rows and columns, so the first row [1, 2] becomes the first column, and the second row [3, 4] becomes the second column. Result: [[1, 3], [2, 4]].

3. Final Answer:

   [[1 3] [2 4]] -> Option A

4. Quick Check:

   Transpose swaps rows and columns = [[1 3] [2 4]] [OK]

Hint: Transpose swaps rows and columns positions [OK]

Common Mistakes:

• Confusing transpose with reversing elements
• Mixing rows and columns incorrectly
• Expecting original array output

4. Identify the error in this code snippet that tries to transpose a NumPy array:

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

medium

A. Array creation syntax is wrong.

B. Using parentheses with .T attribute causes an error.

C. Missing import statement for numpy.

D. print() function is used incorrectly.

Solution

1. Step 1: Check usage of .T

   The .T attribute is not a function, so it should not have parentheses. Using .T() causes a TypeError.

2. Step 2: Verify other parts of the code

   Array creation and import are correct. The print statement is also correct.

3. Final Answer:

   Using parentheses with .T attribute causes an error. -> Option B

4. Quick Check:

   .T is attribute, not method [OK]

Hint: Do not add () after .T attribute [OK]

Common Mistakes:

• Calling .T as a function with ()
• Assuming .T needs import
• Thinking print syntax is wrong

5. You have a 3x2 NumPy array arr = np.array([[1, 2], [3, 4], [5, 6]]). You want to multiply it by another array so that the result is a 2x2 matrix. Which of the following correctly uses transpose to achieve this multiplication?

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
arr2 = ???
result = np.dot(arr.T, arr2)
print(result)

hard

A. arr2 = np.array([[1, 0, 1], [0, 1, 1]])

B. arr2 = np.array([[1, 0], [0, 1]])

C. arr2 = np.array([[1, 0], [0, 1], [1, 1]])

D. arr2 = np.array([[1, 0], [0, 1], [1, 1], [0, 0]])

Solution

1. Step 1: Understand shapes of arrays

   arr is 3x2. Its transpose arr.T is 2x3. For matrix multiplication np.dot(arr.T, arr2), the number of rows in arr2 must be 3 to match arr.T's columns.

2. Step 2: Check options for correct shape

   arr2 = np.array([[1, 0], [0, 1], [1, 1]]) shape is 3x2, so arr.T (2x3) dot arr2 (3x2) results in 2x2 matrix, which is valid. arr2 = np.array([[1, 0], [0, 1]]) shape is 2x2, which does not match for multiplication with 2x3. arr2 = np.array([[1, 0, 1], [0, 1, 1]]) shape is 2x3, which does not match. arr2 = np.array([[1, 0], [0, 1], [1, 1], [0, 0]]) shape is 4x2, invalid.

3. Step 3: Re-examine arr2 = np.array([[1, 0], [0, 1]]) carefully

   Actually, arr2 = np.array([[1, 0], [0, 1]]) is 2x2, which cannot multiply with 2x3. So arr2 = np.array([[1, 0], [0, 1], [1, 1]]) is correct.

4. Final Answer:

   arr2 = np.array([[1, 0], [0, 1], [1, 1]]) -> Option C

5. Quick Check:

   Matrix shapes must align for dot product [OK]

Hint: Check matrix shapes before multiplying [OK]

Common Mistakes:

• Ignoring shape mismatch in dot product
• Confusing rows and columns after transpose
• Choosing arrays with incompatible shapes