np.linalg.eig() for eigenvalues in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to compute eigenvalues of matrix A.

NumPy

import numpy as np
A = np.array([[2, 0], [0, 3]])
eigenvalues, _ = np.linalg.[1](A)
print(eigenvalues)

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Aeig

Bdet

Csolve

Dinv

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.linalg.inv() which computes inverse, not eigenvalues.

Using np.linalg.det() which computes determinant, not eigenvalues.

The function np.linalg.eig() returns eigenvalues and eigenvectors of a square matrix.

2fill in blank

medium

Complete the code to extract only eigenvalues from the result.

NumPy

import numpy as np
B = np.array([[4, 1], [2, 3]])
eigenvalues, [1] = np.linalg.eig(B)
print(eigenvalues)

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Avectors

Bvals

Ceigenvectors

Dvecs

Attempts:

3 left

💡 Hint

Common Mistakes

Naming the second variable as eigenvalues again.

Using unrelated variable names like 'vals' which is unclear.

The second returned value from np.linalg.eig() is the eigenvectors, commonly named eigenvectors.

3fill in blank

hard

Fix the error in the code to correctly compute eigenvalues.

NumPy

import numpy as np
C = np.array([[1, 2], [3, 4]])
eigenvalues, _ = np.linalg.[1](C)
print(eigenvalues)

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Aeigvals

Beig

Ceigen

Deigenvalues

Attempts:

3 left

💡 Hint

Common Mistakes

Using eigvals which returns only eigenvalues and causes an unpacking error.

Using non-existent functions like eigen or eigenvalues.

The function np.linalg.eig() returns both eigenvalues and eigenvectors of a square matrix. We ignore the eigenvectors using _.

4fill in blank

hard

Fill both blanks to create a dictionary of eigenvalue to eigenvector mapping.

NumPy

import numpy as np
D = np.array([[5, 4], [1, 2]])
eigenvalues, eigenvectors = np.linalg.eig(D)
result = {eigenvalues[[1]]: eigenvectors[:, [2]] for i in range(len(eigenvalues))}
print(result)

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Ai

B0

C1

Dlen(eigenvalues)

Attempts:

3 left

💡 Hint

Common Mistakes

Using fixed indices like 0 or 1 instead of the loop variable.

Using length of eigenvalues as index which causes error.

Use the loop variable i to index eigenvalues and eigenvectors to build the dictionary.

5fill in blank

hard

Fill all three blanks to filter eigenvalues greater than 3 and create a dictionary with their eigenvectors.

NumPy

import numpy as np
E = np.array([[6, 2], [2, 3]])
eigenvalues, eigenvectors = np.linalg.eig(E)
filtered = {eigenvalues[[1]]: eigenvectors[:, [2]] for [3] in range(len(eigenvalues)) if eigenvalues[[1]] > 3}
print(filtered)

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Ai

Bj

Ck

Dm

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variables for indexing causing errors.

Using fixed indices instead of loop variable.

Use the same loop variable i to index eigenvalues and eigenvectors and filter eigenvalues greater than 3.