np.linalg.norm() for vector norms in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the Euclidean norm of vector v.

NumPy

import numpy as np
v = np.array([3, 4])
norm = np.linalg.norm([1])
print(norm)

Drag options to blanks, or click blank then click option'

Anp.linalg

B[3, 4]

Cnp.array([3, 4])

Dv

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the numpy module instead of the vector.

Passing a list instead of the numpy array variable.

The function np.linalg.norm() calculates the length (norm) of the vector passed to it. Here, v is the vector.

2fill in blank

medium

Complete the code to calculate the 1-norm (sum of absolute values) of vector x.

NumPy

import numpy as np
x = np.array([-1, 2, -3])
norm_1 = np.linalg.norm(x, ord=[1])
print(norm_1)

Drag options to blanks, or click blank then click option'

A2

Bnp.inf

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using ord=2 which is the default Euclidean norm.

Using ord=0 which is not a valid norm for vectors.

The ord=1 argument tells np.linalg.norm() to compute the 1-norm, which is the sum of absolute values of the vector elements.

3fill in blank

hard

Fix the error in the code to correctly compute the infinity norm (max absolute value) of vector y.

NumPy

import numpy as np
y = np.array([1, -5, 3])
norm_inf = np.linalg.norm(y, ord=[1])
print(norm_inf)

Drag options to blanks, or click blank then click option'

Ainf

Bnp.inf

Cfloat('inf')

Dinfinity

Attempts:

3 left

💡 Hint

Common Mistakes

Using string 'inf' instead of np.inf.

Using float('inf') which is not accepted here.

The correct way to specify the infinity norm in np.linalg.norm() is ord=np.inf. Other options cause errors or are invalid.

4fill in blank

hard

Fill both blanks to create a dictionary of vector norms for each vector in the list.

NumPy

import numpy as np
vectors = [np.array([1, 2]), np.array([3, 4])]
norms = {i: np.linalg.norm(vectors[[1]], ord=[2]) for i in range(len(vectors))}
print(norms)

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Ai

B0

C1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using a fixed index instead of the loop variable.

Using an invalid norm order.

The loop variable i is used to index each vector in the list. Using ord=1 computes the 1-norm for each vector.

5fill in blank

hard

Fill all three blanks to create a dictionary of Euclidean norms for vectors longer than 2 elements.

NumPy

import numpy as np
vectors = [np.array([1, 2]), np.array([3, 4, 5]), np.array([6, 7, 8, 9])]
norms = {i: np.linalg.norm(vectors[[1]], ord=[2]) for i in range(len(vectors)) if len(vectors[[3]]) > 2}
print(norms)

Drag options to blanks, or click blank then click option'

Ai

B2

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using different indices for vector access and length check.

Using wrong norm order.

The loop variable i (option A) is used to index vectors. The Euclidean norm is ord=2. The length check uses the same index i.