np.einsum() for efficient computation in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to compute the sum of elements in array using np.einsum.

NumPy

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

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

A'i->'

B'i->i'

C'ij->i'

D'i,i->'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'i,i->' which expects two arrays.

Using 'ij->i' which is for 2D arrays.

Using 'i->i' which returns the array unchanged.

The subscript 'i->' tells np.einsum to sum over the index 'i', resulting in the sum of all elements.

2fill in blank

medium

Complete the code to compute the dot product of two vectors using np.einsum.

NumPy

import numpy as np
v1 = np.array([1, 2, 3])
v2 = np.array([4, 5, 6])
dot_product = np.einsum('[1]', v1, v2)
print(dot_product)

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

A'ij,jk->ik'

B'i,j->ij'

C'i,i->'

D'i->'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'i->' which sums only one vector.

Using 'ij,jk->ik' which is for matrix multiplication.

Using 'i,j->ij' which computes outer product.

The subscript 'i,i->' multiplies elements of v1 and v2 at the same index and sums the result, computing the dot product.

3fill in blank

hard

Fix the error in the code to perform matrix multiplication using np.einsum.

NumPy

import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
result = np.einsum('[1]', A, B)
print(result)

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

A'ij,jk->ik'

B'ij,ik->jk'

C'ji,kj->ik'

D'ij,jk->ij'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ij,ik->jk' which mismatches indices.

Using 'ji,kj->ik' which reverses indices incorrectly.

Using 'ij,jk->ij' which does not reduce the summed index.

The subscript 'ij,jk->ik' correctly performs matrix multiplication by summing over the shared index 'j'.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword

Dlen(words)

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' as value instead of length.

Using 'len(words)' which is the total number of words.

Using condition without comparison operator.

We use len(word) to get the length and filter words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = [1]: [2] for word in words if [3]

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

Aword.upper()

Blen(word)

Clen(word) > 3

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of upper().

Using word as key without conversion.

Missing the length condition or using wrong comparison.

The key is the uppercase word, the value is its length, and the condition filters words longer than 3 characters.