np.dot() for dot product in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the dot product of two 1D arrays using numpy.

NumPy

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = np.[1](arr1, arr2)
print(result)

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Adot

Bcross

Cmultiply

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.multiply which returns element-wise multiplication, not the dot product.

Using np.cross which computes the cross product, not dot product.

The np.dot() function computes the dot product of two arrays. Here, it multiplies corresponding elements and sums them up.

2fill in blank

medium

Complete the code to calculate the dot product of two 2D arrays using numpy.

NumPy

import numpy as np

mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.array([[5, 6], [7, 8]])
result = np.[1](mat1, mat2)
print(result)

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Amultiply

Bdot

Cadd

Dtranspose

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.multiply which multiplies element-wise, not matrix multiplication.

Using np.transpose which just flips the matrix, not multiply.

np.dot() performs matrix multiplication when given 2D arrays, producing the dot product matrix.

3fill in blank

hard

Fix the error in the code to correctly compute the dot product of two arrays.

NumPy

import numpy as np

vec1 = np.array([1, 2, 3])
vec2 = np.array([4, 5, 6])
result = np.dot(vec1, [1])
print(result)

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Anp.array

Bvec1

Cvec2

Dvec3

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the first vector twice instead of the second.

Passing a non-array object.

The second argument to np.dot() must be the second array to multiply with the first. Here, it should be vec2.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that stores the dot product of each vector with itself for vectors longer than 2 elements.

NumPy

import numpy as np

vectors = {'a': [1, 2, 3], 'b': [4, 5], 'c': [6, 7, 8, 9]}
dot_products = {key: np.[1](np.array(val), np.array(val)) for key, val in vectors.items() if len(val) [2] 2}
print(dot_products)

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Adot

B>

C<=

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum instead of np.dot for dot product.

Using wrong comparison operator like <= instead of >.

The dictionary comprehension calculates the dot product of each vector with itself using np.dot. The condition filters vectors longer than 2 elements using len(val) > 2.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that stores the dot product of vectors with length less than 4, using uppercase keys.

NumPy

import numpy as np

vectors = {'x': [1, 0, 0], 'y': [0, 1, 0, 1], 'z': [1, 1]}
dot_dict = {key.[1](): np.[2](np.array(val), np.array(val)) for key, val in vectors.items() if len(val) [3] 4}
print(dot_dict)

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Alower

Bdot

C<

Dupper

Attempts:

3 left

💡 Hint

Common Mistakes

Using lower() instead of upper() for keys.

Using wrong comparison operator like > instead of <.

The dictionary comprehension converts keys to uppercase using upper(), computes dot products with np.dot, and filters vectors with length less than 4 using < 4.