outer() for outer operations in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to compute the outer product of two 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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Aouter

Bdot

Csum

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.dot instead of np.outer, which computes a dot product, not an outer product.

Using sum or mean which are aggregation functions, not for outer products.

The np.outer() function computes the outer product of two arrays, resulting in a matrix where each element is the product of elements from the two input arrays.

2fill in blank

medium

Complete the code to calculate the outer product of two 1D numpy arrays and store it in 'result'.

NumPy

import numpy as np

x = np.array([2, 3])
y = np.array([5, 7])
result = np.[1](x, y)
print(result)

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Ainner

Bcross

Cdot

Douter

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.dot or np.inner which compute dot and inner products, not outer.

Using np.cross which is for vector cross product, only for 3D vectors.

np.outer() calculates the outer product of two vectors, producing a matrix where each element is the product of elements from the two input arrays.

3fill in blank

hard

Fix the error in the code to correctly compute the outer product of arrays 'a' and 'b'.

NumPy

import numpy as np

a = np.array([1, 2])
b = np.array([3, 4])
result = np.[1](a, b)
print(result)

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Amultiply

Bdot

Couter

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.dot which computes dot product, resulting in a scalar for 1D arrays.

Using np.multiply which does element-wise multiplication, not outer product.

The correct function to compute the outer product is np.outer(). Using np.dot() or others will not produce the outer product matrix.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {word: [1] for word in words if [2]
print(lengths)

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Alen(word)

Bword > 3

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length in the dictionary values.

Comparing the word string directly to a number instead of its length.

The dictionary comprehension uses len(word) to get the length of each word and filters words where the length is greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word in uppercase to its length only if the length is less than or equal to 4.

NumPy

words = ['data', 'science', 'ai', 'ml']
result = { [1]: [2] for word in words if [3] }
print(result)

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Aword.upper()

Blen(word)

Clen(word) <= 4

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the key instead of uppercase.

Using the word instead of its length as the value.

Incorrect condition like comparing the word string directly to a number.

The dictionary comprehension maps each word in uppercase (word.upper()) to its length (len(word)) only if the length is less than or equal to 4 (len(word) <= 4).