Common broadcasting patterns in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a 1D array to each row of a 2D array using broadcasting.

NumPy

import numpy as np
arr2d = np.array([[1, 2, 3], [4, 5, 6]])
arr1d = np.array([10, 20, 30])
result = arr2d [1] arr1d
print(result)

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A*

B-

C/

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction or multiplication instead of addition.

Trying to add arrays of incompatible shapes without broadcasting.

Using the plus operator (+) adds the 1D array to each row of the 2D array by broadcasting.

2fill in blank

medium

Complete the code to multiply a 2D array by a column vector using broadcasting.

NumPy

import numpy as np
arr2d = np.array([[1, 2, 3], [4, 5, 6]])
col_vec = np.array([[10], [20]])
result = arr2d [1] col_vec
print(result)

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A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Not reshaping the column vector properly.

The multiplication operator (*) broadcasts the column vector across each row of the 2D array.

3fill in blank

hard

Fix the error in the code to subtract a 1D array from each column of a 2D array using broadcasting.

NumPy

import numpy as np
arr2d = np.array([[5, 10], [15, 20], [25, 30]])
arr1d = np.array([1, 2, 3])
result = arr2d - arr1d[1]
print(result)

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A.reshape(3,)

B.reshape(1, 3)

C.reshape(3, 1)

D.reshape(1, 1)

Attempts:

3 left

💡 Hint

Common Mistakes

Reshaping to (3,1) which aligns as a column vector, not row.

Not reshaping at all causing shape mismatch error.

Reshaping arr1d to (1,3) aligns it as a row vector to subtract from each column of arr2d by broadcasting.

4fill in blank

hard

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

NumPy

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

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Alen(word)

Blen(word) > 3

Clen(word) < 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong condition for filtering.

The dictionary comprehension maps each word to its length (len(word)) only if the length is greater than 3 (len(word) > 3).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if the length is greater than 2.

NumPy

words = ['hi', 'hello', 'yes', 'no']
result = { [1]: [2] for word in words if [3] }

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Aword.upper()

Blen(word)

Clen(word) > 2

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

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

Not filtering words by length properly.

The comprehension maps the uppercase version of each word (word.upper()) to its length (len(word)) only if the length is greater than 2 (len(word) > 2).