Counting with boolean arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count how many values in the array are True.

NumPy

import numpy as np
arr = np.array([True, False, True, True, False])
count_true = np.[1](arr)

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Asum

Bcount

Cmean

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which does not exist for numpy arrays.

Using len() which returns total length, not count of True.

Using mean() which returns average, not count.

Using np.sum() on a boolean array counts how many values are True because True is treated as 1 and False as 0.

2fill in blank

medium

Complete the code to count how many values in the array are False.

NumPy

import numpy as np
arr = np.array([True, False, True, False, False])
count_false = np.[1](~arr)

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Asum

Blen

Ccount

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which is not a numpy function.

Using mean() which calculates average, not count.

Using len() which returns total length.

Using np.sum() on the negated array ~arr counts how many values are False in the original array.

3fill in blank

hard

Fix the error in the code to count True values along axis 0.

NumPy

import numpy as np
arr = np.array([[True, False], [False, True], [True, True]])
count_true_axis0 = np.sum(arr, axis=[1])

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A1

B2

C0

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 counts per row, not per column.

Using axis=2 causes error because array is 2D.

Using axis=-1 counts last axis but may confuse beginners.

Axis 0 means counting down the rows for each column. So axis=0 sums True values column-wise.

4fill in blank

hard

Fill both blanks to create a dictionary counting words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'car', 'door', 'egg']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using word > 3 compares strings to numbers, causing error.

Using word instead of len(word) for length.

Missing the condition to filter words.

The dictionary comprehension stores the length of each word. The condition filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths if length > 3.

NumPy

words = ['apple', 'bat', 'car', 'door', 'egg']
result = { [1]: [2] for [3] in words if len([3]) > 3 }

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Aword.upper()

Blen(word)

Cword

Dwords

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'words' instead of 'word' in the loop variable.

Using 'words.upper()' which is invalid.

Not filtering words by length.

The dictionary comprehension uses uppercase words as keys and their lengths as values, filtering words longer than 3 letters.