np.count_nonzero() for counting in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count non-zero elements in the array.

NumPy

import numpy as np
arr = np.array([0, 1, 2, 0, 3])
count = np.[1](arr)
print(count)

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

Acount_nonzero

Bsum

Cnonzero

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sum() counts the sum of elements, not the count of non-zero elements.

Using np.nonzero() returns indices, not the count.

The function np.count_nonzero() counts the number of non-zero elements in the array.

2fill in blank

medium

Complete the code to count non-zero elements along axis 0.

NumPy

import numpy as np
arr = np.array([[0, 1, 2], [3, 0, 0]])
count = np.count_nonzero(arr, axis=[1])
print(count)

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

A1

B-1

C2

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 counts across rows, not columns.

Using negative axis values may cause confusion.

Setting axis=0 counts non-zero elements down each column.

3fill in blank

hard

Fix the error in the code to correctly count non-zero elements in a boolean array.

NumPy

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

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

Aarr.nonzero()

Barr

Cnp.sum(arr)

Darr.sum()

Attempts:

3 left

💡 Hint

Common Mistakes

Passing arr.sum() returns a number, not an array, causing errors.

Passing arr.nonzero() returns indices, not suitable here.

np.count_nonzero() expects the array itself as input, not a sum or indices.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

NumPy

words = ['cat', 'house', 'dog', 'elephant']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) stores the word, not its length.

Using '<=' includes shorter words, not longer.

The dictionary comprehension uses len(word) for values and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their counts if count is greater than 1.

NumPy

data = {'apple': 2, 'banana': 1, 'cherry': 3}
result = { [1]: [2] for word in data if data[word] [3] 1 }
print(result)

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

Aword

Bdata[word]

C>

Dword.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of word.upper() keeps keys lowercase.

Using '<' or '<=' includes unwanted counts.

The dictionary comprehension creates keys as uppercase words, values as counts, filtering counts greater than 1.