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)

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

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.

Practice

1.

What does the np.count_nonzero() function do in NumPy?

easy

A. Calculates the sum of all elements in an array

B. Returns the shape of the array

C. Finds the maximum value in an array

D. Counts how many elements in an array are not zero

Solution

1. Step 1: Understand the function purpose

   np.count_nonzero() counts elements that are not zero in the array.

2. Step 2: Compare with other options

   Other options describe different functions like sum, max, or shape, which are not what np.count_nonzero() does.

3. Final Answer:

   Counts how many elements in an array are not zero -> Option D

4. Quick Check:

   Counting non-zero elements = Counts how many elements are not zero [OK]

Hint: Remember: count_nonzero counts non-zero values only [OK]

Common Mistakes:

• Confusing count_nonzero with sum or max functions
• Thinking it returns the array shape
• Assuming it counts zero elements

2.

Which of the following is the correct syntax to count non-zero elements in a NumPy array arr?

arr = np.array([1, 0, 3, 0, 5])

easy

A. np.count_nonzero = arr

B. np.count_nonzero(arr)

C. arr.count_nonzero()

D. np.count(arr != 0)

Solution

1. Step 1: Identify correct function usage

   The function np.count_nonzero() is called with the array as argument: np.count_nonzero(arr).

2. Step 2: Check other options for errors

   np.count_nonzero = arr tries to assign instead of call; arr.count_nonzero() uses method not available on array; np.count(arr != 0) uses a non-existent function np.count().

3. Final Answer:

   np.count_nonzero(arr) -> Option B

4. Quick Check:

   Correct syntax is np.count_nonzero(array) [OK]

Hint: Use np.count_nonzero(array) to count non-zero values [OK]

Common Mistakes:

• Using assignment instead of function call
• Calling count_nonzero as a method on array
• Using non-existent np.count function

3.

What is the output of the following code?

import numpy as np
arr = np.array([[0, 1, 2], [3, 0, 0], [4, 5, 6]])
count = np.count_nonzero(arr, axis=0)
print(count)

medium

A. [3 2 3]

B. [3 3 3]

C. [2 3 2]

D. [2 2 2]

Solution

1. Step 1: Understand axis=0 counting

   Counting non-zero elements along columns (axis=0) means counting down each column.

2. Step 2: Count non-zero per column

   Column 1: values [0,3,4] -> non-zero count = 2 (3 and 4)
   Column 2: values [1,0,5] -> non-zero count = 2 (1 and 5)
   Column 3: values [2,0,6] -> non-zero count = 2 (2 and 6)

3. Final Answer:

   [3 2 3] -> Option A

4. Quick Check:

   Count non-zero per column = [3 2 3] [OK]

Hint: axis=0 counts down columns, axis=1 counts across rows [OK]

Common Mistakes:

• Counting zeros instead of non-zero
• Confusing axis=0 with axis=1
• Miscounting elements per column

4.

Find the error in this code snippet and choose the correct fix:

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

medium

A. Remove axis=1 because arr is 1D, use np.count_nonzero(arr) instead

B. Change axis=1 to axis=0 to fix the error

C. Use arr.count_nonzero() method instead

D. No error, code runs fine

Solution

1. Step 1: Identify array dimension

   Array arr is 1D, so axis=1 is invalid (no second axis).

2. Step 2: Correct function call

   Remove axis argument to count all non-zero elements: np.count_nonzero(arr).

3. Final Answer:

   Remove axis=1 because arr is 1D, use np.count_nonzero(arr) instead -> Option A

4. Quick Check:

   1D arrays have no axis=1, so omit axis [OK]

Hint: Check array shape before using axis in count_nonzero [OK]

Common Mistakes:

• Using axis=1 on 1D arrays causes errors
• Trying to call count_nonzero as array method
• Assuming axis=0 fixes all axis errors

5.

You have a 2D NumPy array representing attendance (1 for present, 0 for absent) of 4 students over 5 days:

attendance = np.array([
  [1, 0, 1, 1, 0],
  [0, 0, 1, 0, 0],
  [1, 1, 1, 1, 1],
  [0, 0, 0, 0, 0]
])

Which code correctly counts how many days each student was present?

hard

A. np.sum(attendance, axis=0)

B. np.count_nonzero(attendance, axis=0)

C. np.count_nonzero(attendance, axis=1)

D. np.count_nonzero(attendance)

Solution

1. Step 1: Understand data layout

   Rows represent students, columns represent days. Counting days present per student means counting non-zero per row (axis=1).

2. Step 2: Choose correct axis

   Use np.count_nonzero(attendance, axis=1) to count non-zero values per student (row).

3. Final Answer:

   np.count_nonzero(attendance, axis=1) -> Option C

4. Quick Check:

   Count per row (student) = axis=1 [OK]

Hint: Count per student = count_nonzero with axis=1 [OK]

Common Mistakes:

• Using axis=0 counts per day, not per student
• Using np.sum instead of count_nonzero (works but different function)
• Counting total non-zero without axis