np.count_nonzero() for counting in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Counting non-zero elements in a 1D array

What is the output of this code snippet using np.count_nonzero()?

NumPy

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

A 0

B 4

C 7

D 3

💡 Hint

Count how many numbers are not zero in the array.

The array has four non-zero numbers: 1, 2, 3, and 4. So, np.count_nonzero returns 4.

❓ data_output

intermediate

Counting non-zero elements along an axis in 2D array

What is the output of np.count_nonzero() when counting non-zero elements along axis 0 in this 2D array?

NumPy

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

A [2 2 2]

B [3 3 3]

C [1 2 2]

D [2 1 1]

💡 Hint

Count non-zero values in each column (axis 0).

Column-wise counts: first column has 2 non-zero (3,4), second column 2 non-zero (1,5), third column 2 non-zero (2,6).

🔧 Debug

advanced

Identify the error in using np.count_nonzero with a condition

What error does this code raise?

NumPy

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

A AxisError: axis 1 is out of bounds for array of dimension 1

B TypeError: '>' not supported between instances of 'int' and 'int'

C ValueError: operands could not be broadcast together

D No error, output is 2

💡 Hint

Check the dimension of the array and the axis used.

The array is 1D, so axis=1 is invalid and causes AxisError.

🚀 Application

advanced

Using np.count_nonzero to count True values in a boolean mask

Given this boolean mask, what does np.count_nonzero(mask) return?

NumPy

import numpy as np
mask = np.array([[True, False, True], [False, False, True], [True, True, False]])
result = np.count_nonzero(mask)
print(result)

A 4

B 3

C 5

D 6

💡 Hint

Count how many True values are in the mask.

There are five True values in the mask array.

🧠 Conceptual

expert

Effect of np.count_nonzero on arrays with NaN values

What is the output of np.count_nonzero() when applied to this array containing NaN values?

NumPy

import numpy as np
arr = np.array([0, np.nan, 1, 2, np.nan, 0])
result = np.count_nonzero(arr)
print(result)

A Raises TypeError

B 3

C 2

D 4

💡 Hint

Remember that NaN is treated as a non-zero value in numpy.

NaN is not zero, so it counts as non-zero. The non-zero elements are the two np.nan, 1, and 2, totaling 4.

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