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.