Find Indices of Elements Equal to Zero in a NumPy Array

It means you want to find the positions (row and column indices) where the elements of a NumPy array are equal to zero. For example, Given the array [[1, 0, 3], [0, 5, 6], [7, 8, 0]], find the row and column indices of all elements equal to zero.

Using numpy.nonzero()

The numpy.nonzero() function returns the indices of non-zero elements. By applying a condition, we can use it to find positions of zeros.

Example: In this example, we create a 1-D array and use np.nonzero() to locate zero values.

import numpy as np

arr = np.array([1, 10, 2, 0, 3, 9, 0, 5, 0, 7, 5, 0, 0])
res = np.nonzero(arr == 0)

print("Array:", arr)
print("Indices of zero elements:", res)

Array: [ 1 10  2  0  3  9  0  5  0  7  5  0  0]
Indices of zero elements: (array([ 3,  6,  8, 11, 12]),)

Explanation

• arr == 0 creates a boolean array where True marks zero positions.
• np.nonzero() returns the indices where True occurs.

Using numpy.where()

The numpy.where() function returns indices where a condition holds true. It’s one of the most common ways to locate elements.

Example: Here, we use np.where() to find the indices of zeros in a 1-D array.

import numpy as np

arr = np.array([1, 0, 2, 0, 3, 0, 0, 5, 6, 7, 5, 0, 8])
res = np.where(arr == 0)[0]

print("Array:", arr)
print("Indices of zero elements:", res)

Array: [1 0 2 0 3 0 0 5 6 7 5 0 8]
Indices of zero elements: [ 1  3  5  6 11]

Explanation:

• arr == 0 creates a boolean mask.
• np.where() extracts the indices of True values.
• [0] is added to flatten the result into a 1-D array.

Using numpy.argwhere()

The numpy.argwhere() function returns the indices of elements that satisfy a condition, grouped by row/column in multi-dimensional arrays.

Example: In this example, we use a 2-D array and find all zero indices using np.argwhere().

import numpy as np

arr = np.array([[0, 2, 3], [4, 1, 0], [0, 0, 2]])
res = np.argwhere(arr == 0)

print("Array:")
print(arr)
print("Indices of zero elements:")
print(res)

Array:
[[0 2 3]
 [4 1 0]
 [0 0 2]]
Indices of zero elements:
[[0 0]
 [1 2]
 [2 0]
 [2 1]]

Explanation

• arr == 0 marks the zero elements.
• np.argwhere() returns their positions as coordinate pairs [row, col].

Using numpy.extract()

The numpy.extract() function returns elements that satisfy a condition. By combining it with np.arange(), we can extract the indices of zeros.

Example: Here, we use np.extract() to directly get the indices of zeros in a 1-D array.

import numpy as np

arr = np.array([1, 0, 2, 0, 3, 0, 0, 5, 6, 7, 5, 0, 8])
res = np.extract(arr == 0, np.arange(len(arr)))

print("Array:", arr)
print("Indices of zero elements:", res)

Array: [1 0 2 0 3 0 0 5 6 7 5 0 8]
Indices of zero elements: [ 1  3  5  6 11]

Explanation:

• arr == 0 creates a mask.
• np.arange(len(arr)) generates index positions.
• np.extract() selects indices where the condition is True.