You can use the following basic syntax to replace NaN values with zero in NumPy:

my_array[np.isnan(my_array)] = 0

This syntax works with both matrices and arrays.

The following examples show how to use this syntax in practice.

Example 1: Replace NaN Values with Zero in NumPy Array

The following code shows how to replace all NaN values with zero in a NumPy array:

import numpy as np

#create array of data
my_array = np.array([4, np.nan, 6, np.nan, 10, 11, 14, 19, 22])

#replace nan values with zero in array
my_array[np.isnan(my_array)] = 0

#view updated array
print(my_array)

[ 4.  0.  6.  0. 10. 11. 14. 19. 22.]

Notice that both NaN values in the original array have been replaced with zero.

Example 2: Replace NaN Values with Zero in NumPy Matrix

Suppose we have the following NumPy matrix:

import numpy as np

#create NumPy matrix
my_matrix = np.matrix(np.array([np.nan, 4, 3, np.nan, 8, 12]).reshape((3, 2)))

#view NumPy matrix
print(my_matrix)

[[nan  4.]
 [ 3. nan]
 [ 8. 12.]]

We can use the following code to replace all NaN values with zero in the NumPy matrix:

#replace nan values with zero in matrix
my_matrix[np.isnan(my_matrix)] = 0

#view updated array
print(my_matrix)

[[ 0.  4.]
 [ 3.  0.]
 [ 8. 12.]]

Notice that both NaN values in the original matrix have been replaced with zero.

Related:

The following tutorials explain how to perform other common tasks in NumPy: