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: How to Remove NaN Values from NumPy Array
Additional Resources
The following tutorials explain how to perform other common tasks in NumPy:
How to Fill NumPy Array with Values
How to Remove Specific Elements from NumPy Array
How to Replace Elements in NumPy Array
How to Get Specific Row from NumPy Array
