You can use the following basic syntax to count the number of elements equal to **True** in a NumPy array:

import numpy as np np.count_nonzero(my_array)

This particular example will return the number of elements equal to **True** in the NumPy array called **my_array**.

The following example shows how to use this syntax in practice.

**Example: Count Number of Elements Equal to True in NumPy Array**

The following code shows how to use the **count_nonzero()** function to count the number of elements in a NumPy array equal to True:

import numpy as np #create NumPy array my_array = np.array([True, False, False, False, True, True, False, True, True]) #count number of values in array equal to True np.count_nonzero(my_array) 5

From the output we can see that **5** values in the NumPy array are equal to **True**.

We can manually look at the NumPy array to verify that there are indeed three elements equal to **True** in the array.

If you would instead like to count the number of element equal to **False**, you can subtract the results from the **count_nonzero()** function from the **size()** function as follows:

import numpy as np #create NumPy array my_array = np.array([True, False, False, False, True, True, False, True, True]) #count number of values in array equal to False np.size(my_array) - np.count_nonzero(my_array) 4

From the output we can see that **4** values in the NumPy array are equal to **False**.

**Note**: If you have any NaN values in your NumPy array, the **count_nonzero()** function will count each NaN value as an element equal to True.

**Additional Resources**

The following tutorials explain how to perform other common operations in Python:

How to Calculate the Mode of NumPy Array

How to Map a Function Over a NumPy Array

How to Sort a NumPy Array by Column