Counting the number of non-NaN elements in a NumPy Array
Last Updated :
23 Jul, 2025
In this article, we are going to see how to count the number of non-NaN elements in a NumPy array in Python.
NAN: It is used when you don't care what the value is at that position. Maybe sometimes is used in place of missing data, or corrupted data.
Method 1: Using Condition
In this example, we will use one-dimensional arrays. In the below-given code, we loop over every entry of the given NumPy array and check if the value is a NaN or not.
Python3
import numpy as np
ex1 = np.array([1, 4, -9, np.nan])
ex2 = np.array([1, 45, -2, np.nan, 3,
-np.nan, 3, np.nan])
def approach_1(data):
# here the input data, is a numpy ndarray
# initialize the number of non-NaN elements
# in data
count = 0
# loop over each entry of the data
for entry in data:
# check whether the entry is a non-NaN value
# or not
if not np.isnan(entry):
# if not NaN, increment "count" by 1
count += 1
return count
print(approach_1(ex1))
print(approach_1(ex2))
Output:
3
5
Method 2: Using isnan()
Using the functionality of NumPy arrays, that we can perform an operation on the whole array at once, instead of a single element.
Used function:
- np.isnan(data): Returns a boolean array after performing np.isnan() operation on one of the entries of the array, data
- np.sum(): Since we are inputting a boolean array to the sum function, it returns the number of True values (1s) in the bool array.
Python3
import numpy as np
ex3 = np.array([[3, 4, -390, np.nan],
[np.nan, np.nan, np.nan, -90]])
def approach_2(data):
return np.sum(~np.isnan(data))
print(approach_2(ex3))
Output:
4
numpy.count_nonzero() function counts the number of non-zero values in the array arr.
Syntax : numpy.count_nonzero(arr, axis=None)
Parameters :
arr : [array_like] The array for which to count non-zeros.
axis : [int or tuple, optional] Axis or tuple of axes along which to count non-zeros. Default is None, meaning that non-zeros will be counted along a flattened version of arr.
Return : [int or array of int] Number of non-zero values in the array along a given axis. Otherwise, the total number of non-zero values in the array is returned.
Python3
import numpy as np
ex4 = np.array([[0.35834379, 0.67202438, np.nan, np.nan,
np.nan, 0.47870971],
[np.nan, np.nan, np.nan, 0.08113384,
0.70511741, 0.15260996],
[0.09028477, np.nan, 0.16639899,
0.47740582, 0.7259116, 0.94797347],
[0.80305651, np.nan, 0.67949724,
0.84112054, 0.15951702, 0.07510587],
[0.28643337, 0.00804256, 0.36775056,
0.19360266, 0.07288145, 0.37076932]])
def approach_3(data):
return data.size - np.count_nonzero(np.isnan(data))
print(approach_3(ex4))
Output:
22
