Counting the number of non-NaN elements in a NumPy Array
Last Updated :
03 Apr, 2023
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):
count = 0
for entry in data:
if not np.isnan(entry):
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
Like Article
Suggest improvement
Share your thoughts in the comments
Please Login to comment...