sciPy stats.nanmean() function | Python

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scipy.stats.nanmean(array, axis=0) function calculates the arithmetic mean by ignoring the Nan (not a number) values of the array elements along the specified axis of the array.

It’s formula –

Parameters :
array : Input array or object having the elements, including Nan values, to calculate the arithmetic mean.
axis : Axis along which the mean is to be computed. By default axis = 0.

Returns : Arithmetic mean of the array elements (ignoring the Nan values) based on the set parameters.

Code #1:

# Arithmetic Mean  
import scipy 
import numpy as np 
  
arr1 = [1, 3, np.nan, 27]  
   
print("Arithmetic Mean using nanmean :", scipy.nanmean(arr1)) 
  
print("Arithmetic Mean without handling nan value :", scipy.mean(arr1))  

Output :

Arithmetic Mean using nanmean : 10.333333333333334
Arithmetic Mean without handling nan value : nan

Code #2: With multi-dimensional data

# Arithmetic Mean  
from scipy import mean 
from scipy import nanmean 
import numpy as np 
  
arr1 = [[1, 3, 27],  
        [3, np.nan, 6],  
        [np.nan, 6, 3],  
        [3, 6, np.nan]]  
   
print("Arithmetic Mean is :", mean(arr1))  
print("Arithmetic Mean handling nan :", nanmean(arr1))  
  
# using axis = 0 
print("\nArithmetic Mean is with default axis = 0 : \n",  
      mean(arr1, axis = 0)) 
print("\nArithmetic Mean handling nan with default axis = 0 : \n",  
      nanmean(arr1, axis = 0)) 
  
# using axis = 1 
print("\nArithmetic Mean is with default axis = 1 : \n",  
      mean(arr1, axis = 1))   
print("\nArithmetic Mean handling nan with default axis = 1 : \n",  
      nanmean(arr1, axis = 1))   

Output :

Arithmetic Mean is : nan
Arithmetic Mean handling nan : 6.444444444444445

Arithmetic Mean is with default axis =0 : 
 [nan nan nan]

Arithmetic Mean handling nan with default axis =0 : 
 [ 2.33333333  5.         12.        ]

Arithmetic Mean is with default axis =1 : 
 [10.33333333         nan         nan         nan]

Arithmetic Mean handling nan with default axis =1 : 
 [10.33333333  4.5         4.5         4.5       ]