Numpy MaskedArray.mean() function | Python

numpy.MaskedArray.mean() function is used to return the average of the masked array elements along given axis.Here masked entries are ignored, and result elements which are not finite will be masked.

Syntax : numpy.ma.mean(axis=None, dtype=None, out=None)

Parameters:



axis :[ int, optional] Axis along which the mean is computed. The default (None) is to compute the mean over the flattened array.
dtype : [dtype, optional] Type of the returned array, as well as of the accumulator in which the elements are multiplied.
out : [ndarray, optional] A location into which the result is stored.
  -> If provided, it must have a shape that the inputs broadcast to.
  -> If not provided or None, a freshly-allocated array is returned.

Return : [mean_along_axis, ndarray] A new array holding the result is returned unless out is specified, in which case a reference to out is returned.

Code #1 :

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# Python program explaining
# numpy.MaskedArray.mean() method 
    
# importing numpy as geek  
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 
    
# creating input array  
in_arr = geek.array([[1, 2], [ 3, -1], [ 5, -3]])
print ("Input array : ", in_arr) 
    
# Now we are creating a masked array. 
# by making  entry as invalid.  
mask_arr = ma.masked_array(in_arr, mask =[[1, 0], [ 1, 0], [ 0, 0]]) 
print ("Masked array : ", mask_arr) 
    
# applying MaskedArray.mean    
# methods to masked array
out_arr = mask_arr.mean() 
print ("mean of masked array along default axis : ", out_arr) 

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Output:

Input array :  [[ 1  2]
 [ 3 -1]
 [ 5 -3]]
Masked array :  [[-- 2]
 [-- -1]
 [5 -3]]
mean of masked array along default axis :  0.75

 

Code #2 :

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# Python program explaining
# numpy.MaskedArray.mean() method 
     
# importing numpy as geek  
# and numpy.ma module as ma 
import numpy as geek 
import numpy.ma as ma 
     
# creating input array 
in_arr = geek.array([[1, 0, 3], [ 4, 1, 6]]) 
print ("Input array : ", in_arr)
      
# Now we are creating a masked array. 
# by making one entry as invalid.  
mask_arr = ma.masked_array(in_arr, mask =[[ 0, 0, 0], [ 0, 0, 1]]) 
print ("Masked array : ", mask_arr) 
     
# applying MaskedArray.mean methods 
# to masked array
out_arr1 = mask_arr.mean(axis = 0
print ("mean of masked array along 0 axis : ", out_arr1)
  
out_arr2 = mask_arr.mean(axis = 1
print ("mean of masked array along 1 axis : ", out_arr2)

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Output:

Input array :  [[1 0 3]
 [4 1 6]]
Masked array :  [[1 0 3]
 [4 1 --]]
mean of masked array along 0 axis :  [2.5 0.5 3.0]
mean of masked array along 1 axis :  [1.3333333333333333 2.5]


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