Numpy MaskedArray.mean () Function | python

numpy.MaskedArray.mean() is used to return the average value of masked array elements along a given axis. Here, masked entries are ignored and leaf items that are not leaf 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.
 – & gt; If provided, it must have a shape that the inputs broadcast to.
 – & gt; 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:

# Program Python explaining
# numpy.MaskedArray.mean () method

 
# import numy as geek
# and numpy.ma module as ma

import numpy as geek 

import numpy.ma as ma 

 
# create input array

in _arr = geek.array ([[ 1 , 2 ], [ 3 , - 1 ], [ 5 , - 3 ]])

print ( " Input array: " , in_arr) 

 
# Now we create a masked array.
# invalidate the post.

mask_arr = ma.masked_array (in_arr, mask = [[ 1 , 0 ], [ 1 , 0 ], [ 0 , 0 ]]) 

print ( "Masked array:" , mask_arr) 

 
# apply MaskedArray.mean
# methods of the masked array

out_arr = mask_arr.mean () 

print ( "mean of masked array along default axis: " , out_arr) 

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:

# Python program explaining
# numpy.MaskedArray.mean () method

 
# import numy as geek
# and numpy.ma module as ma

import numpy as geek 

import numpy. ma as ma 

 
# create input array

in_arr = geek.array ([[ 1 , 0 , 3 ], [ 4 , 1 , 6 ]]) 

print ( "Input array:" , in_arr)

 
# Now we create a masked array.
# making one the entry is invalid.

mask_arr = ma. masked_array (in_arr, mask = [[ 0 , 0 , 0 ], [ 0 , 0 , 1 ]]) 

print ( "Masked array:" , mask_arr) 

 
# applying MaskedArray.mean methods
# in 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)

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]