 # 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] `