  # Numpy MaskedArray.average () Function | python

NumPy | Python Methods and Functions

`numpy.MaskedArray.average()` is used to return the weighted average of an array along a given axis.

Syntax: ` numpy.ma.average (arr, axis = None, weights = None, returned = False) `

Parameters:

arr: [array_like] Input masked array whose data to be averaged. Masked entries are not taken into account in the computation.
axis: [int, optional] Axis along which to average arr. If None, averaging is done over the flattened array.
weights: [array_like, optional] The importance that each element has in the computation of the average. If weights = None, then all data in arr are assumed to have a weight equal to one. If weights is complex, the imaginary parts are ignored.
returned: [bool, optional] It indicates whether a tuple (result, sum of weights) should be returned as output (True), or just the result (False). Default is False.

Return: [scalar or MaskedArray] The average along the specified axis. When returned is True, return a tuple with the average as the first element and the sum of the weights as the second element.

Code # 1:

 ` # Python program explaining ` ` # numpy.MaskedArray.average () method `   ` # import numy as a geek ` ` # and the 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. # making an entry invalid. mask_arr = ma.masked_array (in_arr, mask = [[[ 1 , 0  ], [ 1 , 0 ], [ 0 , 0 ]])  print ( "Masked array:" , mask_arr)    # apply MaskedArray.average # methods of the masked array out_arr = ma.average (mask_arr)  print ( "normal average of masked array: " , out_arr) `

Output:

` Input array: [[1 2] [3 -1] [5 -3] ] Masked array: [[- 2] [- -1] [5 -3]] normal average of masked array: 0.75 `

Code # 2:

 ` # Python program explaining ` ` # numpy.MaskedArray.average () method `   ` # import numy as a geek ` ` # and the 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) `   ` # We are now creating a masked array. ` ` # invalidating the post. ` ` mask_arr ` ` = ` ` ma.masked_array (in_arr, mask ` ` = ` ` [[` ` 1 ` `, ` ` 0 ` `], [` ` 1 ` `, ` ` 0 ` `], [` ` 0 ` `, ` ` 0 ` `]]) ` ` print ` ` (` ` "Masked array:" ` `, mask_arr) `   ` # apply MaskedArray.average ` ` # masked array methods ` ` out_arr ` ` = ` ` ma.average (mask_arr, weights ` ` = ` ` [[` ` 0 ` `, ` ` 1 ` `], [` ` 0 ` `, ` ` 2 ], [ 3 , 1 ]]) `` print ( "weighted average of masked array:" , out_arr) `

Output:

` Input array: [[1 2] [3 -1] [5 -3]] Masked array: [[- 2] [- -1] [5 -3]] weighted average of masked array: 1.7142857142857142 `