  # Numpy MaskedArray.sum () Function | python

NumPy | Python Methods and Functions

`numpy.MaskedArray.median()` is used to calculate the sum of masked array elements along a given axis.

Syntax: ` numpy.ma.sum (arr, axis = None, dtype = None, out = None, keepdims = False) `

Parameters:

axis: [int, optional] Axis along which the sum is computed. The default (None) is to compute the sum 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.
keepdims: [bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Return: [sum_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.sum () 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. # invalidating the entry. mask_arr = ma.masked_array (in_arr, mask = [[ 1 , 0 ], [ 1 , 0 ], [ 0 , 0 ]])  print ( "Masked array:" , mask_arr)    # apply MaskedArray.sum # methods of the masked array out_arr = ma. sum (mask_arr)  print ( "sum of masked array along default axis: " , out_arr)  `

` ` Output :

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

Code # 2:

 ` # Python program explaining ` ` # numpy.MaskedArray.sum () method `   ` # import numy as a geek ` ` # and the numpy module. ma as ma ` ` import ` ` numpy as geek ` ` import ` ` numpy.ma as ma `   ` # create input array ` < code class = "plain"> in_arr ` = ` ` geek.array ([[` ` 1 ` `, ` ` 0 ` `, ` ` 3 ` `], [` ` 4 ` `, ` ` 1 ` `, ` ` 6 ` `]]) ` ` print ` ` (` ` "Input array:" ` `, in_arr) `   ` # We are now creating a masked array. ` ` # invalidating one entry. ` ` mask_arr ` ` = ` ` ma.masked_array (in_arr, mask ` ` = ` ` [[` 0 `, ` ` 0 ` `, 0 ], [ 0 , 0 , 1 ]]) `` print ( "Masked array:" , mask_arr)    # applying the MaskedArray.sum methods # into the masked array out_arr1 = ma. sum (mask_arr, axis = 0 )  print ( " sum of masked array along 0 axis: " , out_arr1)   out_arr2 = ma. sum (mask_arr, axis = 1 )  print ( "sum of masked array along 1 axis:" , out_arr2) `

Output:

``` Input array: [[1 0 3] [4 1 6]] Masked array: [[1 0 3] [4 1 -]] sum of masked array along 0 axis: [5 1 3] sum of masked array along 1 axis: [4 5]