Numpy MaskedArray.sum() function | Python
numpy.MaskedArray.median()
function is used to compute the sum of the masked array elements over the given axis.
Syntax : numpy.ma.sum(arr, axis=None, dtype=None, out=None, keepdims=False)
Parameters:
arr : [ ndarray ] Input masked array.
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.
-> If provided, it must have a shape that the inputs broadcast to.
-> 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 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([[ 1 , 2 ], [ 3 , - 1 ], [ 5 , - 3 ]])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_array(in_arr, mask = [[ 1 , 0 ], [ 1 , 0 ], [ 0 , 0 ]])
print ( "Masked array : " , mask_arr)
out_arr = ma. sum (mask_arr)
print ( "sum 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]]
sum of masked array along default axis : 3
Code #2 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([[ 1 , 0 , 3 ], [ 4 , 1 , 6 ]])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_array(in_arr, mask = [[ 0 , 0 , 0 ], [ 0 , 0 , 1 ]])
print ( "Masked array : " , mask_arr)
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)
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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]
Last Updated :
18 Oct, 2019
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