Numpy MaskedArray.cumsum () Function | python



numpy.MaskedArray.cumsum() . However, their position is preserved and the result will be masked in the same places.

Syntax: numpy.ma.cumsum (axis = None, dtype = None, out = None)

Parameters:
axis: [int, optional] Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum 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: [cumsum_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.cumsum () 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) 

 
# We now 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.cumsum
# masked array methods

out_arr = mask_arr.cumsum () 

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

Output:

 Input array: [[1 2] [3 -1] [5 -3]] Masked array: [[- 2] [- -1] [5 -3]] cumulative sum of masked array along default axis: [- 2 - 1 6 3] 

Code # 2:

# Python program explaining
# numpy.MaskedArray.cumsum () 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.
# invalidating one entry.

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

print ( "Masked array:" , mask_arr) 

 
# applying MaskedArray.cumsum methods
# to the masked array

out_arr1 = mask_arr.cumsum (axis = 0

print ( " cumulative sum of masked array along 0 axis: "  , out_arr1)

 

out_arr2 = mask_arr.cumsum (axis = 1

print ( "cumulative sum of masked array along 1 axis:" , out_arr2)

 

Exit :

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