Numpy MaskedArray.cumsum() function | Python

numpy.MaskedArray.cumsum() Return the cumulative sum of the masked array elements over the given axis.Masked values are set to 0 internally during the computation. However, their position is saved, and the result will be masked at the same locations.

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.
  -> If provided, it must have a shape that the inputs broadcast to.
  -> 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 :

# Python program explaining
# numpy.MaskedArray.cumsum() method

# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma

# creating input array
in_arr = geek.array([[1, 2], [ 3, -1], [ 5, -3]])
print ("Input array : ", in_arr)

# Now we are creating a masked array.
# by making  entry as invalid.
mask_arr = ma.masked_array(in_arr, mask =[[1, 0], [ 1, 0], [ 0, 0]])
print ("Masked array : ", mask_arr)

# applying MaskedArray.cumsum
# methods to masked array
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

# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma

# creating input array
in_arr = geek.array([[1, 0, 3], [ 4, 1, 6]])
print ("Input array : ", in_arr)

# Now we are creating a masked array.
# by making one entry as invalid.
mask_arr = ma.masked_array(in_arr, mask =[[ 0, 0, 0], [ 0, 0, 1]])
print ("Masked array : ", mask_arr)

# applying MaskedArray.cumsum methods
# to 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)

Output:

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]