Numpy recarray.cumsum() function | Python

In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is [(a, int), (b, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['a'] and arr['b']. Record arrays allow the fields to be accessed as members of the array, using arr.a and arr.b.

numpy.recarray.cumsum() function returns the cumulative sum of array elements over a given axis.

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



Parameters:
axis : Axis along which the cumulative sumis computed. The default is to compute the sum of the flattened array.
dtype : 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 : A new array holding the result is returned unless out is specified, in which case it is returned.

Code #1 :

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# Python program explaining
# numpy.recarray.cumsum() method 
  
# importing numpy as geek
import numpy as geek
  
# creating input array with 2 different field 
in_arr = geek.array([[(5.0, 2), (3.0, -4), (6.0, 9)],
                     [(9.0, 1), (5.0, 4), (-12.0, -7)]],
                     dtype =[('a', float), ('b', int)])
  
print ("Input array : ", in_arr)
  
# convert it to a record array,
# using arr.view(np.recarray)
rec_arr = in_arr.view(geek.recarray)
print("Record array of float: ", rec_arr.a)
print("Record array of int: ", rec_arr.b)
  
# applying recarray.cumsum methods
# to float record array along axis 1
out_arr = rec_arr.a.cumsum( axis = 1)
print ("Output array along axis 1: ", out_arr) 
  
# applying recarray.cumsum methods 
# to int record array along default axis
out_arr = rec_arr.b.cumsum()
print ("Output  array along default axis : ", out_arr) 

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Output:

Input array :  [[(  5.,  2) (  3., -4) (  6.,  9)]
 [(  9.,  1) (  5.,  4) (-12., -7)]]
Record array of float:  [[  5.   3.   6.]
 [  9.   5. -12.]]
Record array of int:  [[ 2 -4  9]
 [ 1  4 -7]]
Output array along axis 1:  [[ 5.  8. 14.]
 [ 9. 14.  2.]]
Output  array along default axis :  [ 2 -2  7  8 12  5]


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