numpy.cumsum() function is used when we want to compute the cumulative sum of array elements over a given axis.
Syntax : numpy.cumsum(arr, axis=None, dtype=None, out=None)
Parameters :
arr : [array_like] Array containing numbers whose cumulative sum is desired. If arr is not an array, a conversion is attempted.
axis : Axis along which the cumulative sum is 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. If dtype is not specified, it defaults to the dtype of arr, unless arr has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.
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 : Working
# Python program explaining# numpy.cumsum() functionimport numpy as geek in_num = 10 print ("Input number : ", in_num) out_sum = geek.cumsum(in_num) print ("cumulative sum of input number : ", out_sum) |
Output :
Input number : 10 cumulative sum of input number : [10]
Code #2 :
# Python program explaining# numpy.cumsum() function import numpy as geek in_arr = geek.array([[2, 4, 6], [1, 3, 5]]) print ("Input array : ", in_arr) out_sum = geek.cumsum(in_arr) print ("cumulative sum of array elements: ", out_sum) |
Output :
Input array : [[2 4 6] [1 3 5]] cumulative sum of array elements: [ 2 6 12 13 16 21]
Code #3 :
# Python program explaining# numpy.cumsum() function import numpy as geek in_arr = geek.array([[2, 4, 6], [1, 3, 5]]) print ("Input array : ", in_arr) out_sum = geek.cumsum(in_arr, axis = 1) print ("cumulative sum of array elements taking axis 1: ", out_sum) |
Output :
Input array : [[2 4 6] [1 3 5]] cumulative sum of array elements taking axis 1: [[ 2 6 12] [ 1 4 9]]
