Tutorial

numpy.cumsum() in Python

Published on August 3, 2022
author

Pankaj

numpy.cumsum() in Python

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Python numpy cumsum() function returns the cumulative sum of the elements along the given axis.

Python numpy cumsum() syntax

The cumsum() method syntax is:

cumsum(array, axis=None, dtype=None, out=None)
  • The array can be ndarray or array-like objects such as nested lists.
  • The axis parameter defines the axis along which the cumulative sum is calculated. If the axis is not provided then the array is flattened and the cumulative sum is calculated for the result array.
  • The dtype parameter defines the output data type, such as float and int.
  • The out optional parameter is used to specify the array for the result.

Python numpy cumsum() Examples

Let’s look at some examples of calculating cumulative sum of numpy array elements.

1. Cumulative Sum of Numpy Array Elements without axis

import numpy as np

array1 = np.array(
    [[1, 2],
     [3, 4],
     [5, 6]])

total = np.cumsum(array1)
print(f'Cumulative Sum of all the elements is {total}')

Output: Cumulative Sum of all the elements is [ 1 3 6 10 15 21] Here, the array is first flattened to [ 1 2 3 4 5 6]. Then the cumulative sum is calculated, resulting in [ 1 3 6 10 15 21].

2. Cumulative Sum along the axis

import numpy as np

array1 = np.array(
    [[1, 2],
     [3, 4],
     [5, 6]])

total_0_axis = np.cumsum(array1, axis=0)
print(f'Cumulative Sum of elements at 0-axis is:\n{total_0_axis}')

total_1_axis = np.cumsum(array1, axis=1)
print(f'Cumulative Sum of elements at 1-axis is:\n{total_1_axis}')

Output:

Cumulative Sum of elements at 0-axis is:
[[ 1  2]
 [ 4  6]
 [ 9 12]]
Cumulative Sum of elements at 1-axis is:
[[ 1  3]
 [ 3  7]
 [ 5 11]]

3. Specifying data type for the cumulative sum array

import numpy as np

array1 = np.array(
    [[1, 2],
     [3, 4],
     [5, 6]])

total_1_axis = np.cumsum(array1, axis=1, dtype=float)
print(f'Cumulative Sum of elements at 1-axis is:\n{total_1_axis}')

Output:

Cumulative Sum of elements at 1-axis is:
[[ 1.  3.]
 [ 3.  7.]
 [ 5. 11.]]

Reference: API Doc

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