| by Arround The Web | No comments

## NumPy np.cumsum()

The cumsum() function in NumPy allows you to calculate the cumulative sum of elements along a given axis.

Let us explore.

## Function Syntax

The function syntax is as shown below:

numpy.cumsum(a, axis=None, dtype=None, out=None)

## Function Parameters

The function returns the parameters as shown:

1. a – refers to the input array.
2. axis – along which axis the cumulative sum is performed.
3. dtype – specifies the data type of the output.
4. out – specifies the output array to store the result.

## Function Return Value

The function returns a new array with the cumulative sum of the input array elements.

## Example #1

The code below shows how to calculate the cumulative sum of a two-dimensional array along the None axis.

# import numpy
import numpy as np
arr = np.array([[1,2,3], [4,5,6]])
print(f"result: {np.cumsum(arr, axis=None)}")

The code above should flatten the array and an array holding the cumulative sum of the elements.

An example output is as shown:

result: [ 1  3  6 10 15 21]

## Example #2

The following example shows how to use the cumsum() function along the zero axis.

arr = np.array([[1,2,3], [4,5,6]])
print(f"result: {np.cumsum(arr, axis=0)}")

This should return:

result:
[[1 2 3]
[5 7 9]]

## Example #3

Along the axis=1, the function returns the result as:

arr = np.array([[1,2,3], [4,5,6]])
print(f"result: {np.cumsum(arr, axis=1)}")

The output array is as shown:

result:
[[ 1  3  6]
[ 4  9 15]]

## Conclusion

Using this article, you learned how to calculate the cumulative sum of elements along a given axis in an input array using the cumsum() function. Feel free to explore the docs for more.