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:

a – refers to the input array.
axis – along which axis the cumulative sum is performed.
dtype – specifies the data type of the output.
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.

Thanks for reading!!