NumPy Summations

  Summations in NumPy: Difference Between Addition and Summation

 

Addition vs Summation

Though addition and summation might sound similar, they have distinct purposes in programming:

• Addition involves combining two numbers or arrays.
• Summation refers to adding multiple elements, often over an entire array or along a specific axis.

Program: 

Addition
Let’s see an example of adding two arrays element-wise using NumPy:

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([1, 2, 3])
newarr = np.add(arr1, arr2)
print(newarr)

Output:

[2 4 6]

Here, np.add() adds the corresponding elements of arr1 and arr2.

Program: 

Summation
Now, let’s perform a summation across multiple arrays:

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([1, 2, 3])
newarr = np.sum([arr1, arr2])
print(newarr)

Output:

12

Here, np.sum() sums all elements from both arrays combined.

Summation Over an Axis

You can also sum along a specific axis using the axis parameter:

Program:

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([1, 2, 3])
newarr = np.sum([arr1, arr2], axis=1)
print(newarr)

Output:

[6 6]

axis=1 sums the elements within each array separately.

 Cumulative Sum

A cumulative sum (also called partial sum) adds elements progressively.
For example, the cumulative sum of [1, 2, 3, 4] results in [1, 3, 6, 10].
You can compute this using np.cumsum():

Program:

import numpy as np
arr = np.array([1, 2, 3])
newarr = np.cumsum(arr)
print(newarr)

Output:

[1 3 6]