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# Python Data Science NumPy ufunc Summations

What is the difference between summation and addition?

Addition is done between two arguments whereas summation happens over n elements.

Example of Summation: newarr = np.sum([arr1, arr2])

Example 1: Add the values in arr1 to the values in arr2.

Code

import numpy as np

arr1 = np.array([2, 3, 4])
arr2 = np.array([5, 6, 3])

print(newarr)

the output will be

[7 9 7]

Example 2: Sum the values in arr1 and the values in arr2.

Code

import numpy as np

arr1 = np.array([2, 3, 4])
arr2 = np.array([5, 6, 3])

newarr = np.sum([arr1, arr2])

print(newarr)

the output will be
23

Summation Over an Axis: If you specify axis=1, NumPy will sum the numbers in each array.

Example 3: Perform summation in the following array over 1st axis.

Code

import numpy as np

arr1 = np.array([2, 3, 4])
arr2 = np.array([5, 6, 3])

newarr = np.sum([arr1, arr2], axis=1)

print(newarr)

the output will be

[ 9 14]

Cummulative Sum: Cummulative sum means partially adding the elements in array.

E.g. The partial sum of [1, 2, 3, 4, 5] would be [1, 1+2, 1+2+3, 1+2+3+4, 1+2+3+4+5] = [1, 3, 6, 10, 15].

Perfom partial sum with the cumsum() function.

Example 4: Perform cummulative summation in the following array.

Code

import numpy as np

arr = np.array([1, 2, 3, 4])

newarr = np.cumsum(arr)

print(newarr)

the output will be

[ 1 3 6 10]
Note: NumPy ufunc Universal function or ufuncs is a function which operates on ndarrays in an element by element fashion and supports array broadcasting, type casting, and many other standard features.