Finding Duplicate, Unique, and Common Elements Between Two Arrays Using NumPy

Program Overview

This Python program uses the NumPy library to create two arrays.
It then extracts and displays the following elements using NumPy functions:
- Duplicate elements
- Unique (non-repeating) elements
- Common elements between the two arrays

Python Code:

import numpy as np

# Create two sample arrays
array1 = np.array([1, 2, 3, 4, 5, 6, 2, 3])
array2 = np.array([3, 4, 7, 8, 3, 9, 10])

# Common elements between both arrays
common = np.intersect1d(array1, array2)

# Unique elements in each array
unique1 = np.setdiff1d(array1, array2)
unique2 = np.setdiff1d(array2, array1)

# Duplicate elements in each array
duplicates1 = np.unique(array1[np.isin(array1, array1[np.where(np.bincount(array1) > 1)])])
duplicates2 = np.unique(array2[np.isin(array2, array2[np.where(np.bincount(array2) > 1)])])

print("Array 1:", array1)
print("Array 2:", array2)
print("Common elements:", common)
print("Unique elements in Array 1:", unique1)
print("Unique elements in Array 2:", unique2)
print("Duplicate elements in Array 1:", duplicates1)
print("Duplicate elements in Array 2:", duplicates2)

Sample Output:

Array 1: [1 2 3 4 5 6 2 3]  
Array 2: [3 4 7 8 3 9 10]  
Common elements: [3 4]  
Unique elements in Array 1: [1 2 5 6]  
Unique elements in Array 2: [ 7  8  9 10]  
Duplicate elements in Array 1: [2 3]  
Duplicate elements in Array 2: [3]

Explanation:

- np.intersect1d() finds common elements between the two arrays
- np.setdiff1d() extracts elements that are unique to each array
- np.bincount() and np.where() help identify repeated values
- np.unique() ensures that duplicates are listed only once