How to Pair Elements with Rear elements in Matrix Row Using Python?

In some scenarios of programming, you might have faced a time where you need to pair each element of matrices in a row with the rear element, in other words the element which appears immediately after the number. So, in this article we will be looking at some methods and examples to pair the elements according to the conditions.

Matrix

These are powerful data structures used to represent collections of elements organized in rows and columns. Like matrix having n row and m column will be called as n * m matrix.

Here are some methods which we can perform to pair the elements of the matrix.

Nested Loop

This is a simple and primary method which comes in the most of the developer’s mind. Here we can use nested loops to iterate over each row of the matrix. Within the inner loop, we iterate from the first element of the row to the second-to-last element, as there is no subsequent element after the last one. The curr and nxt variables store the current element and the element that follows it which is the rear element.

Example

mat= [
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
]
def pair_with_rear(mat):
   for row in mat:
   for i in range(len(row) - 1):
      curr = row[i]
      nxt = row[i + 1]
      print(f"Pair: {curr} - {nxt}")

pair_with_rear(mat)

List Comprehension

This method uses list comprehension to create a pair of elements. This comprehension traverses on each row in the matrix and in each row it pairs each element with the rear element using (row[i], row[i+1]) which is stored in the pair list.

Example

mat = [
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
]

def pair_with_rear(mat):
   pairs = [(row[i], row[i + 1])
   for row in mat
   for i in range(len(row) - 1)]

   for pair in pairs:
      print(f"Pair: {pair[0]} - {pair[1]}")

pair_with_rear(mat)

Output

Pair: 1 - 2
Pair: 2 - 3
Pair: 4 - 5
Pair: 5 - 6
Pair: 7 - 8
Pair: 8 - 9

List Compression and Zip Method

This method uses the zip() function in conjunction with the list comprehension to pair each row element. This function takes the element from each iteration (in the below program it is the current row and sliced row with the elements from the second position) and combines them into tuples which are getting stored in the pairs list.

Example

mat = [
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
]

def pair_with_rear(mat):
   pairs = [(curr, nxt)
   for row in mat
   for curr, nxt in zip(row, row[1:])]
   for pair in pairs:
      print(f"Pair: {pair[0]} - {pair[1]}")
pair_with_rear(mat)

Pair: 1 - 2
Pair: 2 - 3
Pair: 4 - 5
Pair: 5 - 6
Pair: 7 - 8
Pair: 8 - 9

NumPy Library

We use the NumPy library for the efficient process of the matrix. Firstly, we flattened the matrix to convert it into the 1D array using np.array(mat).flatten(). Then using list comprehension we create pairs by iterating over the flattened array and pair each element with its rear or subsequent element.

Example

import numpy as np
mat = [
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
]
def pair_with_rear(matrix):
   flattened_matrix = np.array(matrix).flatten()
   pairs = [(flattened_matrix[i], flattened_matrix[i + 1])

   for i in range(len(flattened_matrix) - 1)]
   for pair in pairs:
      print(f"Pair: {pair[0]} - {pair[1]}")

pair_with_rear(mat)

Output

Pair: 1 - 2
Pair: 2 - 3
Pair: 3 - 4
Pair: 4 - 5
Pair: 5 - 6
Pair: 6 - 7
Pair: 7 - 8
Pair: 8 - 9

Itertools Linerary

We use Itertools library to handle the pairing operations. Similar to the Numpy Library approach in this method, we also flatten the array into the 1D matrix and concatenate all the rows of the matrix into a single 1D iterable array. Then using the list comprehension, the pairs are created by iterating over the flatten list, where the current element is denoted by flatten_mat[i] and the next element is denoted by flatten_mat[i+1] which is stored in the pairs list.

Example

import itertools
mat = [
   [1, 2, 3],
   [4, 5, 6],
   [7, 8, 9]
]

def pair_with_rear(matrix):
   flattened_matrix = list(itertools.chain.from_iterable(matrix))
   pairs = [(flattened_matrix[i], flattened_matrix[i + 1])
   for i in range(len(flattened_matrix) - 1)]
   for pair in pairs:
      print(f"Pair: {pair[0]} - {pair[1]}")
pair_with_rear(mat)

Output

Pair: 1 - 2
Pair: 2 - 3
Pair: 3 - 4
Pair: 4 - 5
Pair: 5 - 6
Pair: 6 - 7
Pair: 7 - 8
Pair: 8 - 9

So, you may choose any method which you feel comfortable with. Each of the methods provide the way to pair the elements with its rear/subsequent element.