Make pair from two list such that elements are not same in pairs - Python

Last Updated : 11 Jul, 2025

We are given two lists, and our task is to create pairs where the elements in each pair must be different. For example, given a = [1, 2, 3] and b = [2, 3, 4], valid pairs would be [(1, 2), (1, 3), (1, 4), (2, 4), (3, 2), (3, 4)], ensuring that no pair has the same elements. Let's explore different methods to do this in Python.

Using List Comprehension

We can use a simple list comprehension to generate all possible pairs while ensuring that the elements are different.

Python 
# Initializing lists  
a = [1, 2, 3]  
b = [2, 3, 4]  

# Generating pairs where elements are different  
pairs = [(x, y) for x in a for y in b if x != y]  

print(pairs)

Output
[(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 2), (3, 4)]

Explanation:

  • We iterate over each element in a and b.
  • The condition x != y ensures that elements in the pair are not the same.

Let's explore some more ways to create pairs where the elements in each pair are different.

Table of Content

Using itertools.product()

itertools.product() function generates all possible pairs, and we can filter out invalid ones using a condition.

Python 
from itertools import product  

# Initializing lists  
a = [1, 2, 3]  
b = [2, 3, 4]  

# Generating pairs using itertools  
pairs = [(x, y) for x, y in product(a, b) if x != y]  

print(pairs)

Output
[(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 2), (3, 4)]

Explanation:

  • product(a, b) generates all possible pairs.
  • The condition x != y filters out invalid pairs.
  • This method is useful for large lists as itertools is optimized for performance.

Using filter() with product()

We can use the filter() function to remove invalid pairs while using product().

Python 
from itertools import product  

# Initializing lists  
a = [1, 2, 3]  
b = [2, 3, 4]  

# Using filter to remove pairs where elements are the same  
pairs = list(filter(lambda pair: pair[0] != pair[1], product(a, b)))  

print(pairs)

Output
[(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 2), (3, 4)]

Explanation:

  • product(a, b) generates all possible pairs.
  • filter() removes pairs where elements are the same.

Using Nested Loops

We can also use explicit loops to generate pairs while ensuring that elements are different.

Python 
# Initializing lists  
a = [1, 2, 3]  
b = [2, 3, 4]  

# Generating pairs using loops  
pairs = []  
for x in a:  
    for y in b:  
        if x != y:  
            pairs.append((x, y))  

print(pairs)

Output
[(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 2), (3, 4)]

Explanation:

  • This method is easy to understand but less concise than list comprehension.
  • It is useful when additional conditions need to be applied inside the loop.

Using NumPy for Large Lists

For larger datasets, NumPy provides an efficient way to generate and filter pairs.

Python 
import numpy as np  

# Initializing lists  
a = np.array([1, 2, 3])  
b = np.array([2, 3, 4])  

# Creating all pairs  
pairs = np.array(np.meshgrid(a, b)).T.reshape(-1, 2)  

# Filtering out invalid pairs  
pairs = pairs[pairs[:, 0] != pairs[:, 1]]  

print(pairs)

Output
[[1 2]
 [1 3]
 [1 4]
 [2 3]
 [2 4]
 [3 2]
 [3 4]]

Explanation:

  • np.meshgrid(a, b) creates a grid of values.
  • T.reshape(-1, 2) converts it into a list of pairs.
  • The condition pairs[:, 0] != pairs[:, 1] filters out invalid pairs.
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