5 Best Ways to Find Sum of Concatenated Pairs of Each Element in a Python List

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πŸ’‘ Problem Formulation: Imagine you are given a list of numbers and you are tasked with finding the sum of all possible concatenated pairs of these numbers. For instance, if the input is [1, 2, 3], you should treat the elements as strings and concatenate them in pairs: ’11’, ’12’, ’13’, ’21’, ’22’, ’23’, ’31’, ’32’, ’33’, and then sum their numerical values to get the final result, which is 330.

Method 1: Nested Loops

This method uses two nested loops to iterate over the list and concatenate each pair of elements. Each concatenated string is converted back to an integer before being added to the sum. It’s straightforward and easy to understand but might not be the most efficient for large lists.

Here’s an example:

list_of_numbers = [1, 2, 3]
sum_of_pairs = 0
for i in list_of_numbers:
    for j in list_of_numbers:
        sum_of_pairs += int(str(i) + str(j))
print(sum_of_pairs)

Output: 330

The above code snippet creates two loops, each iterating over the list_of_numbers. It concatenates each pair of elements, converts the result to an integer, and adds it to the sum_of_pairs, which is then printed out.

Method 2: List Comprehension

List comprehension in Python provides a concise way to create lists and can also be used to generate the concatenated pairs before summing them. This method is more Pythonic and utilizes the compact syntax of list comprehensions for readability.

Here’s an example:

list_of_numbers = [1, 2, 3]
sum_of_pairs = sum(int(str(i) + str(j)) for i in list_of_numbers for j in list_of_numbers)
print(sum_of_pairs)

Output: 330

This code uses a list comprehension combined with a generator expression to iterate through all possible pairs, concatenate and convert them to integers in one line before summing them up.

Method 3: itertools.product()

The itertools.product() function can be used to create a Cartesian product of the list with itself, effectively giving us all the pairs needed for concatenation. This method is clean and efficient, especially for larger lists.

Here’s an example:

import itertools

list_of_numbers = [1, 2, 3]
pairs = itertools.product(list_of_numbers, repeat=2)
sum_of_pairs = sum(int(str(a) + str(b)) for a, b in pairs)
print(sum_of_pairs)

Output: 330

In the snippet, itertools.product() is used to generate all possible pairs, which are then concatenated, converted to integers, and summed. This approach improves readability by abstracting the pair generation logic.

Method 4: Using map() and functools.reduce()

The map() function along with functools.reduce() can be used to apply the concatenation and summation logic across the list elements. This method leverages functional programming concepts.

Here’s an example:

from functools import reduce
import operator

list_of_numbers = [1, 2, 3]
concatenated = map(lambda x: int(str(x[0]) + str(x[1])), itertools.product(list_of_numbers, repeat=2))
sum_of_pairs = reduce(operator.add, concatenated)
print(sum_of_pairs)

Output: 330

In this code, itertools.product() is used to form pairs and map() concatenates them. The reduce() function with the add operator from operator module calculates the sum of the concatenated values.

Bonus One-Liner Method 5: Using Generator Expression Directly

For a succinct one-liner approach, a generator expression can directly concatenate and sum pairs within the sum() function. This method is concise but may sacrifice some readability.

Here’s an example:

print(sum(int(str(i)+str(j)) for i in [1, 2, 3] for j in [1, 2, 3]))

Output: 330

This one-liner leverages generator expressions within the sum function, iterating over each pair, concatenating their string representations, then converting them to integers and summing them on the fly.

Summary/Discussion

  • Method 1: Nested Loops. Simple and straightforward. Not efficient for very large lists.
  • Method 2: List Comprehension. Pythonic and compact. Can be a bit less performant due to list creation overhead.
  • Method 3: itertools.product(). Clean implementation. Efficient over large lists.
  • Method 4: map() and functools.reduce(). Functional approach. Higher complexity and potentially less accessible for beginners.
  • Method 5: One-Liner Generator Expression. Extremely concise. May be less clear to those unfamiliar with Python syntax.