**π‘ Problem Formulation:** When working with lists in Python, there might be a need to sort the list items based on the numerical values of their digits. For example, given a list `['202', '32', '123']`

, the desired result after sorting on the basis of digits might be `['123', '202', '32']`

. This article explores five methods to accomplish this task.

## Method 1: Using the `sorted()`

Function with Custom Key

The `sorted()`

function in Python allows us to sort any iterable by specifying a custom key function. Here, we can define a key function that converts the list items to numerical types to compare their digits for sorting purposes.

Here’s an example:

def digits_key(s): return int(''.join(sorted(s))) numbers = ['202', '32', '123'] sorted_numbers = sorted(numbers, key=digits_key) print(sorted_numbers)

Output: `['123', '202', '32']`

This code snippet defines a key function `digits_key`

that transforms each string in the list to an integer after sorting the characters in the string. The list is then sorted based on these transformed integer values.

## Method 2: Directly Using `lambda`

Function

A Lambda function is an anonymous inline function that is used for a short duration and few operations. Using a lambda function as the sorting key can streamline the code and avoid defining a separate function.

Here’s an example:

numbers = ['202', '32', '123'] sorted_numbers = sorted(numbers, key=lambda s: int(''.join(sorted(s)))) print(sorted_numbers)

Output: `['123', '202', '32']`

This example directly passes a lambda function as the sorting key, where the lambda function performs the same digit sorting transformation as seen in Method 1.

## Method 3: Using a Custom Comparison Function with `functools.cmp_to_key`

Custom comparison functions allow for complex sorting logic. Python’s `functools`

module provides a utility to convert a comparison function to a key function. This method could be useful for more complicated sorting criteria.

Here’s an example:

from functools import cmp_to_key def compare_items(a, b): return int(''.join(sorted(a))) - int(''.join(sorted(b))) numbers = ['202', '32', '123'] sorted_numbers = sorted(numbers, key=cmp_to_key(compare_items)) print(sorted_numbers)

Output: `['123', '202', '32']`

This code snippet creates a custom comparison function `compare_items`

that sorts items based on their digits, which is then converted to a key function with `cmp_to_key`

from the `functools`

module.

## Method 4: Using List Comprehensions and Multiple Sorting Criteria

List comprehensions provide an alternative to lambda functions for creating a list of sorting keys, and using multiple sorting criteria can handle complex scenarios, allowing for more flexibility.

Here’s an example:

numbers = ['202', '32', '123'] sort_key = [(int(''.join(sorted(n))), n) for n in numbers] sorted_numbers = [item[1] for item in sorted(sort_key)] print(sorted_numbers)

Output: `['123', '202', '32']`

In this snippet, we use list comprehensions to create a list of tuples `sort_key`

where each tuple contains the integer sort key and the original item. We then sort the `sort_key`

list and extract the sorted items.

## Bonus One-Liner Method 5: Using Complex List Comprehension and Sorting Inline

Python is known for its ability to write concise one-liners. This method demonstrates how to combine list comprehension and inline sorting to achieve the desired result in a single statement.

Here’s an example:

numbers = ['202', '32', '123'] sorted_numbers = [n for _, n in sorted((int(''.join(sorted(n))), n) for n in numbers)] print(sorted_numbers)

Output: `['123', '202', '32']`

This one-liner creates a sorted list of tuples, as seen in Method 4, directly within a list comprehension, then selects the second element of each tuple to construct the sorted list of numbers.

## Summary/Discussion

**Method 1: Using**Very readable and maintainable. Might be slightly more verbose than necessary for small tasks.`sorted()`

Function with Custom Key.**Method 2: Directly Using Lambda Function.**Concise and convenient for short scripts. Lacks the explicitness of named functions which can impact readability.**Method 3: Using a Custom Comparison Function with**Allows for complex sorting logic. More verbose and potentially slower due to the overhead of comparison rather than using a sort key.`functools.cmp_to_key`

.**Method 4: Using List Comprehensions and Multiple Sorting Criteria.**Flexible and if needed, can handle additional sorting criteria. Can become unwieldy with increased complexity.**Bonus Method 5: One-Liner with Complex List Comprehension and Sorting Inline.**Extremely concise. However, it may be difficult to understand at a glance, thus not recommended for complex sorting logic.