**π‘ Problem Formulation:** We often encounter a need to compute the sum of the lengths of multiple strings within a list, specifically at given indices. For example, consider a list of strings `["hello", "world", "python", "coding"]`

and a set of indices `[1, 2]`

. The desired output is the sum of the lengths of strings at index 1 and 2, which is `len("world") + len("python") = 5 + 6 = 11`

.

## Method 1: Using a For-Loop

This method involves iterating through the given list of indices, accessing each string by its index, calculating the length, and adding it to a running sum. It is very straightforward and can be easily understood by beginners.

Here’s an example:

strings = ["hello", "world", "python", "coding"] indices = [1, 2] sum_lengths = 0 for index in indices: sum_lengths += len(strings[index])

Output: `11`

This code snippet creates a sum variable called `sum_lengths`

, loops over each index in the list `indices`

, and adds the length of the string at that index to `sum_lengths`

. After the loop, `sum_lengths`

contains the total sum of the lengths of the strings at the given indices.

## Method 2: Using List Comprehension

List comprehension provides a more concise and pythonic way to achieve the same result. This method comprehends an on-the-fly list of lengths and sums it up, all in a single line of code.

Here’s an example:

strings = ["hello", "world", "python", "coding"] indices = [1, 2] sum_lengths = sum(len(strings[index]) for index in indices)

Output: `11`

Using a generator expression within the `sum()`

function, we generate the lengths of the strings at the specified indices on-the-fly and sum them up immediately, leading to the same result with less code.

## Method 3: Using the map Function

The `map()`

function is utilized to apply a function to every item of an iterable. When combined with a lambda function, it can be used to calculate lengths and sum them elegantly.

Here’s an example:

strings = ["hello", "world", "python", "coding"] indices = [1, 2] sum_lengths = sum(map(lambda i: len(strings[i]), indices))

Output: `11`

The `map()`

function applies a lambda function that returns the length of each string at given indices. The resulting map object is then passed directly into the `sum()`

function to calculate the total sum.

## Method 4: Using the operator Module

For those preferring not to use lambda expressions, the `operator`

module provides functional alternatives. The `itemgetter()`

function can be used to fetch items and sum their lengths.

Here’s an example:

from operator import itemgetter strings = ["hello", "world", "python", "coding"] indices = [1, 2] sum_lengths = sum(len(s) for s in itemgetter(*indices)(strings))

Output: `11`

A combination of `itemgetter()`

with a list comprehension is used here. First, the `itemgetter()`

function fetches the desired string elements, and then the lengths of these strings are calculated and summed.

## Bonus One-Liner Method 5: Using numpy

For those who work with numerical computations in Python, `numpy`

provides efficient array processing capabilities. By converting the list to a NumPy array first, the task can be greatly simplified.

Here’s an example:

import numpy as np strings = np.array(["hello", "world", "python", "coding"]) indices = [1, 2] sum_lengths = sum(len(s) for s in strings[indices])

Output: `11`

Here we create a NumPy array from the list of strings, use array indexing to extract the relevant strings, and then sum their lengths. This can be more efficient than pure Python approaches, especially for large datasets.

## Summary/Discussion

**Method 1:**For-Loop. Simple and straightforward. May be slower for very large lists of indices.**Method 2:**List Comprehension. Pythonic and concise. Efficient but may be less readable to newcomers.**Method 3:**map Function. More functional approach. Similar in efficiency to list comprehension but requires understanding of`map()`

and lambda functions.**Method 4:**operator Module. Avoids lambda functions. Involves importing an additional module but remains readable and functional.**Method 5:**Using numpy. Best for numerical and large datasets. Requires`numpy`

installation and might be an overkill for simple tasks.