**π‘ Problem Formulation:** You are given a nested list (a list of lists) in Python, where each sublist may contain numeric values. The challenge is to find the sublist with the maximum aggregate value. For instance, given `[[1, 2, 3], [4, 5], [6]]`

, the desired output would be `[4, 5]`

, as it has the maximum sum of 9.

## Method 1: Using the max function with a custom key

This method involves using the built-in `max`

function and providing a custom key function that calculates the sum of elements in each sublist. This returns the sublist that has the maximum sum in the entire nested list.

Here’s an example:

nested_list = [[1, 2, 3], [4, 5], [6]] max_sublist = max(nested_list, key=sum) print(max_sublist)

Output: `[4, 5]`

This code initiates a search for the maximum sublist based on the sum of its elements using the `max`

function. The `key`

argument is set to `sum`

, which tells Python to use the sum of the sublist’s elements as the comparing factor.

## Method 2: Using a loop to calculate the sum manually

For those who prefer not to use built-in functions, this method involves iterating over each sublist in the nested list, calculating the sum, and manually keeping track of the maximum sum and corresponding sublist.

Here’s an example:

max_value = float('-inf') max_sublist = [] for sublist in [[1, 2, 3], [4, 5], [6]]: current_sum = sum(sublist) if current_sum > max_value: max_value = current_sum max_sublist = sublist print(max_sublist)

Output: `[4, 5]`

This snippet iterates through each sublist, calculating the sum, and updates the maximum sum and corresponding sublist whenever a new maximum is found. It initializes `max_value`

to negative infinity to ensure that any positive sum will overwrite it during the first comparison.

## Method 3: Using list comprehension and max()

This approach combines list comprehension to create a list of sums and then applies the `max`

function to find the highest sum. Then, it retrieves the sublist associated with the highest sum.

Here’s an example:

nested_list = [[1, 2, 3], [4, 5], [6]] sums = [sum(sublist) for sublist in nested_list] max_sublist = nested_list[sums.index(max(sums))] print(max_sublist)

Output: `[4, 5]`

The first step creates a list of sums for each sublist. It then finds the maximum sum and uses its index to retrieve the corresponding sublist from the original nested list.

## Method 4: Using reduce and a lambda function

The `functools.reduce`

function can be used in conjunction with a lambda to compare sublists based on their sums. It reduces the nested list to the maximum sublist.

Here’s an example:

from functools import reduce nested_list = [[1, 2, 3], [4, 5], [6]] max_sublist = reduce(lambda a, b: a if sum(a) > sum(b) else b, nested_list) print(max_sublist)

Output: `[4, 5]`

This code employs `reduce`

to apply the lambda function across the nested list, carrying forward the greater of two sublists as determined by their sums until the maximum is found.

## Bonus One-Liner Method 5: Using a generator expression with max()

With Python’s expressive syntax, finding the maximum sublist can be condensed even further. This one-liner uses a generator expression within the `max`

function.

Here’s an example:

max_sublist = max((sublist for sublist in [[1, 2, 3], [4, 5], [6]]), key=sum) print(max_sublist)

Output: `[4, 5]`

This concise code exemplifies the power of Python’s generator expressions and the `max`

function. In a single line, it achieves what the other methods do with several lines of code.

## Summary/Discussion

**Method 1:**Max with Custom Key. Simple and elegant. Uses built-in functions. May not be the fastest for very large datasets due to the use of the`sum`

function for every comparison made by`max`

.**Method 2:**Loop with Manual Sum. More transparent in operation. Generally slower than Method 1, but useful for educational purposes or when custom logic is necessary.**Method 3:**List Comprehension and Max. Efficient and Pythonic. Combines two powerful concepts for clean code. However, requires additional memory for sum storage.**Method 4:**Reduce with Lambda. Functional programming approach. Can be less intuitive for those unfamiliar with`reduce`

. Often less readable due to the complexity of lambda functions.**Method 5:**One-Liner Generator Expression. Very concise. Best for small to medium datasets. Readability might suffer for those not accustomed to Python’s succinct syntax.