**π‘ Problem Formulation:** Given a matrix (a list of lists), the goal is to sort the matrix’s rows based on the ascending sum of the elements in each row. For example, if your input matrix is `[[3, 1], [1, 2], [2, 2]]`

, the output should be `[[1, 2], [2, 2], [3, 1]]`

since the sums of the rows are 3, 3, and 4 respectively.

## Method 1: Using a Custom Sort Function

Python’s `sorted`

function is highly versatile, especially when combined with a custom sorting key. By using a lambda function to sum the rows, we can easily sort the matrix as required.

Here’s an example:

matrix = [[3, 1], [1, 2], [2, 2]] sorted_matrix = sorted(matrix, key=lambda x: sum(x)) print(sorted_matrix)

The output will be:

[[1, 2], [2, 2], [3, 1]]

This method uses the built-in sorting capability of Python without needing to write complicated sorting algorithms. The lambda function serves as a key that defines the sort order, which, in this case, is the sum of elements in each row.

## Method 2: Using the Sort Method with a Custom Function

The sort method directly alters the original matrix instead of returning a new sorted matrix. We can pass a custom lambda function as the key to control how the rows get sorted.

Here’s an example:

matrix = [[3, 1], [1, 2], [2, 2]] matrix.sort(key=lambda x: sum(x)) print(matrix)

The output will be:

[[1, 2], [2, 2], [3, 1]]

While this method mutates the original list, it is efficient because it does not require additional memory. The sort method is fast and robust, making in-place sorting convenient and easy to implement.

## Method 3: Using the Operator Module

For those who prefer not to use lambda functions, the `operator`

module offers a neat alternative. The `itemgetter`

function can be used in conjunction with a transposed matrix to sort the rows.

Here’s an example:

import operator matrix = [[3, 1], [1, 2], [2, 2]] # Calculating the sum of each row using a list comprehension and storing it as a separate list sums = [sum(row) for row in matrix] # Using the list of sums with `itemgetter` to sort the original matrix sorted_matrix = [row for _,row in sorted(zip(sums, matrix), key=operator.itemgetter(0))] print(sorted_matrix)

The output will be:

[[1, 2], [2, 2], [3, 1]]

This method may seem more complex, but it separates the computation of sums from the sorting process. It takes advantage of the sorting functionality of Python while using the `operator`

module to provide a clean sorting key.

## Method 4: Using NumPy Library

If you work with matrices frequently, using the NumPy library can streamline operations. NumPy provides a rich set of functions optimized for performance on numerical data sets.

Here’s an example:

import numpy as np matrix = np.array([[3, 1], [1, 2], [2, 2]]) # Argsort the sums to get the sorted indices indices = np.argsort(np.sum(matrix, axis=1)) # Rearrange the matrix using the sorted indices sorted_matrix = matrix[indices] print(sorted_matrix)

The output will be:

[[1 2] [2 2] [3 1]]

By relying on NumPy’s `argsort`

function, we obtain indices that sort the matrix by sum, which we then use to reorder our original matrix. This method is not only concise but also benefits from NumPy’s performance optimizations.

## Bonus One-Liner Method 5: Using List Comprehension and Sorted

Python’s list comprehension can provide a compact and elegant way to achieve the sorting task in a single line, making the code Pythonic and readable.

Here’s an example:

matrix = [[3, 1], [1, 2], [2, 2]] sorted_matrix = sorted(matrix, key=sum) print(sorted_matrix)

The output will be:

[[1, 2], [2, 2], [3, 1]]

This one-liner leverages the built-in sum function, providing a clear concise solution. The readability of this code is suited for Python enthusiasts who value expressive and idiomatic coding practices.

## Summary/Discussion

**Method 1:**Custom Sort Function. Offers clarity and flexibility. Requires lambdas.**Method 2:**In-Place Sorting. Efficient memory usage. Mutates the original matrix.**Method 3:**Using the Operator Module. Separates concerns of summing and sorting. Slightly more complex.**Method 4:**Using NumPy Library. Optimized for numerical data, great for matrix operations. Requires external library.**Method 5:**List Comprehension. Pythonic and readable, compact one-liner. Might lack transparency for new coders.