**π‘ Problem Formulation:** In Python, it’s common to encounter the need to process rows in a dataset or a matrix where each row is comprised of list elements. The objective is to apply operations or criteria to these rows efficiently. For example, given a list of lists `[[1, 2], [3, 4], [5, 6]]`

, we might want to find rows with certain properties, transform them, or extract specific information.

## Method 1: Using List Comprehensions

List comprehensions in Python provide a concise way to create lists based on existing lists. When dealing with rows of list elements, list comprehensions can be used to filter and process rows in a single readable line of code.

Here’s an example:

matrix = [[1, 2], [3, 4], [5, 6]] filter_odd = [row for row in matrix if all(elem % 2 != 0 for elem in row)]

Output: `[]`

The provided code snippet demonstrates how to use a list comprehension to filter rows that contain only odd elements. The `all()`

function is used to check if all elements fulfill the given condition.

## Method 2: Using the `filter()`

Function

The `filter()`

function in Python allows for the application of a filtering criterion to an iterable, returning those elements that satisfy the predicate function.

Here’s an example:

matrix = [[1, 3], [2, 4], [5, 7]] is_odd = lambda row: all(x % 2 != 0 for x in row) filtered_rows = list(filter(is_odd, matrix))

Output: `[[1, 3], [5, 7]]`

The code uses the `filter()`

function, which takes a lambda function as its predicate to filter out rows in which all elements are odd. The `list()`

function is then used to convert the filter object back into a list.

## Method 3: Using a For Loop

Using a traditional for loop to iterate through each row and manually check the criteria allows more complex logic and potentially other operations during the iteration.

Here’s an example:

matrix = [[0, 2], [3, 3], [6, 8]] filtered_rows = [] for row in matrix: if all(x % 2 == 0 for x in row): filtered_rows.append(row)

Output: `[[0, 2], [6, 8]]`

The above code uses a for loop to append rows to a new list `filtered_rows`

, but only if all of their elements are even, as dictated by the condition inside the `all()`

function.

## Method 4: Using NumPy for Multi-Dimensional Arrays

NumPy is a Python library for numerical computing, which provides support for multi-dimensional arrays and matrices along with a collection of mathematical functions to operate on these array objects.

Here’s an example:

import numpy as np matrix = np.array([[1, 2], [3, 4], [5, 6]]) filtered_rows = matrix[np.all(matrix % 2 == 0, axis=1)]

Output: `[[2, 4]]`

This snippet uses NumPy’s `all()`

method with `axis=1`

to test conditions across rows. Rows with all even elements are returned, utilizing the powerful capabilities of NumPy for array operations.

## Bonus One-Liner Method 5: Using Generator Expressions

Generator expressions provide an even more memory-efficient way to deal with lists, especially useful when working with large datasets, as they yield items one by one using the iterator protocol.

Here’s an example:

matrix = [[1, 3], [2, 4], [5, 7]] filtered_rows = (row for row in matrix if all(x % 2 != 0 for x in row))

Output: `<generator object <genexpr> at 0x...>`

To consume the generator expression, you can iterate over it or convert it to a list using `list(filtered_rows)`

. This method is particularly useful for its low memory usage when the list is large.

## Summary/Discussion

**Method 1:**List Comprehensions. Strengths: concise and readable. Weaknesses: can become unreadable with complex conditions.**Method 2:**`filter()`

Function. Strengths: functional programming style, clean. Weaknesses: requires conversion to list, which can be less performant for large datasets.**Method 3:**For Loop. Strengths: versatile and intuitive. Weaknesses: more verbose, potentially slower for large data processing.**Method 4:**NumPy. Strengths: optimized for numerical computations, very fast on large datasets. Weaknesses: requires learning NumPy syntax, additional library dependency.**Bonus Method 5:**Generator Expressions. Strengths: Memory-efficient. Weaknesses: might be slightly less intuitive, requires materialization to list for output.