**π‘ Problem Formulation:** Imagine you have a 2D matrix (list of lists) in Python and you want to filter and print the rows where one specific index contains a certain value. For example, given a matrix `[[1,2],[3,2],[4,5]]`

and the index 1, we want to print only those rows where the element at index 1 is `2`

, which would be `[[1,2],[3,2]]`

.

## Method 1: Using List Comprehension

List comprehension in Python provides a concise way to generate lists. It can be used to create a new list of rows where a specific column (index) matches a given value. This method is efficient and easy to read, making it well-suited for such a task.

Here’s an example:

matrix = [[1,2],[3,2],[4,5]] index_to_match = 1 value_to_match = 2 matching_rows = [row for row in matrix if row[index_to_match] == value_to_match] for row in matching_rows: print(row)

Output:

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

The code snippet creates a new list `matching_rows`

that includes only the rows from the original `matrix`

where the element at the given `index_to_match`

equals `value_to_match`

. It then prints each row in this new list.

## Method 2: Using a For Loop

For loops allow for more traditional and verbose iteration and condition checking on lists, which can be beneficial for more complex conditions or when debugging is necessary. They provide a straightforward and explicit approach to iterating over elements.

Here’s an example:

matrix = [[1,2],[3,2],[4,5]] index_to_match = 1 value_to_match = 2 for row in matrix: if row[index_to_match] == value_to_match: print(row)

Output:

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

This snippet directly iterates over each row in the `matrix`

, checking the condition for the element at `index_to_match`

. If the condition is true, it prints the row immediately.

## Method 3: Using the filter() Function

The `filter()`

function in Python can be used to filter items in a list. It creates a list of elements for which a function returns true. In this case, an inline `lambda`

function can serve as the predicate for the filtering process.

Here’s an example:

matrix = [[1,2],[3,2],[4,5]] index_to_match = 1 value_to_match = 2 matching_rows = filter(lambda row: row[index_to_match] == value_to_match, matrix) for row in matching_rows: print(row)

Output:

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

The code snippet utilizes the built-in `filter()`

function to construct an iterator of rows that have the specified value at the chosen index, which are then printed in a for loop.

## Method 4: Using numpy.where()

When working with numerical data, the NumPy library offers the `numpy.where()`

function, which is a powerful tool that can be used to select elements from an array based on a condition. This can be especially useful when dealing with large datasets or matrices.

Here’s an example:

import numpy as np matrix = np.array([[1,2],[3,2],[4,5]]) index_to_match = 1 value_to_match = 2 matching_indices = np.where(matrix[:, index_to_match] == value_to_match)[0] matching_rows = matrix[matching_indices] for row in matching_rows: print(row)

Output:

[1 2] [3 2]

This snippet converts the list of lists to a NumPy array, which allows for more sophisticated indexing and selection capabilities. The `numpy.where()`

function is used to find the indices of rows that match the condition, and then those rows are printed.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions are similar to list comprehensions but are more memory-efficient, generating values on the fly. This method is suitable for one-time iterations over the results and can handle large matrices with ease.

Here’s an example:

matrix = [[1,2],[3,2],[4,5]] index_to_match = 1 value_to_match = 2 matching_rows = (row for row in matrix if row[index_to_match] == value_to_match) for row in matching_rows: print(row)

Output:

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

The code uses a generator expression to define `matching_rows`

, which is an iterator that lazily evaluates each row when looped over in the for loop, thereby printing the matching rows without storing all of them in memory at once.

## Summary/Discussion

**Method 1:**List Comprehension. Fast and readable. Not as memory-efficient for very large datasets.**Method 2:**Using a For Loop. Explicit and easy to understand. More verbose and potentially less Pythonic than list comprehensions or generator expressions.**Method 3:**Using the filter() Function. Functionally clean, but can be less intuitive and more obscure than other methods.**Method 4:**Using numpy.where(). Highly efficient for numerical calculations and large datasets. Requires NumPy and doesn’t work directly with lists of lists without conversion.**Method 5:**Using a Generator Expression. Memory-efficient and powerful for larger datasets. The results are not directly reusable since generators are exhausted after one iteration.