Lesson 3

Hello Coders! This unit's exciting programming lesson will involve traversing the labyrinth of two-dimensional matrices. We're going to apply our **Python** skills to combine submatrices from two different matrices, creating a new one. It appears to be quite a complex task, doesn't it? But don't fret. We'll go through this together, one step at a time.

Are you ready for the task? Here it is: Imagine having two different 2D matrices, `A`

, and `B`

. Our job is to devise a Python function—let's name it `submatrix_concatenation()`

—which takes these two matrices as inputs, along with the coordinates specifying submatrices within `A`

and `B`

. This function is expected to stitch the two chosen submatrices together, forming a new one, `C`

. Notably, the submatrices from `A`

and `B`

should have the same number of rows, and in the final matrix `C`

, elements from `A`

's submatrix should be on the left and those from `B`

's submatrix on the right.

Let's visualize this with a couple of matrices.
Given the matrix `A`

as

`1[[1, 2, 3, 4], 2[5, 6, 7, 8], 3[9, 10, 11, 12]]`

and the matrix `B`

as

`1[[11, 12, 13], 2[14, 15, 16], 3[17, 18, 19]]`

If we select 2x2 submatrices from each (comprising the 2nd to 3rd rows and 2nd to 3rd columns from `A`

, and 1st to 2nd rows and 1st to 2nd columns from `B`

), their concatenation would look like:

`1[[6, 7, 11, 12], 2[10, 11, 14, 15]]`

Our first step towards the solution is to extract submatrices from `A`

and `B`

from the given coordinates. For this, we'll use Python's list slicing technique to get the rows, and then the required columns from those rows:

Python`1def submatrix_concatenation(matrix_A, matrix_B, submatrix_coords): 2 start_row_A, end_row_A, start_col_A, end_col_A = submatrix_coords[0] 3 start_row_B, end_row_B, start_col_B, end_col_B = submatrix_coords[1] 4 5 # Using list slicing to extract the required rows and columns 6 submatrix_A = [row[start_col_A-1:end_col_A] for row in matrix_A[start_row_A-1:end_row_A]] 7 submatrix_B = [row[start_col_B-1:end_col_B] for row in matrix_B[start_row_B-1:end_row_B]]`

Next, we need to stitch together these submatrices. We can achieve this by concatenating corresponding rows from both matrices into a new matrix. It's easy to accomplish this by using Python's list concatenation (`+`

operator) and list comprehension:

Python`1def submatrix_concatenation(matrix_A, matrix_B, submatrix_coords): 2 start_row_A, end_row_A, start_col_A, end_col_A = submatrix_coords[0] 3 start_row_B, end_row_B, start_col_B, end_col_B = submatrix_coords[1] 4 5 # Using list slicing to extract the required rows and columns 6 submatrix_A = [row[start_col_A-1:end_col_A] for row in matrix_A[start_row_A-1:end_row_A]] 7 submatrix_B = [row[start_col_B-1:end_col_B] for row in matrix_B[start_row_B-1:end_row_B]] 8 9 # Using list concatenation and list comprehension to combine the submatrices 10 result_matrix = [row_A + row_B for row_A, row_B in zip(submatrix_A, submatrix_B)] 11 return result_matrix`

There we go! We've combined submatrices from two matrices into one, provided they have the same number of rows.

Congratulations! You've tackled an elaborate matrix manipulation task in this unit. This required you to have a clear understanding of Python's **list slicing** and **list comprehension**. Through this exercise, not only have you honed your Python proficiency, but you've also wrestled with the conceptual intricacies of submatrices.

Now, it's time for some hands-on practice! In the subsequent practice session, dive into more complex challenges that involve manipulating and working with multiple matrices. This lesson will be your handy guide as you explore similar problems. Keep practicing, and soon, you'll be adept at solving matrix manipulation tasks. Happy Coding!