How to Condense a Python Matrix into a Square Matrix?

To condense a Python matrix into a square matrix, you need to remove any extra rows or columns. Here’s a step-by-step process to achieve that:

1. Determine the dimensions of the original matrix. Let’s say the original matrix has dimensions m rows and n columns.
2. Find the minimum of m and n to determine the side length of the square matrix. Let’s call this value min_dim.
3. Extract the first min_dim rows and min_dim columns from the original matrix to form the condensed square matrix.

Here’s an example implementation:

def condense_to_square(matrix):
    m = len(matrix)  # Number of rows in the original matrix
    n = len(matrix[0])  # Number of columns in the original matrix
    min_dim = min(m, n)  # Minimum of m and n

    # Extract the first min_dim rows and min_dim columns
    condensed_matrix = [row[:min_dim] for row in matrix[:min_dim]]

    return condensed_matrix

Let’s demonstrate this with an example:

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

condensed_matrix = condense_to_square(original_matrix)

print(condensed_matrix)

Output:

[[1, 2, 3],
 [4, 5, 6],
 [7, 8, 9]]

In this example, the original matrix had 4 rows and 3 columns. Since the minimum dimension is 3, we extracted the first 3 rows and 3 columns to create a square matrix.