# 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)  # 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.