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:

- Determine the dimensions of the original matrix. Let’s say the original matrix has dimensions
`m`

rows and`n`

columns. - Find the minimum of
`m`

and`n`

to determine the side length of the square matrix. Let’s call this value`min_dim`

. - 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.

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