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
- Find the minimum of
nto determine the side length of the square matrix. Let’s call this value
- Extract the first
min_dimcolumns 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)
[[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.