Python | numpy matrix.reshape()

The matrix.reshape() function in the numpy library is used to reshape an array, including matrices, in a desired shape.

Syntax

numpy.matrix.reshape(shape, order='C')

Parameters

• shape: The new shape that the matrix should be reshaped into. This can be specified as a tuple of integers or an integer representing the new shape of the array. It should be compatible with the original shape.

• order (optional): This parameter determines the order the elements are read from the original matrix during reshaping. It can be one of the following values:

  • 'C' (default): C-style order, which means elements are read row-wise.
  • 'F': Fortran-style order, which means elements are read column-wise.
  • 'A': If any memory order is acceptable, this value can be used. It uses 'C' order if the array is contiguous in memory, and 'F' order otherwise.

Returns

A reshaped matrix with the specified shape.

Example

import numpy as np

matrix = np.matrix([[1, 2, 3], [4, 5, 6]])
resized_matrix = matrix.reshape((3, 2))

print(resized_matrix)

Output:

[[1 2]
 [3 4]
 [5 6]]

The above example demonstrates how to reshape a matrix using the matrix.reshape() function. In this case, the original matrix is reshaped into a 3x2 matrix. The resulting matrix is printed, showing the new shape.

Note that the reshaping is done row-wise by default. If you want to reshape the matrix column-wise, you can specify the order parameter as 'F'.

import numpy as np

matrix = np.matrix([[1, 2, 3], [4, 5, 6]])
resized_matrix = matrix.reshape((3, 2), order='F')

print(resized_matrix)

Output:

[[1 4]
 [2 5]
 [3 6]]

Here, the matrix is reshaped into a 3x2 matrix using Fortran-style order. The resulting matrix is printed, showing the new shape in a column-wise fashion.

By utilizing the matrix.reshape() function, you can easily reshape numpy arrays, including matrices, to match your desired dimensions or order.