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This tutorial will discuss methods for dividing a matrix by a vector in NumPy.

Splitting a matrix by vector in NumPy using array slicing in Python

A matrix is ​​a two-dimensional array, while a vector is just a one-dimensional array. If we want to divide the elements of the matrix by the elements of the vector in each row, we have to add a new dimension to the vector. We can use the array slicing method in Python to add a new dimension to the vector. The following code example shows us how to divide each row of a matrix by a vector using the array slicing method in Python.

import numpy as np

matrix = np.array([[2, 2, 2], [4, 4, 4], [6, 6, 6]])

vector = np.array([2, 4, 6])

matrix = matrix / vector[:, None]
print(matrix)

Output:

[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

We first np.array()created the matrix and the vector using the function. Then we added a new axis to the vector using the slice method. We then divided the matrix by the array and saved the result in the matrix.

Partitioning matrices by vectors in NumPy using the transpose method in NumPy

We can also transpose the matrix to divide each row of the matrix by each vector element. After that, we can transpose the result to return to the previous orientation of the matrix. Refer to the following code example.

import numpy as np

matrix = np.array([[2, 2, 2], [4, 4, 4], [6, 6, 6]])

vector = np.array([2, 4, 6])

matrix = (matrix.T / vector).T
print(matrix)

Output:

[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

In the above code, we transpose the matrix and divide it by the vector. After that, we transpose the result and store it in matrix.

Use the function in NumPy numpy.reshape()to divide a matrix by a vector

The whole idea behind this method is that we have to convert the vector into a two-dimensional array first. numpy.reshape()The function can be used to convert the vector into a two-dimensional array where each row contains only one element. Then we can easily divide each row of the matrix by each row of the vector.

import numpy as np

matrix = np.array([[2, 2, 2], [4, 4, 4], [6, 6, 6]])

vector = np.array([2, 4, 6])

matrix = matrix / vector.reshape((3, 1))
print(matrix)

Output:

[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

In the above code, we use np.reshape()the function to vectorconvert into a 2D array. After that, we divide matrixby vectorand store the result in matrix.