How to Create a Vector or Matrix in Python?

In this article, we will show you how to create vectors and matrices in Python using NumPy, a powerful library for numerical computing.

NumPy is a Python library designed to work efficiently with arrays. It is fast, simple to learn, and memory-efficient. NumPy allows us to create n-dimensional arrays for mathematical operations.

What are Vectors?

A vector is a 1-dimensional NumPy array containing components (ordinary numbers). You can think of a vector as a list of numbers where vector algebra operations are performed on these numbers.

We use the np.array() method to create vectors.

Syntax

np.array(data)

Parameters

• data ? A 1-D list or array-like object

Return Value ? Returns a NumPy array (vector)

Creating a Horizontal Vector

A horizontal vector is created from a simple list using numpy.array() ?

import numpy as np

# Creating a horizontal vector from a list
numbers = [15, 20, 25, 30, 35]
horizontal_vector = np.array(numbers)

print("Original list:", numbers)
print("Horizontal vector:", horizontal_vector)
print("Shape:", horizontal_vector.shape)

Original list: [15, 20, 25, 30, 35]
Horizontal vector: [15 20 25 30 35]
Shape: (5,)

Creating a Vertical Vector

A vertical vector is created using nested lists where each element is in its own sub-list ?

import numpy as np

# Creating a vertical vector using nested lists
vertical_vector = np.array([[10], [20], [30], [40], [50]])

print("Vertical vector:")
print(vertical_vector)
print("Shape:", vertical_vector.shape)

Vertical vector:
[[10]
 [20]
 [30]
 [40]
 [50]]
Shape: (5, 1)

Creating Matrices

Using numpy.array()

The most common way to create a matrix is using np.array() with nested lists ?

import numpy as np

# Creating a 3x3 matrix using nested lists
matrix = np.array([[1, 2, 3],
                   [4, 5, 6], 
                   [7, 8, 9]])

print("Matrix using np.array():")
print(matrix)
print("Shape:", matrix.shape)
print("Dimensions:", matrix.ndim)

Matrix using np.array():
[[1 2 3]
 [4 5 6]
 [7 8 9]]
Shape: (3, 3)
Dimensions: 2

Using numpy.matrix()

The np.matrix() function creates a specialized 2D array optimized for linear algebra operations ?

import numpy as np

# Creating a matrix using np.matrix()
matrix_obj = np.matrix([[5, 3, 9],
                        [4, 5, 6],
                        [7, 8, 2]])

print("Matrix using np.matrix():")
print(matrix_obj)
print("Type:", type(matrix_obj))

Matrix using np.matrix():
[[5 3 9]
 [4 5 6]
 [7 8 2]]
Type: <class 'numpy.matrix'>

Common Matrix Creation Methods

NumPy provides several built-in functions to create special matrices ?

import numpy as np

# Identity matrix
identity = np.eye(3)
print("Identity matrix:")
print(identity)

# Matrix of zeros
zeros_matrix = np.zeros((2, 4))
print("\nZeros matrix:")
print(zeros_matrix)

# Matrix of ones
ones_matrix = np.ones((3, 2))
print("\nOnes matrix:")
print(ones_matrix)

Identity matrix:
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]

Zeros matrix:
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]]

Ones matrix:
[[1. 1.]
 [1. 1.]
 [1. 1.]]

Comparison of Methods

Conclusion

Use np.array() for creating vectors and matrices in most cases. For specialized linear algebra operations, np.matrix() can be helpful, though np.array() is generally preferred in modern NumPy usage.