How to create a vector in Python using NumPy

NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Numpy is basically used for creating array of n dimensions.

Vector are built from components, which are ordinary numbers. We can think of a vector as a list of numbers, and vector algebra as operations performed on the numbers in the list. In other words vector is the numpy 1-D array.

In order to create a vector we use np.array method.

Syntax : np.array(list)

Argument : It take 1-D list it can be 1 row and n columns or n rows and 1 column



Return : It returns vector which is numpy.ndarray

Note : We can create vector with other method as well which return 1-D numpy array for example np.arange(10), np.zeros((4, 1)) gives 1-D array, but most appropriate way is using np.array with the 1-D list.

Creating a Vector
In this example we will create a horizontal vector and a vertical vector

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# importing numpy
import numpy as np
  
# creating a 1-D list (Horizontal)
list1 = [1, 2, 3]
  
# creating a 1-D list (Vertical)
list2 = [[10],
        [20],
        [30]]
  
# creating a vector1
# vector as row
vector1 = np.array(list1)
  
# creating a vector 2
# vector as column
vector2 = np.array(list2)
  
  
# showing horizontal vector
print("Horizontal Vector")
print(vector1)
  
print("----------------")
  
# showing vertical vector
print("Vertical Vector")
print(vector2)

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Output :

Horizontal Vector
[1 2 3]
----------------
Vertical Vector
[[10]
 [20]
 [30]]

Basic Arithmetic operation:
In this example we will see do arithmetic operations which are element-wise between two vectors of equal length to result in a new vector with the same length

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# importing numpy
import numpy as np
  
# creating a 1-D list (Horizontal)
list1 = [5, 6, 9]
  
# creating a 1-D list (Horizontal)
list2 = [1, 2, 3]
  
# creating first vector 
vector1 = np.array(list1)
  
# printing vector1
print("First Vector          : " + str(vector1))
  
# creating secodn vector
vector2 = np.array(list2)
  
# printing vector2
print("Second Vector         : " + str(vector2))
  
# adding both the vector
# a + b = (a1 + b1, a2 + b2, a3 + b3)
addition = vector1 + vector2
  
# printing addition vector
print("Vector Addition       : " + str(addition))
  
# subtracting both the vector
# a - b = (a1 - b1, a2 - b2, a3 - b3)
subtraction = vector1 - vector2
  
# printing addition vector
print("Vector Substraction   : " + str(subtraction))
  
# multiplying  both the vector
# a * b = (a1 * b1, a2 * b2, a3 * b3)
multiplication = vector1 * vector2
  
# printing multiplication vector
print("Vector Multiplication : " + str(multiplication))
  
# dividing  both the vector
# a / b = (a1 / b1, a2 / b2, a3 / b3)
division = vector1 / vector2
  
# printing multiplication vector
print("Vector Division       : " + str(multiplication))
   

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Output :

First Vector          : [5 6 9]
Second Vector         : [1 2 3]
Vector Addition       : [ 6  8 12]
Vector Substraction   : [4 4 6]
Vector Multiplication : [ 5 12 27]
Vector Division       : [ 5 12 27]

Vector Dot Product
In mathematics, the dot product or scalar product is an algebraic operation that takes two equal-length sequences of numbers and returns a single number.
For this we will use dot method.

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# importing numpy
import numpy as np
  
# creating a 1-D list (Horizontal)
list1 = [5, 6, 9]
  
# creating a 1-D list (Horizontal)
list2 = [1, 2, 3]
  
# creating first vector 
vector1 = np.array(list1)
  
# printing vector1
print("First Vector  : " + str(vector1))
  
# creating secodn vector
vector2 = np.array(list2)
  
# printing vector2
print("Second Vector : " + str(vector2))
  
# getting dot product of both the vectors
# a . b = (a1 * b1 + a2 * b2 + a3 * b3)
# a . b = (a1b1 + a2b2 + a3b3)
dot_product = vector1.dot(vector2)
  
# printing dot product
print("Dot Product   : " + str(dot_product))

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First Vector  : [5 6 9]
Second Vector : [1 2 3]
Dot Product   : 44

Vector-Scalar Multiplication
Multiplying a vector by a scalar is called scalar multiplication. To perform scalar multiplication, we need to multiply the scalar by each component of the vector.

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# importing numpy
import numpy as np
  
# creating a 1-D list (Horizontal)
list1 = [1, 2, 3]
  
# creating first vector 
vector = np.array(list1)
  
# printing vector1
print("Vector  : " + str(vector))
  
# scalar value 
scalar = 2
  
# printing scalar value
print("Scalar  : " + str(scalar))
   
# getting scalar multiplication value
# s * v = (s * v1, s * v2, s * v3)
scalar_mul = vector * scalar
  
# printing dot product
print("Scalar Multiplication : " + str(scalar_mul))
  
  
   

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Output

Vector  : [1 2 3]
Scalar  : 2
Scalar Multiplication : [2 4 6]

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