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
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.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
Horizontal Vector [1 2 3] ---------------- Vertical Vector [  ]
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
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
First Vector : [5 6 9] Second Vector : [1 2 3] Dot Product : 44
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.
Vector : [1 2 3] Scalar : 2 Scalar Multiplication : [2 4 6]
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