Scalar Multiplication in Numpy

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               Here, we use Scalar multiplications in different ways. Let us try three methods listed as follows..

  • ·       1D array scalar multiplication
  • ·       2D array scalar multiplication
  • ·       Different scalars for each column

The sample code is given below.

1D array scalar multiplication:

 This code multiplies the array by 3. First, it creates an array of elements. Each element is multiplied by 3. The result is displayed.

Code:

import numpy as np1

# Create an array with elements

s_arr = np1.array([1, 3, 5, 7, 9, 11, 13, 15])

# Scalar multiplication by 3

result = s_arr * 3

print("Original array:", s_arr)  

print("Multiplied by 3:", result)

Output:

Original array: [ 1  3  5  7  9 11 13 15]

Multiplied by 3: [ 3  9 15 21 27 33 39 45]

2D array scalar multiplication:

              It has two-dimensional array. Each element is multiplied by 3. Finally, the output is displayed.

Code:

import numpy as np1

# Create a 2D array as matrix

s_matrix = np1.array([[1, 2, 1],

                   [3, 5, 4]])

# Multiply the matrix by 3

result = s_matrix * 3

print("Original matrix:\n", s_matrix)

print("Multiplied by 3:\n", result)

Output:

Original matrix:

 [[1 2 1]

 [3 5 4]]

Multiplied by 3:

 [[ 3  6  3]

 [ 9 15 12]]

Different scalars for each column:

              This method uses three different scalar values for each column. These values are multiplied and displayed as output.

Code:

import numpy as np1

# Creation of 2D array

matrix = np1.array([[1, 2, 1],

                   [3, 5, 4]])

# Scalars for each column

scalars = np1.array([1, 2, 3])

# multiply each column by its scalar

result = matrix * scalars

print("Original matrix:\n", matrix)

print("Scalars per column:", scalars)

print("Result:\n", result)

Output:

Original matrix:

 [[1 2 1]

 [3 5 4]]

Scalars per column: [1 2 3]

Result:

 [[ 1  4  3]

 [ 3 10 12]]

These are the ways to implement scalar multiplication in numpy using python. Hope, you understood the concept. Keep Coding!!!

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