PG Certificate Course in Data Science, AI & Machine Learning by IIT Roorkee. Apply Now & Get up to Rs. 75,000 OFF! Offer Ends in:

Apply Now- Home
- Assessment

24 / 32

In this chapter, we will discuss the Multiplication and Dot Product of two NumPy arrays.

**Multiplication**

Multiplication between two NumPy arrays is an element-wise product, and is represented by '`*`

'

e.g.

```
import numpy as np
A = np.array( [ [ 1,1], [0, 1] ] )
B = np.array( [ [2, 0], [3, 4] ] )
```

Please note, this element-wise product '`*`

' is different from 'dot product '`.`

' of matrices.

'dot product' is also known as 'matrix product'.

```
M = A * B
print(A)
array([ [1, 1],
[0, 1] ])
print(B)
array( [ [2, 0],
[3, 4] ] )
print(M)
```

Output (M) will be

```
array([ [2, 0 ],
[0, 4] ] )
```

Above, element-wise product(M) is computed as below

```
M = [ [ A[1, 1] * B[1, 1], A[1, 2] * B[1, 2] ],
[ A[2, 1] * B[2, 1], A[2, 2] * B[2, 2] ] ]
= [ [ (1 * 2), (1 * 0)],
[ (0 * 3), (1, 4) ] ]
M = [ [2, 0],
[0, 4] ]
```

**Matrix Product (or Dot Product)**

Matrix Product (or Dot Product) between two NumPy is different from the element-wise product (or Multiplication). The dot product is represented by '`.`

'

e.g.

```
import numpy as np
C = np.array( [ [ 1,1], [0, 1] ] )
D = np.array( [ [2, 0], [3, 4] ] )
```

Please note, this dot product '`.`

' or 'matrix product' is different from 'element-wise' product '`*`

'

```
P = np.dot(C, D)
print(C)
array([ [1, 1],
[0, 1] ])
print(D)
array( [ [2, 0],
[3, 4] ] )
print(P)
```

Output (P) will be

```
array([ [5, 4 ],
[ 3, 4] ] )
```

Above, element-wise product(P) is computed as below

```
P = [ [ [( (C[1,1] * D[1, 1]) + (C[1, 2] * D[2, 1] ) )], [( (C[1,1] * D[1, 2]) + (C[1, 2] * D[2, 2] ) )] ],
[ [( (C[2,1] * D[1, 1]) + (C[2, 2] * D[2, 1] ) )], [( (C[2,1] * D[1, 2]) + (C[2, 2] * D[2, 2] ) )] ] ]
= [ [ ( (1 * 2) + (1 * 3) ), ( (1 * 0) + (1 * 4) ) ],
[ ( (0 * 2) + (1 * 3) ), ( (0 * 2) + (1 * 4) ) ] ]
= [ [ (2 + 3), (0 + 4) ],
[ (0 + 3), (0 + 4) ] ]
= [ [5, 4],
[3, 4] ]
```

Please follow the below steps:

(1) Please import required libraries

```
import numpy as np
```

**Multiplication (element-wise product)**

(2) Create two NumPy arrays - `A_arr`

and `B_arr`

- as shown below

```
A_arr = np.array([ [ 5,9], [4, 7] ])
B_arr = np.array( [ [2, 8], [1, 6] ] )
```

(3) Multiply the above two NumPy arrays (`A_arr`

and `B_arr`

) and store the result in a variable `M_arr`

```
<<your code comes here>> = A_arr << your code comes here>> B_arr
```

(4) Print the array `M_arr`

to see its values

```
print(<<your code comes here>>)
```

**Dot Product (Matrix Product)**

(1) Create two NumPy arrays - `C_arr`

and `D_arr`

- as shown below

```
C_arr = np.array([ [ 5,9], [4, 7] ])
D_arr = np.array( [ [2, 8], [1, 6] ] )
```

(2) Perform dot product of the above two NumPy arrays (`C_arr`

and `D_arr`

) and store the result in a variable `P_arr`

```
<<your code comes here>> = np.dot(<<your code comes here>>)
```

(3) Print the array `P_arr`

to see its values

```
print(<<your code comes here>>)
```

XP

Taking you to the next exercise in seconds...

Want to create exercises like this yourself? Click here.

Checking Please wait.

Success

Error

No hints are availble for this assesment

Fetching answer, please wait...

Error

**Note - **Having trouble with the assessment engine? Follow the steps listed
here

## Loading comments...