## Question

Asked By – Malintha

I have two matrices

```
a = np.matrix([[1,2], [3,4]])
b = np.matrix([[5,6], [7,8]])
```

and I want to get the element-wise product, `[[1*5,2*6], [3*7,4*8]]`

, equaling

`[[5,12], [21,32]]`

I have tried

```
print(np.dot(a,b))
```

and

```
print(a*b)
```

but both give the result

`[[19 22], [43 50]]`

which is the matrix product, not the element-wise product. How can I get the the element-wise product (aka Hadamard product) using built-in functions?

**Now we will see solution for issue: How to get element-wise matrix multiplication (Hadamard product) in numpy? **

## Answer

For elementwise multiplication of `matrix`

objects, you can use `numpy.multiply`

:

```
import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
np.multiply(a,b)
```

**Result**

```
array([[ 5, 12],
[21, 32]])
```

However, you should really use `array`

instead of `matrix`

. `matrix`

objects have all sorts of horrible incompatibilities with regular ndarrays. With ndarrays, you can just use `*`

for elementwise multiplication:

```
a * b
```

If you’re on Python 3.5+, you don’t even lose the ability to perform matrix multiplication with an operator, because `@`

does matrix multiplication now:

```
a @ b # matrix multiplication
```

This question is answered By – Rahul K P

**This answer is collected from stackoverflow and reviewed by FixPython community admins, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 **