# How to get element-wise matrix multiplication (Hadamard product) in numpy?

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?

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
```

From: stackoverflow.com/q/40034993

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