## Question

Asked By – Alex S

I’m trying to multiply each of the terms in a 2D array by the corresponding terms in a 1D array. This is very easy if I want to multiply every column by the 1D array, as shown in the numpy.multiply function. But I want to do the opposite, multiply each term in the row.

In other words I want to multiply:

```
[1,2,3] [0]
[4,5,6] * [1]
[7,8,9] [2]
```

and get

```
[0,0,0]
[4,5,6]
[14,16,18]
```

but instead I get

```
[0,2,6]
[0,5,12]
[0,8,18]
```

Does anyone know if there’s an elegant way to do that with numpy?

Thanks a lot,

Alex

**Now we will see solution for issue: Multiplying across in a numpy array **

## Answer

Normal multiplication like you showed:

```
>>> import numpy as np
>>> m = np.array([[1,2,3],[4,5,6],[7,8,9]])
>>> c = np.array([0,1,2])
>>> m * c
array([[ 0, 2, 6],
[ 0, 5, 12],
[ 0, 8, 18]])
```

If you add an axis, it will multiply the way you want:

```
>>> m * c[:, np.newaxis]
array([[ 0, 0, 0],
[ 4, 5, 6],
[14, 16, 18]])
```

You could also transpose twice:

```
>>> (m.T * c).T
array([[ 0, 0, 0],
[ 4, 5, 6],
[14, 16, 18]])
```

This question is answered By – jterrace

**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 **