# python numpy euclidean distance calculation between matrices of row vectors

## python numpy euclidean distance calculation between matrices of row vectors

To get the distance you can use the norm method of the linalg module in numpy:

```
np.linalg.norm(x - y)
```

While you can use vectorize, @Karls approach will be rather slow with numpy arrays.

The easier approach is to just do `np.hypot(*(points - single_point).T)`

. (The transpose assumes that points is a Nx2 array, rather than a 2xN. If its 2xN, you dont need the `.T`

.

However this is a bit unreadable, so you write it out more explictly like this (using some canned example data…):

```
import numpy as np
single_point = [3, 4]
points = np.arange(20).reshape((10,2))
dist = (points - single_point)**2
dist = np.sum(dist, axis=1)
dist = np.sqrt(dist)
```

#### python numpy euclidean distance calculation between matrices of row vectors

```
import numpy as np
def distance(v1, v2):
return np.sqrt(np.sum((v1 - v2) ** 2))
```