# How can the Euclidean distance be calculated with NumPy?

###### Posted By: Anonymous

I have two points in 3D:

```
(xa, ya, za)
(xb, yb, zb)
```

And I want to calculate the distance:

```
dist = sqrt((xa-xb)^2 + (ya-yb)^2 + (za-zb)^2)
```

What’s the best way to do this with NumPy, or with Python in general? I have:

```
import numpy
a = numpy.array((xa ,ya, za))
b = numpy.array((xb, yb, zb))
```

## Solution

Use `numpy.linalg.norm`

:

```
dist = numpy.linalg.norm(a-b)
```

You can find the theory behind this in Introduction to Data Mining

This works because the **Euclidean distance** is the **l2 norm**, and the default value of the **ord** parameter in `numpy.linalg.norm`

is 2.

###### Answered By: Anonymous

Disclaimer: This content is shared under creative common license cc-by-sa 3.0. It is generated from StackExchange Website Network.