While most of the optimization probably needs to happen within your `dist`

function, there are some tips here to speed things up:

```
# Don't manually sum
for rand in rand1:
num += sum([dist(gal, rand) for gal in gal_pos])
#If you can vectorize something, then do
import numpy as np
new_dist = np.vectorize(dist)
for rand in rand1:
num += np.sum(new_dist(gal_pos, rand))
# use already-built code whenever possible (as already suggested)
scipy.spatial.distance.cdist(gal, rand1, metric='euclidean')
```