clean code without much if else

```
import math
import numpy as np
# Add the function, `assign_to_cluster`
# This creates the column, `cluster`, by applying assign_to_cluster row-by-row
# Uncomment when ready
def assign_to_cluster(row):
id_distance = {}
for centroid_i,features in centroids_dict.items():
sub_distance=(np.array(features)) - (np.array([row["ppg"],row["atr"]]))
sub_distance=sub_distance**2
root_distance = sub_distance.sum()
euclid_distance = math.sqrt(root_distance)
id_distance[centroid_i] = euclid_distance
#below will return key / cluster id corresponding to lowest euclid_distance
return min(id_distance, key=id_distance.get)
point_guards['cluster'] = point_guards.apply(lambda row: assign_to_cluster(row), axis=1)
```