How to find farthest data points from a predefined cluster in a data set with Python?
You want to know the nearest neighbor of you unlabeled data in you labeled cluster.
Using sklearn, you can fit a NearestNeighbors() class with a giving metric, algorithm (Ball-tree, KD-tree,...) and all other parameters (see here).
Then get the labeled nearest neighbor from your unlabeled datapoint and its distance by using kneighbors() method.
Here is a sample code:
import numpy as np
from sklearn.neighbors import NearestNeighbors

# Fake data
labeled_samples = [[0, 1.2], [0, 1.3], [0, 1.4]]
unlabeled_samples = [[0, 1.7], [0.5, 0.5], [1, 1]]

# Create your class with your labeled cluster
neigh = NearestNeighbors(n_neighbors=1)
neigh.fit(labeled_samples)

# get the distance/index to the nearest neighbor of you unlabeled data
distances, indexes = neigh.kneighbors(unlabeled_samples, 1, return_distance=True)

Then you just have to sort the result.
Note: using this approach is more optimized than computing all distances from all labeled datapoints and then sort them. See this note for more info.