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DS-1000 / 865 / I have fitted a k-means algorithm on 5000+ samples using the python scikit-learn…

Problem

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Problem:

I have fitted a k-means algorithm on 5000+ samples using the python scikit-learn library. I want to have the 50 samples closest (data, not just index) to a cluster center "p" (e.g. p=2) as an output, here "p" means the p^th center. How do I perform this task?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
p, X = load_data()
assert type(X) == np.ndarray
km = KMeans()
def get_samples(p, X, km):
    # return the solution in this function
    # samples = get_samples(p, X, km)
    ### BEGIN SOLUTION

library

Sklearn

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Source and history

upstream_id / Original problem ID
865
task_id
268cb2f7-10a7-5034-80c1-d2a6a6722bc1
task_key
default--test--865
task_revision_id
1
config / split
default / test

Official source

initial import