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DS-1000 / 864 / I have fitted a k-means algorithm on more than 400 samples using the python…

Problem

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Problem:

I have fitted a k-means algorithm on more than 400 samples using the python scikit-learn library. I want to have the 100 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()
</code>
closest_100_samples = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Sklearn

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Source and history

upstream_id / Original problem ID
864
task_id
db203b4e-f45c-5c5d-8bda-f66d82f447e2
task_key
default--test--864
task_revision_id
1
config / split
default / test

Official source

initial import