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DS-1000 / 897 / I have a csv file which looks like below

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Problem:

I have a csv file which looks like below

date                       mse
2018-02-11                 14.34
2018-02-12                 7.24
2018-02-13                 4.5
2018-02-14                 3.5
2018-02-16                 12.67
2018-02-21                 45.66
2018-02-22                 15.33
2018-02-24                 98.44
2018-02-26                 23.55
2018-02-27                 45.12
2018-02-28                 78.44
2018-03-01                 34.11
2018-03-05                 23.33
2018-03-06                 7.45
...                        ...
Now I want to get two clusters for the mse values so that I know what values lies to which cluster and their mean.

Now since I do not have any other set of values apart from mse (I have to provide X and Y), I would like to use just mse values to get a k means cluster.For now for the other set of values, I pass it as range which is of same size as no of mse values.This is what I did

from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

df = pd.read_csv("generate_csv/all_data_device.csv", parse_dates=["date"])
f1 = df['mse'].values
# generate another list
f2 = list(range(0, len(f1)))
X = np.array(list(zip(f1, f2)))
kmeans = KMeans(n_clusters=2, n_init=10).fit(X)
labels = kmeans.predict(X)
# Centroid values
centroids = kmeans.cluster_centers_
#print(centroids)

fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(X[:, 0], X[:, 1], c=labels)
ax.scatter(centroids[:, 0], centroids[:, 1], marker='*', c='#050505', s=1000)
plt.title('K Mean Classification')
plt.show()
How can I just use the mse values to get the k means cluster? I am aware of the function 'reshape()' but not quite sure how to use it?

A:

<code>
from sklearn.cluster import KMeans
df = load_data()
</code>
labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Sklearn

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Source and history

upstream_id / Original problem ID
897
task_id
d9fe9257-0162-5a49-b997-6e791f49e407
task_key
default--test--897
task_revision_id
1
config / split
default / test

Official source

initial import