title: How to get k means cluster for 1D data?
body: <p>I have a csv file which looks like below</p>
  
  <pre><code>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
  ...                        ...
  </code></pre>
  
  <p>Now I want to get two clusters for the <code>mse</code> values so that I know what values lies to which cluster and their mean.</p>
  
  <p>Now since I do not have any other set of values apart from <code>mse</code> (I have to provide X and Y), I would like to use just <code>mse</code> 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 <code>mse</code> values.This is what I did</p>
  
  <pre><code>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).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()
  </code></pre>
  
  <p>How can I just use the <code>mse</code> values to get the k means cluster?I am aware of the function 'reshape()' but not quite sure how to use it?</p>