title: sklearn normalize() produces every value as 1
body: <p>I'm trying to normalize a single feature to [0, 1], but the result I'm getting back is all float values of 1 and is clearly wrong.  </p>
  
  <pre><code>import pandas as pd
  import numpy as np
  from sklearn.preprocessing import normalize
  
  test = pd.DataFrame(data=[7, 6, 5, 2, 9, 9, 7, 8, 6, 5], columns=['data'])
  normalize(test['data'].values.reshape(-1, 1))
  </code></pre>
  
  <p>This produces the following output:</p>
  
  <pre><code>array([[1.],
         [1.],
         [1.],
         [1.],
         [1.],
         [1.],
         [1.],
         [1.],
         [1.],
         [1.]])
  </code></pre>
  
  <p>I thought this might be an int to float datatype issue so I tried casting to float first, <code>normalize(test['data'].astype(float).values.reshape(-1, 1))</code>, but this gives the same result.  What am I missing?</p>