title: Use same Min and Max Data for Multiple Features in MinMaxScaler
body: <p>I have a dataset of 5 features. Two of these features are very similar but do not have the same min and max values.</p>
  
  <pre><code>... | feature 2 | feature 3 | ...
  --------------------------------
  ..., 208.429993, 206.619995, ...
  ..., 207.779999, 205.050003, ...
  ..., 206.029999, 203.410004, ...
  ..., 204.429993, 202.600006, ...
  ..., 206.429993, 204.25, ...
  </code></pre>
  
  <p><code>feature 3</code> is always smaller than <code>feature 2</code> and it is important that it stays that way after scaling. But since feature 2 and features 3 do not have the exact same <code>min</code> and <code>max</code> values, after scaling they will both end up having 0 and 1 as min and max by default. This will remove the relationship between the values. In fact after scaling, the first sample becomes:</p>
  
  <pre><code> ... | feature 2 | feature 3 | ...
  --------------------------------
   ...,  0.00268,   0.00279, ...
  </code></pre>
  
  <p>This is something that I do not want. I cannot seem to find a way to manually change the min and max values of <code>MinMaxScaler</code>. There are other ugly hacks such as manipulating the data and combining feature2 and feature 3 into one for the scaling and splitting again afterward. But I would like to know first if there is a solution that is handled by <code>sklearn</code>, such as using the same min and max for multiple features.</p>
  
  <p>Otherwise, the simplest workaround would do.</p>