title: Preprocessing in scikit learn - single sample - Depreciation warning
body: <p>On a fresh installation of Anaconda under Ubuntu... I am preprocessing my data in various ways prior to a classification task using Scikit-Learn.</p>
  
  <pre><code>from sklearn import preprocessing
  
  scaler = preprocessing.MinMaxScaler().fit(train)
  train = scaler.transform(train)    
  test = scaler.transform(test)
  </code></pre>
  
  <p>This all works fine but if I have a new sample (temp below) that I want to classify (and thus I want to preprocess in the same way then I get</p>
  
  <pre><code>temp = [1,2,3,4,5,5,6,....................,7]
  temp = scaler.transform(temp)
  </code></pre>
  
  <p>Then I get a deprecation warning...</p>
  
  <pre><code>DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 
  and will raise ValueError in 0.19. Reshape your data either using 
  X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1)
  if it contains a single sample. 
  </code></pre>
  
  <p>So the question is how should I be rescaling a single sample like this?</p>
  
  <p>I suppose an alternative (not very good one) would be...</p>
  
  <pre><code>temp = [temp, temp]
  temp = scaler.transform(temp)
  temp = temp[0]
  </code></pre>
  
  <p>But I'm sure there are better ways.</p>