title: Handling missing (nan) values on sklearn.preprocessing
body: <p>I'm trying to normalize data with missing (i.e. nan) values before processing it, using scikit-learn preprocessing.</p>
  <p>Apparently, some scalers (e.g. StandardScaler) handle the missing values the way I want - by which I mean normalize the existing values while keeping the nans - while other (e.g. Normalizer) just raise an error.</p>
  <p>I've looked around and haven't found - how can I use the Normalizer with missing values, or replicate its behavior (with norm='l1' and norm='l2'; I need to test several normalization options) some other way?</p>
  <pre><code>from sklearn.preprocessing import Normalizer, StandardScaler
  import numpy as np
  
  data = np.array([0,1,2,np.nan, 3,4])
  
  scaler = StandardScaler(with_mean=True, with_std=True)
  scaler.fit_transform(data.reshape(-1,1))
  
  normalizer = Normalizer(norm='l2')
  normalizer.fit_transform(data.reshape(-1,1))    
  </code></pre>