title: Can sklearn.preprocessing.KBinsDiscretizer with strategy='quantile' drop the duplicated bins?
body: <p>I used <code>sklearn.preprocessing.KBinsDiscretizer(n_bins=10, encode='ordinal')</code> to discretize my continuous feature. </p>
  
  <p>The strategy is <code>'quantile'</code>, by defalut. But my data distribution is actually not uniformly, like 70% of rows is 0. </p>
  
  <p>Then I got <code>KBinsDiscretizer.bins_edges=[0.,0.,0.,0.,0.,0.,0.,256.,602., 1306., 18464.]</code>. </p>
  
  <p>There're many duplicate bins. So, is there a method to drop the duplicates in KBinsDiscretizer's bins? </p>
  
  <p><code>KBinsDiscretizer</code> calculates the quantile of input. If the most samples of input are zero, the 10-quantiles will have multiple zeros. The result I expected is a discretizer with unique bins. For the example I mentioned, is <code>[0.,256.,602., 1306., 18464.]</code>.</p>