title: How to do discretization of continuous attributes in sklearn?
body: <p>My data consists of a mix of continuous and categorical features. Below is a small snippet of how my data looks like in the csv format (Consider it as data collected by a super store chain that operates stores in different cities)</p>
  
  <pre><code>city,avg_income_in_city,population,square_feet_of_store_area,  store_type ,avg_revenue
  NY  ,54504            , 3506908   ,3006                       ,INDOOR    , 8000091
  CH  ,44504            , 2505901   ,4098                       ,INDOOR    , 4000091
  HS  ,50134            , 3206911   ,1800                       ,KIOSK     , 7004567
  NY  ,54504            , 3506908   ,1000                       ,KIOSK     , 2000091
  </code></pre>
  
  <p>Her you can see that avg_income_in_city, square_feet_of_store_area and avg_revenue are continuous values where as city,store_type etc are categorical classes (and few more which I have not shown here to maintain the brevity of the data).</p>
  
  <p>I wish to model the data in order to predict the revenue. The question is how to 'Discretizate' the continuous values using sklearn? Does sklearn provide any "readymade" class/method for Discretization of the continuous values? (like we have in Orange e.g Orange.Preprocessor_discretize(data, method=orange.EntropyDiscretization())</p>
  
  <p>Thanks !</p>