How to do discretization of continuous attributes in sklearn?

Question

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)

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

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).

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())

Thanks !

Answer 1

The answer is no. There is no binning in scikit-learn. As eickenberg said, you might want to use np.histogram. Features in scikit-learn are assumed to be continuous, not discrete. The main reason why there is no binning is probably that most of sklearn is developed on text, image featuers or dataset from the scientific community. In these settings, binning is rarely helpful. Do you know of a freely available dataset where binning is really beneficial?