Q:

How to use KBinsDiscretizer to make continuous data into bins in Sklearn?

I am working on a ML algorithm in which I tried to convert the continuous target values into small bins to understand the problem better. Hence to make better prediction. My original problem is for regression but I convert into classification by making small bins with labels. 
I did as follow,
from sklearn.preprocessing import KBinsDiscretizer  
est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
s = est.fit(target) 
Xt = est.transform(s)

It shows a value error like below. Then I reshaped my data into 2D. yet I could not solve it.

ValueError: Expected 2D array, got 1D array instead:

from sklearn.preprocessing import KBinsDiscretizer

myData = pd.read_csv("train.csv", delimiter=",")
target = myData.iloc[:,-5]  # this is a continuous data which must be 
                        # converted into bins with a new column.

xx = target.values.reshape(21263,1)

est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
s = est.fit(xx) 
Xt = est.transform(s)

You can see my target has 21263 rows. I have to divide these into 10 equal bins and write it into a a new column in my dataframe. Thanks for the guidance.
P.S.: 
Max target value:185.0
Min target value:0.00021

A:

The mistake in your first attempt is you are giving the output of fit function into transform. .fit() returns the fitted model and not the input data. The correct way would be either of one of the below. 
from sklearn.preprocessing import KBinsDiscretizer  
est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
Xt = est.fit_transform(target) 

or 
from sklearn.preprocessing import KBinsDiscretizer  
est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
est.fit(target)
Xt = est.transform(target)

A:

Okay I was able to solve it. In any case I post the answer if anyone else need this in the future. I used pandas.qcut
target['Temp_class'] = pd.qcut(target['Temeratue'], 10, labels=False)

This has solved my problem. 

