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 su

Answer 1

you could using pandas.cut method, like this:

bins = [0, 4, 10, 30, 45, 99999]
labels = ['Very_Low_Fare', 'Low_Fare', 'Med_Fare', 'High_Fare','Very_High_Fare']
train_orig.Fare[:10]
Out[0]: 
0     7.2500
1    71.2833
2     7.9250
3    53.1000
4     8.0500
5     8.4583
6    51.8625
7    21.0750
8    11.1333
9    30.0708
Name: Fare, dtype: float64

pd.cut(train_orig.Fare, bins=bins, labels=labels)[:10]
Out[50]: 
0          Low_Fare
1    Very_High_Fare
2          Low_Fare
3    Very_High_Fare
4          Low_Fare
5          Low_Fare
6    Very_High_Fare
7          Med_Fare
8          Med_Fare
9         High_Fare
Name: Fare, dtype: category
Categories (5, object): [High_Fare < Low_Fare < Med_Fare < Very_High_Fare < Very_Low_Fare]