compute columns based on multiple conditions

Question

I was reading a blog for conditaion based new computations where new col \'category\' is inserted.

data = {\'name\': [\'Jason\', \'Molly\', \'Tina\', \'Jake\', \         


        

Answer 1

You can using pd.cut (BTW , 40 is not old man :-()

pd.cut(df.age,bins=[0,20,39,np.inf],labels=['kid','young','old'])
Out[179]: 
0      old
1      old
2    young
3    young
4      old
Name: age, dtype: category
Categories (3, object): [kid < young < old]

Answer 2

For multiple conditions, you can just use numpy.select instead of numpy.where

import numpy as np

cond = [df['age'] < 20, df['age'].between(20, 39), df['age'] >= 40]
choice = ['kid', 'young', 'old']

df['category'] = np.select(cond, choice)
#    name  age  preTestScore  postTestScore category
#0  Jason   42             4             25      old
#1  Molly   52            24             94      old
#2   Tina   36            31             57    young
#3   Jake   24             2             62    young
#4    Amy   73             3             70      old