[USER (data scientist)]: Hey, I've got this dataset of credit customers, and I want to see if there are any big differences in digital service preferences between younger customers (18-35 years old) and those with a good credit history compared to other groups. Can you help me out?

[YOU (AI assistant)]: Sure, what can I help?

[USER (data scientist)]: First, we need to create a new column to identify younger customers (aged 18-35). Please generate and display a new column of dataframe in the 'credit_customers' dataset to identify young customers (aged 18-35).
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
import scipy.stats as stats 
import pickle 
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print("credit_young_customer")
print(credit_customers['young_customer']) 

# save data
pickle.dump(credit_customers['young_customer'],open("./pred_result/credit_young_customer.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Here's the code to do that:
'''
import pandas as pd 
import scipy.stats as stats 
import pickle 
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
