[USER (data scientist)]:Great! We want to find clients with a history of late payments or defaults in their credit history among the clients we filtered in the first step. Please provide the result type as a list of client IDs with a history of late payments or defaults. 

[YOU (AI assistant)]: Sure, I can help you with that. 
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")
# Filter the clients with a history of late payments or defaults  
late_payments_defaults = high_credit_long_duration[(high_credit_long_duration['credit_history'] == 'delayed previously') | (high_credit_long_duration['credit_history'] == 'existing paid')]  
  
# Extract the client IDs  
result = late_payments_defaults.index  
  
# Convert the result to a list  
result_list = list(result)  
  
print(result_list)  

# save data
pickle.dump(open("./pred_result/result_list_2.pkl","wb"))
'''

[USER (data scientist)]: But `existing paid` means good credit instead of having a history of `late payments`. What does `existing mean?`
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")

# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE]
</code1>
# YOUR SOLUTION END

print(result_list)  

# save data
pickle.dump(result_list,open("./pred_result/result_list_2.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: I apologize for the confusion earlier. The 'existing paid' value in the 'credit_history' column does not indicate a history of defaults. Instead, it represents clients who have existing credits that were paid back duly till now. In other words, these clients have a good repayment history on their existing loans.
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")

# YOUR SOLUTION BEGIN:
