[USER (data scientist)]:For the sixth question, we want to find the count of clients in the final filtered dataset who reside in rented or owned housing, excluding those living rent-free. Please provide the result type as a count of clients residing in rented or owned housing. 

[YOU (AI assistant)]: Sure, I can help you with that. 

'''
import pandas as pd  
import pickle 
from decision_company import read_csv_file, fetch_column, logical_or, filter_by_condition, count_rows

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  

# Create conditions  
condition1 = fetch_column(high_credit_long_duration, 'housing') == 'rent'  
condition2 = fetch_column(high_credit_long_duration, 'housing') == 'own'  
  
# Perform logical OR operation  
final_condition = logical_or(condition1, condition2)  
  
# Filter the clients residing in rented or owned housing  
rented_owned_housing = filter_by_condition(high_credit_long_duration, final_condition)  
  
# Calculate the count of clients residing in rented or owned housing  
result_count = count_rows(rented_owned_housing)  
  
print(result_count)  

# save data
pickle.dump(result_count,open("./ref_result/result_count_2.pkl","wb"))
'''

[USER (data scientist)]: Shouldn't final dataset be `stable_employment`?
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import pickle 
from decision_company import read_csv_file, fetch_column, logical_or, filter_by_condition, count_rows

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  

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

print(result_count)

# save data
pickle.dump(result_count,open("./pred_result/result_count_2.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure.

# MY SOLUTION BEGIN:
