[USER (data scientist)]: Are there any significant differences in credit amount between younger customers and those with a good credit history compared to other customer segments? Please provide statistical tests (e.g., t-test or ANOVA) to compare credit amounts between the specified customer segments. 

[YOU (AI assistant)]:No problem! Here's the code to perform the test: 
'''
import pandas as pd  
import scipy.stats as stats  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# Create a new column to identify younger customers (aged 18-35)  
credit_customers['young_customer'] = (credit_customers['age'] >= 18) & (credit_customers['age'] <= 35)  
  
# Filter customers with good credit history  
good_credit_history = credit_customers['credit_history'] == 'existing paid'  
  
# Create groups based on young_customer and good_credit_history  
group1 = credit_customers.loc[credit_customers['young_customer'] & good_credit_history, 'credit_amount']  
group2 = credit_customers.loc[~credit_customers['young_customer'] & good_credit_history, 'credit_amount']  
group3 = credit_customers.loc[credit_customers['young_customer'] & ~good_credit_history, 'credit_amount']  
group4 = credit_customers.loc[~credit_customers['young_customer'] & ~good_credit_history, 'credit_amount']  
  
# Perform ANOVA test  
f_statistic, p_value = stats.f_oneway(group1, group2, group3, group4)  
  
print(f"F-statistic: {f_statistic}, P-value: {p_value}")  

# save data
pickle.dump(f_statistic,open("./ref_result/f_statistic.pkl","wb"))

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

[USER (data scientist)]: No, good credit history should also contain 'no credits/all paid', 'all paid'.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import scipy.stats as stats  
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(f"F-statistic: {f_statistic}, P-value: {p_value}")  

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

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: I am sorry, the correct code should be: 
'''
import pandas as pd  
import scipy.stats as stats  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
