There is the data: credit_customers containing the following columns: ['checking_status', 'duration', 'credit_history', 'purpose', 'credit_amount', 'savings_status', 'employment', 'installment_commitment', 'personal_status', 'other_parties', 'residence_since', 'property_magnitude', 'age', 'other_payment_plans', 'housing', 'existing_credits', 'job', 'num_dependents', 'own_telephone', 'foreign_worker', 'class']. 
--- The description for each column this data is: 
Checking_status: Status of the applicant's checking account ("no checking": No checking account, "<0": Overdrawn or negative balance, "0<=X<200": Low positive balance, e.g., between $0 and $200, and ">=200": Healthy balance)
Duration: Duration of the loan or credit term (measured in months)
Credit_history: Applicant's past handling of credit ("no credits/all paid": No prior credit or all previous credits paid off, "existing paid": Current credits are being paid off diligently, "critical/other existing credit": Past or current credits with issues, like late payments, "delayed previously": History of delayed payments)
Purpose: Reason for the loan or credit application (purchasing a car, financing education, buying electronics ...etc)
Credit_amount: Amount of money requested in the loan or credit application
Savings_status: Amount of savings the applicant has ("no known savings": No savings account or negligible savings, "<100": Savings less than $100, "100<=X<500": Savings between $100 and $500, "500<=X<1000": Savings between $500 and $1000, and ">=1000": Savings of $1000 or more)
Employment: Applicant's employment status or the length of their current employment ("unemployed": No current employment, "<1": Employed for less than a year, "1<=X<4": Employed between 1 and 4 years, ">=7": Employed for 7 years or more)
Installment_commitment: Portion of the applicant's disposable income that is allocated for loan repayments (represented as a percentage or a fixed numerical value)
Personal_status: Details of the applicant's personal and demographic information ("male single": A single male, "female div/dep/mar": A divorced, separated, or married female, "male div/sep": A divorced or separated male, and "male mar/wid": A married or widowed male)
Other_parties: Any third parties involved in the credit agreement ("none": No other parties involved, "guarantor": A guarantor is present who guarantees repayment, and "co-applicant": Another individual is co-signing the loan)
Residence_since: Length of time the applicant has been living at their current residence (measured in years)
Property_magnitude: Type or value of property the applicant owns, serving as potential collateral ("real estate": Owning a house or land, "life insurance": Having a life insurance policy with a cash value, "car": Owning a car, and "no known property": No property that can be used as collateral)
Age: Age of the applicant
Other_payment_plans: Any other ongoing loans or financial obligations the applicant has outside of the loan in question ("none": No other payment plans or ongoing loans, "bank": The applicant has other loans or payment plans with a bank, and "stores": The applicant has credit obligations with stores or retail chains)
Housing: Living situation of the applicant ("own": The applicant owns their home, "rent": The applicant is renting their living space, and "for free": The applicant lives in a space without paying rent (e.g., with family))
Existing_credits: Number of current credit lines or loans that the applicant already has (represented numerically)
Job: Applicant's employment type ("skilled": Professionally skilled job or a white-collar occupation, "unskilled resident": Unskilled job and residing in the country, "high qualif/self emp/mgmt": Highly qualified, self-employed, or in management positions, and "unemp/unskilled non res": Unemployed, lacks professional or skilled training, and is not a resident of the country)
Num_dependents: Number of individuals financially dependent on the applicant
Own_telephone: Whether the applicant has a telephone
Foreign_worker: Whether the applicant is a foreign worker
Class: Target variable for a predictive model, indicating the risk category of the credit application ("good": Low risk, indicating a high likelihood of timely repayment, or "bad": High risk, suggesting potential difficulties in repaying the loan)
---
Considering contents from the dataset and requirements from user. Please note DO NOT CHANGE FILE AND VARIABLE NAMES THAT I HAVE SET!
Interactions begin:
[USER (data scientist)]: Hey, I need some help analyzing customer data. Can you give me a hand? 

[YOU (AI assistant)]:Of course! I'm here to help. What's the dataset like and what do you want to find out? 

[USER (data scientist)]: It's a CSV file named "credit_customers.csv". I want to start by looking at the age distribution of our customers. 

[YOU (AI assistant)]: No problem! Let's begin by loading the dataset with pandas. Here's the code: 
'''
import pandas as pd

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Awesome, now I'd like to get some basic stats for the age column, like mean, median, mode, and range. Please calculate and display the mean, median, mode, and range of the 'age' column in the 'credit_customers' dataset.

[YOU (AI assistant)]: You got it! Here's the code to calculate those stats :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Descriptive statistics for age   

age_mean = credit_customers['age'].mean()   

age_median = credit_customers['age'].median()   

age_mode = credit_customers['age'].mode()[0]   

age_range = credit_customers['age'].max() - credit_customers['age'].min()
  
print(f"Age - Mean: {age_mean}, Median: {age_median}, Mode: {age_mode}, Range: {age_range}")

'''

[USER (data scientist)]: Thanks a bunch! Can you also whip up a plot to show the age distribution? Please generate and display a histogram with a KDE (Kernel Density Estimate) plot for the age distribution in the 'credit_customers' dataset.

[YOU (AI assistant)]: Absolutely! Let's use seaborn and matplotlib to create a histogram for the age distribution. Here's the code :
'''
import pandas as pd   
import matplotlib.pyplot as plt   
import seaborn as sns

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Distribution plot for age  
plt.figure(figsize=(10, 6))   
sns.histplot(credit_customers['age'], kde=True, bins=20)   
plt.title("Age Distribution")   
plt.xlabel("Age")   
plt.ylabel("Frequency")   
plt.savefig('ref_result/histplot.png')
plt.show()  

'''

[USER (data scientist)]: That's super helpful. Now, I'd like to check out the employment status distribution of our customers. Please generate and display a series of the distribution of the 'employment' status values in the 'credit_customers' dataset.

[YOU (AI assistant)]: Sure thing! You can use the "value_counts()" function to see the employment status distribution. Here's the code :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
# Distribution plot for age  
employment_counts = credit_customers['employment'].value_counts()
print("\nEmployment Status Distribution:")  
print(employment_counts) 

'''

[USER (data scientist)]: Can you also create a plot to show the employment status distribution? Please generate and display a count plot for the distribution of employment statuses in the 'credit_customers' dataset.

[YOU (AI assistant)]: Of course! Here's the code to make a bar plot for the employment status distribution :
'''
import pandas as pd  
import matplotlib.pyplot as plt  
import seaborn as sns  
   
# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
   
# Distribution plot for employment status  
plt.figure(figsize=(10, 6))   
sns.countplot(x='employment', data=credit_customers, order=credit_customers['employment'].value_counts().index)   
plt.title("Employment Status Distribution")   
plt.xlabel("Employment Status")   
plt.ylabel("Frequency")   
plt.savefig('ref_result/countplot.png')  
plt.show()  

'''

[USER (data scientist)]: Fantastic! Lastly, I'd like to see the credit history distribution of our customers. Please generate and display a series of the distribution of credit history values in the 'credit_customers' dataset.

[YOU (AI assistant)]: No problem! You can use the "value_counts()" function to get the credit history distribution. Here's the code :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv")

credit_history_counts = credit_customers['credit_history'].value_counts()   
print("\nCredit History Distribution:")  
print(credit_history_counts)  

'''

[USER (data scientist)]: Can you also make a plot to show the credit history distribution? Please generate and display a count plot for the distribution of credit history categories in the 'credit_customers' dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to create a bar plot for the credit history distribution :
'''
import pandas as pd   
import matplotlib.pyplot as plt  
import seaborn as sns

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv")

# Distribution plot for credit history  
plt.figure(figsize=(10, 6))   
sns.countplot(x='credit_history', data=credit_customers, order=credit_customers['credit_history'].value_counts().index)   
plt.title("Credit History Distribution")   
plt.xlabel("Credit History")   
plt.ylabel("Frequency")   
plt.xticks(rotation=45)    
plt.savefig('ref_result/countplot_2.png') 
plt.show()  

'''

[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).

[YOU (AI assistant)]: Here's the code to do that :
'''
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)

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

'''

[USER (data scientist)]: Awesome, now let's sift out the customers who have a solid credit history. We can consider 'existing paid', 'no credits/all paid', and 'all paid' as indicators of good credit history. Please generate and display a Series indicating customers with a good credit history from the 'credit_customers' dataset.

[YOU (AI assistant)]: To filter customers with good credit history, we can use the following code :
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
good_credit_history = credit_customers['credit_history'].isin(['existing paid', 'no credits/all paid', 'all paid'])  
  
print(good_credit_history)  

'''

[USER (data scientist)]: Alright, now I want to create four groups based on whether they're young customers and have good credit history. Please create four groups of series based on combinations of young customer status and good credit history in the 'credit_customers' dataset.

[YOU (AI assistant)]: Alright, let's create the four groups as follows :
'''
import pandas as pd  
import pickle
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# 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']

'''

[USER (data scientist)]: Awesome, now let's do an ANOVA test to see if there are any significant differences between these groups. Please perform an ANOVA test to compare the credit amounts across four groups based on young customer status and good credit history in the 'credit_customers' dataset, display the F-statistic and P-value.

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---

Please note that you have to generate the WHOLE python code instead of code segments based on the code snippet using Pandas library 2.0.3 version and Matplotlib library 3.7.4 version. You must keep all comments in code snippet unchanged.

[YOU (AI assistant)]: To perform the ANOVA test, you can use the following code:
'''
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:
