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)]:Hi Tapilot, let's start by answering the first question. We will find clients with high credit amounts and longer loan durations. We can consider high credit amounts as those above the 75th percentile and longer loan durations as those above the 75th percentile as well. Please provide the result type as a list of client IDs and their respective credit amounts and loan durations. Specifically, you can generate a list of tuples from the dataframe, where each tuple contains the index, credit amount, and duration for customers in the 'credit_customers' dataset who have both credit amounts and durations above the 75th percentile.

[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")

# Calculate the 75th percentile for credit_amount and duration  
credit_amount_75th = credit_customers['credit_amount'].quantile(0.75)  
duration_75th = credit_customers['duration'].quantile(0.75)  
  
# Filter the dataset based on the calculated values  
high_credit_long_duration = credit_customers[(credit_customers['credit_amount'] > credit_amount_75th) & (credit_customers['duration'] > duration_75th)]  
  
# Extract the required information (client IDs, credit amounts, and loan durations)  
result = high_credit_long_duration[['credit_amount', 'duration']]  
  
# Convert the result to a list of tuples  
result_list = list(result.itertuples(index=True, name=None))  
  
print(result_list) 

'''

[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. Specifically, you can generate a list to identify clients with a history of late payments from the 'credit_customers' dataframe and save it as a pickle file.

[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  
late_payments = high_credit_long_duration[high_credit_long_duration['credit_history'] == 'delayed previously']  
  
# Extract the client IDs  
result = late_payments.index  
  
# Convert the result to a list  
result_list = list(result)  
  
print(result_list)   

'''

[USER (data scientist)]:We want to identify clients who have multiple existing credits and high installment commitments (above the median value of the 'installment_commitment' column) among the clients filtered in the first step. Please provide the result as a list of client IDs with multiple existing credits and high installment commitments. Specifically, you can generate a list to identify client IDs from the 'credit_customers' dataframe who have more than one existing credit and an installment commitment above the median of the dataset, and store this list in a pickle file.

[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 multiple existing credits and high installment commitments
multiple_credits_high_commitment = high_credit_long_duration[(high_credit_long_duration['existing_credits'] > 1) & (high_credit_long_duration['installment_commitment'] > credit_customers['installment_commitment'].median())]
# Extract the client IDs
result = multiple_credits_high_commitment.index
# Convert the result to a list
result_list = list(result)

print(result_list) 

'''

[USER (data scientist)]:For the fourth question, we want to find the count of clients aged between 25 and 55 in the filtered dataset from the first step. Please provide the result type as a count of clients aged between 25 and 55. Specifically, you can generate a count (integer) and save it as a pickle file by filtering the 'high_credit_long_duration' dataframe for clients aged between 25 and 55 from the 'credit_customers.csv' dataset.

[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 aged between 25 and 55
clients_25_to_55 = high_credit_long_duration[(high_credit_long_duration['age'] >= 25) & (high_credit_long_duration['age'] <= 55)]
# Calculate the count of clients aged between 25 and 55
result_count = clients_25_to_55.shape[0]
  
print(result_count)

'''

[USER (data scientist)]: We want to find clients who are employed and preferably have stable employment among the clients we filtered in the first step. We can consider stable employment as those with employment durations of 4 years or more. Please provide the result type as a list of client IDs with stable employment. Specifically, you can generate a list to visualize the client IDs with stable employment (4 years or more) from the 'credit_customers' dataframe.

[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 stable employment (4 years or more)
stable_employment = high_credit_long_duration[(high_credit_long_duration['employment'] == '4<=X<7') | (high_credit_long_duration['employment'] == '>=7')]
# Extract the client IDs
result = stable_employment.index
# Convert the result to a list
result_list = list(result)

print(result_list)

'''

[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. Specifically, you can generate a dataframe filtering clients in the 'credit_customers' dataset who reside in rented or owned housing, and calculate the count of these clients, saving the result as a pickle file.

[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 residing in rented or owned housing  
rented_owned_housing = stable_employment[(stable_employment['housing'] == 'rent') | (stable_employment['housing'] == 'own')]  

# Calculate the count of clients residing in rented or owned housing  
result_count = rented_owned_housing.shape[0]  

print(result_count)

'''

[USER (data scientist)]:For the seventh question, we want to find the common characteristics of clients who may benefit from debt consolidation in the filtered dataset. Please provide a summary of common characteristics, such as average credit amount, average loan duration, and most common employment status. Specifically, you can generate a summary, including a dataframe and pickle files, to report on the common characteristics of the 'stable_employment' subset of the 'credit_customers' dataset, specifically focusing on the average credit amount, average loan duration, and most common employment status.

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 the summary of common characteristics
print("Average credit amount:", average_credit_amount)

# save data
pickle.dump(average_credit_amount,open("./pred_result/average_credit_amount.pkl","wb"))
print("Average loan duration:", average_loan_duration)

# save data
pickle.dump(average_loan_duration,open("./pred_result/average_loan_duration.pkl","wb"))
print("Most common employment status:", most_common_employment)

# save data
pickle.dump(most_common_employment,open("./pred_result/most_common_employment.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)]: Sure, I can help you with that.
'''
import pandas as pd  
import pickle 
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")

# YOUR SOLUTION BEGIN:
