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 there! I've got this dataset called credit_customers, and I wanna find clients with high credit amounts and long loan durations.

[YOU (AI assistant)]: Absolutely! Let's break it down. First, we need to calculate the 75th percentile for credit_amount and duration. Need a hand with that? 

[USER (data scientist)]: Yeah, show me how to get the 75th percentile for both of those. Please calculate and display the 75th percentile values for 'credit_amount' and 'duration' in the 'credit_customers' dataset.

[YOU (AI assistant)]: No problem! Here's how you can calculate the 75th percentile for credit_amount and duration :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
credit_amount_75th = credit_customers['credit_amount'].quantile(0.75)   
duration_75th = credit_customers['duration'].quantile(0.75) 
  
print(f"credit_amount_75th: {credit_amount_75th}, duration_75th: {duration_75th}")

'''

[USER (data scientist)]: Cool! Now, how do I filter the dataset to keep only clients with amounts and durations higher than these values? Please generate a DataFrame representing customers in the 'credit_customers' dataset with credit amounts and durations greater than their respective 75th percentile values.

[YOU (AI assistant)]: You got it! Just use this code to filter the dataset based on the calculated values :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
high_credit_long_duration = credit_customers[(credit_customers['credit_amount'] > credit_amount_75th) & (credit_customers['duration'] > duration_75th)]   
  
print(f"high_credit_long_duration: {high_credit_long_duration}")  

'''

[USER (data scientist)]: Nice one! But I only need the credit_amount and duration columns from this filtered data. How do I get those? Please generate a DataFrame representing a filtered subset of the 'high_credit_long_duration' group in the 'credit_customers' dataset, including only the 'credit_amount' and 'duration' columns.

[YOU (AI assistant)]: Easy peasy! Use this code to extract the columns you need :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
high_credit_long_duration_filtered = high_credit_long_duration[['credit_amount', 'duration']] 

print(f"high_credit_long_duration: {high_credit_long_duration_filtered}")  

'''

[USER (data scientist)]: Sweet! Last thing, can you help me turn the result into a list of tuples? Please generate a list of tuples representing rows from a filtered DataFrame.

[YOU (AI assistant)]: Of course! Here's the code to convert the result into a list of tuples :
'''
import pandas as pd   
import pickle

# Load the dataset   
credit_customers = pd.read_csv("credit_customers.csv") 
  
result_list = list(high_credit_long_duration_filtered.itertuples(index=True, name=None))

print(f"high_credit_long_duration: {result_list}")  

'''

[USER (data scientist)]: The dataset's called 'high_credit_long_duration', and you'll find the credit history in the 'credit_history' column. Let's first filter out the clients with a history of late payments. How many clients have a history of late payments?

A. Unanswerable 
B. Answerable
C. Can be answered with basic assumptions
D. None of above

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)]: 