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, I have a dataset called "credit_customers.csv" and I would like to analyze the distribution of credit_history, employment, age, and credit_amount. Can you help me with that?

[YOU (AI assistant)]: Sure, I can help you with that. First, let's start by loading the dataset using pandas. Here's the code to do that: 
'''
import pandas as pd 
from decision_company import read_csv_file

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
'''
[USER (data scientist)]: Great, now I want to create histograms for each of the mentioned variables. Please generate and display a set of subplots including bar charts for 'Credit History' and 'Employment' distributions, and histograms for 'Age' and 'Credit Amount' distributions in the 'credit_customers' dataset.

[YOU (AI assistant)]: Alright, let's create histograms for credit_history, employment, age, and credit_amount using matplotlib. Here's the code to do that :
'''
import pandas as pd  
import matplotlib.pyplot as plt  
from decision_company import read_csv_file, create_subplots, get_fig_from_df, count_unique_values, set_layout, save_plot, show_plots

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

# Plot histograms  
fig, axes = create_subplots(2, 2, figsize=(12, 8))  
  
get_fig_from_df(count_unique_values(credit_customers['credit_history']),kind='bar', ax=axes[0, 0], title='Credit History')  
get_fig_from_df(count_unique_values(credit_customers['employment']), kind='bar', ax=axes[0, 1], title='Employment')  
get_fig_from_df(credit_customers['age'],kind='hist', bins=20, ax=axes[1, 0], title='Age')  
get_fig_from_df(credit_customers['credit_amount'], kind='hist', bins=20, ax=axes[1, 1], title='Credit Amount')  

set_layout() 
save_plot('ref_result/subplots.png') 
show_plots()  
'''

[USER (data scientist)]: Hey, I need some help analyzing a dataset of credit customers. I want to find out how loan approval rates vary across different factors like credit history categories, employment statuses, age groups (specifically 40-59), and credit amount ranges. Can you help me with that? 

[YOU (AI assistant)]: Of course! Let's start by loading your dataset. Do you have a CSV file with the data? 

[USER (data scientist)]: Yeah, it's called "credit_customers.csv". 

[YOU (AI assistant)]: Awesome! Let's load the dataset using pandas. Here's the code for that: 
'''
import pandas as pd 
from decision_company import read_csv_file

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
'''
[USER (data scientist)]: Cool, thanks! Now, let's kick things off by figuring out the approval rates, you know, when the 'class' column is marked as 'good', for each credit history category. Please calculate and display  a Series of the approval rates for different credit histories in the 'credit_customers' dataset.

[YOU (AI assistant)]: Sure thing! Here's the code to calculate approval rates for credit history categories :
'''
import pandas as pd   
import pickle
from decision_company import read_csv_file, count_unique_values, bind_dataframe, pivot_a_level, fill_missing_values

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

# Calculate approval rates  
credit_history_group = bind_dataframe(credit_customers, 'credit_history')  
credit_history_counts = count_unique_values(credit_history_group['class'], normalize=True)  
approval_rates_credit_history = fill_missing_values(pivot_a_level(credit_history_counts), value=0)['good']    

print(approval_rates_credit_history)

'''

[USER (data scientist)]: Nice! Now, let's do the same for employment statuses and again approval refers to column `class` is `good`. Please calculate and display a Series of the approval rates for different employment statuses in the 'credit_customers' dataset.

[YOU (AI assistant)]: You got it! Here's the code to calculate approval rates for employment statuses :
'''
import pandas as pd   
import pickle
from decision_company import read_csv_file, count_unique_values, bind_dataframe, pivot_a_level, fill_missing_values

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
employment_group = bind_dataframe(credit_customers, 'employment')  
employment_counts = count_unique_values(employment_group['class'], normalize=True)  
approval_rates_employment = fill_missing_values(pivot_a_level(employment_counts), value=0)['good']  
 
print(approval_rates_employment)

'''

[USER (data scientist)]: Thanks! Now, how about the approval rates for the age group 40-59? Please calculate and display the approval rates for the middle-aged group (ages 41 to 58) in the 'credit_customers' dataset.

[YOU (AI assistant)]: No problem! Here's the code to calculate approval rates for the age group 40-59 :
'''
import pandas as pd   
import pickle
from decision_company import read_csv_file, count_unique_values, bind_dataframe, pivot_a_level, fill_missing_values, logical_and, filter_by_condition 

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")   
  
age_column = credit_customers['age'] 
middle_age_condition = logical_and(age_column > 40, age_column < 59)  
middle_age_customers = filter_by_condition(credit_customers, middle_age_condition)  
middle_age_group = bind_dataframe(middle_age_customers, 'age')  
middle_age_counts = count_unique_values(middle_age_group['class'], normalize=True)  
approval_rates_middle_age = fill_missing_values(pivot_a_level(middle_age_counts), value=0)['good']  
 
print(approval_rates_middle_age)

'''

[USER (data scientist)]: Awesome! Finally, let's figure out the approval rates for various credit amount ranges. Just remember, we'll be using bins of 2000 for the credit amounts. Please categorize the 'credit_amount' into specified ranges in the 'credit_customers' dataset, calculate and display a Series of the approval rates for these credit amount ranges focusing on 'good' class outcomes.

[YOU (AI assistant)]: Sure thing! Here's the code to calculate approval rates for credit amount ranges :
'''
import pandas as pd   
import pickle
from decision_company import read_csv_file, count_unique_values, bind_dataframe, pivot_a_level, fill_missing_values, make_bins 

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
credit_customers['credit_amount_range'] = make_bins(credit_customers['credit_amount'], bins=[0, 2000, 4000, 6000, 8000, 10000, 12000, 14000, 16000, 18000, 20000], include_lowest=True)  
credit_amount_group = bind_dataframe(credit_customers, 'credit_amount_range')  
credit_amount_counts = count_unique_values(credit_amount_group['class'], normalize=True)  
approval_rates_credit_amount = fill_missing_values(pivot_a_level(credit_amount_counts), value=0)['good']  
 
print(approval_rates_credit_amount)

'''

[USER (data scientist)]: Awesome! Now, I'd like to visualize these approval rates using bar charts. Please generate and display a set of bar charts on a single figure, showing the approval rates by credit history, employment status, for the middle-age group (ages 40-59), and by credit amount range in the 'credit_customers' dataset.

[YOU (AI assistant)]: Sounds good! Let's create bar charts for each category using matplotlib. Here's the code for that :
'''
import pandas as pd   
import matplotlib.pyplot as plt
from decision_company import read_csv_file, get_fig_from_df, set_layout, create_subplots, show_plots, save_plot

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")
  
# Plot bar charts  
fig, axes = create_subplots(2, 2, figsize=(12, 8))  
  
get_fig_from_df(approval_rates_credit_history, kind='bar', ax=axes[0, 0], title='Approval Rates by Credit History')  
get_fig_from_df(approval_rates_employment, kind='bar', ax=axes[0, 1], title='Approval Rates by Employment Status')  
get_fig_from_df(approval_rates_middle_age, kind='bar', ax=axes[1, 0], title='Approval Rates for Middle-Age Group (40-59)')  
get_fig_from_df(approval_rates_credit_amount, kind='bar', ax=axes[1, 1], title='Approval Rates by Credit Amount Range')  
  
set_layout()  
save_plot('ref_result/bar_chart.png')  
show_plots() 
  
'''

[USER (data scientist)]: Hey, I've got this dataset of credit customers, and I'm trying to see how loan approval rates are related to factors like credit history, employment status, age, and credit amount. Can you help me out? 

[YOU (AI assistant)]: Absolutely! Let's start by loading your dataset. Is it in a CSV file? 

[USER (data scientist)]: Yeah, it's called "credit_customers.csv". 

[YOU (AI assistant)]: Cool! Let's load it up using pandas: 
'''
import pandas as pd 

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Nice one! Now, I need to turn the 'class' column into binary values, like 1 for 'good' and 0 for 'bad'. Please create and display a Series of a binary representation of the 'class' column in the 'credit_customers' dataset.

[YOU (AI assistant)]: No problem! Let's convert the 'class' column using a lambda function :
'''
import pandas as pd   
import pickle
from decision_company import read_csv_file

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
credit_customers['class_binary'] = credit_customers['class'].apply(lambda x: 1 if x == 'good' else 0)  

print(credit_customers['class_binary'])

'''

[USER (data scientist)]: That's just what I needed. Next, I want to make a new DataFrame with numerical values for credit_history and employment. Please generate a DataFrame by converting the 'credit_history' and 'employment' columns in the 'credit_customers' dataset to numerical codes.

[YOU (AI assistant)]: Sure thing! Let's create a new DataFrame and convert those columns to numerical values :
'''
import pandas as pd   
import pickle
from decision_company import read_csv_file, df_copy, cast_to_a_dtype, categoricalIndex, categorical_codes

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")
  
# Create a new DataFrame with numerical values for credit_history and employment  
credit_customers_numerical = df_copy(credit_customers)  
credit_customers_numerical['credit_history'] = categorical_codes(categoricalIndex(cast_to_a_dtype(credit_customers_numerical['credit_history'], 'category')))
credit_customers_numerical['employment'] = categorical_codes(categoricalIndex(cast_to_a_dtype(credit_customers_numerical['employment'], 'category')))  
  
print(credit_customers_numerical)

'''

[USER (data scientist)]: Awesome! Now, let's make some scatter plots to visualize the correlations. How about we start with loan approval rates and credit history? Please generate and display a scatter plot to visualize the relationship between loan approval rates ('class_binary') and credit history ('credit_history') in the numerically encoded 'credit_customers' dataset.

[YOU (AI assistant)]: Sounds good! Let's create a scatter plot for loan approval rates vs. credit history using seaborn and matplotlib :
'''
import pandas as pd   
import matplotlib.pyplot as plt   
import seaborn as sns 
from decision_company import read_csv_file, create_subplots, scatter_fig_instance, set_plot_split_title, set_layout, show_plots, save_plot

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

# Plot scatter plots  
fig, axes = create_subplots(2, 2, figsize=(12, 8))  
  
scatter_fig_instance(credit_customers_numerical, 'credit_history', 'class_binary', axes[0, 0])  
set_plot_split_title(axes[0, 0], 'Loan Approval Rates vs. Credit History')  
  
set_layout()  
save_plot('ref_result/scatterplot_1.png')  
show_plots()

'''

[USER (data scientist)]: That's perfect! Now, let's do the same for loan approval rates and employment status. Please generate and display a scatter plot to visualize the relationship between loan approval rates ('class_binary') and employment status ('employment') in the numerically encoded 'credit_customers' dataset.

[YOU (AI assistant)]: You got it! Here's the scatter plot for loan approval rates vs. employment status :
'''
import pandas as pd   
import matplotlib.pyplot as plt   
import seaborn as sns 
from decision_company import read_csv_file, create_subplots, scatter_fig_instance, set_plot_split_title, set_layout, show_plots, save_plot

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

# Plot scatter plots  
fig, axes = create_subplots(2, 2, figsize=(12, 8))  
  
scatter_fig_instance(credit_customers_numerical, 'employment', 'class_binary', axes[0, 1])  
set_plot_split_title(axes[0, 1], 'Loan Approval Rates vs. Employment Status')  

set_layout()  
save_plot('ref_result/scatterplot_2.png')  
show_plots()

'''

[USER (data scientist)]: Thanks! Finally, let's create scatter plots for loan approval rates vs. age and credit amount. Please generate and display scatter plots to visualize the relationships between loan approval rates ('class_binary') and both 'age' and 'credit amount' in the 'credit_customers' dataset.

[YOU (AI assistant)]: Sure thing! Here are the scatter plots for loan approval rates vs. age and credit amount :
'''
import pandas as pd   
import matplotlib.pyplot as plt   
import seaborn as sns 
from decision_company import read_csv_file, create_subplots, scatter_fig_instance, set_plot_split_title, set_layout, show_plots, save_plot

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

# Plot scatter plots  
fig, axes = create_subplots(2, 2, figsize=(12, 8))  
  
scatter_fig_instance(credit_customers_numerical, 'age', 'class_binary', axes[1, 0])  
set_plot_split_title(axes[1, 0], 'Loan Approval Rates vs. Age')  
  
scatter_fig_instance(credit_customers_numerical, 'credit_amount', 'class_binary', axes[1, 1])  
set_plot_split_title(axes[1, 1], 'Loan Approval Rates vs. Credit Amount')  
  
set_layout()  
save_plot('ref_result/scatterplot_3.png')  
show_plots()

'''

[USER (data scientist)]: Hey, I need some help with a dataset called "credit_customers.csv". Can you help me load it?

[YOU (AI assistant)]: Absolutely! Let's start by loading the dataset using pandas: 
'''
import pandas as pd 

# Load the dataset 
credit_customers = pd.read_csv("credit_customers.csv") 
'''
[USER (data scientist)]: Awesome, thanks! Now, I want to look at specific customer segments: middle-aged folks aged 40-59, people with stable jobs, and those with good or excellent credit. Can you help me filter them? Please generate three DataFrames from the 'credit_customers' dataset for three distinct groups: middle-aged customers (ages 41 to 58), customers with stable employment ('>=7', '4<=X<7'), and those with good credit history ('existing paid', 'no credits/all paid', 'all paid').

[YOU (AI assistant)]: Sure thing! Let's filter the dataset based on the customer segments you mentioned :
'''
import pandas as pd   
import pickle
from decision_company import read_csv_file, logical_and, check_elements_in_list, filter_by_condition

# Load the dataset      
credit_customers = read_csv_file("credit_customers.csv")      
  
# Filter customer segments      
middle_aged_condition = logical_and(credit_customers['age'] > 40, credit_customers['age'] < 59)  
middle_aged_customers = filter_by_condition(credit_customers, middle_aged_condition)  
  
stable_employment_condition = check_elements_in_list(credit_customers['employment'], ['>=7', '4<=X<7'])  
stable_employment_customers = filter_by_condition(credit_customers, stable_employment_condition)  
  
good_credit_history_condition = check_elements_in_list(credit_customers['credit_history'], ['existing paid', 'no credits/all paid', 'all paid'])  
good_credit_history_customers = filter_by_condition(credit_customers, good_credit_history_condition)  
  
print(middle_aged_customers)

print(stable_employment_customers)

print(good_credit_history_customers)

'''

[USER (data scientist)]: Nice work! Now, how about calculating the loan approval rates for each of these segments? Please generate three Series representing the approval rates for middle-aged customers, customers with stable employment, and customers with good credit history in the 'credit_customers' dataset.

[YOU (AI assistant)]: You got it! Let's calculate the approval rates for each customer segment :
'''
import pandas as pd   
import pickle
from decision_company import read_csv_file, count_unique_values

# Load the dataset      
credit_customers = read_csv_file("credit_customers.csv")   
  
# Calculate approval rates      
approval_rates_middle_aged = count_unique_values(middle_aged_customers['class'], normalize=True)      
approval_rates_stable_employment = count_unique_values(stable_employment_customers['class'], normalize=True)      
approval_rates_good_credit_history = count_unique_values(good_credit_history_customers['class'], normalize=True)      
 
print(approval_rates_middle_aged)

print(approval_rates_stable_employment)

print(approval_rates_good_credit_history)

'''

[USER (data scientist)]: That's perfect! Now, can we create some pie charts to visualize these approval rates? Please generate and display a set of pie charts illustrating the loan approval rates for three different customer groups in the 'credit_customers' dataset: middle-aged customers (40-59 years), customers with stable employment, and customers with good or excellent credit history.

[YOU (AI assistant)]: Absolutely! Let's create pie charts for the loan approval rates of each customer segment :
'''
import pandas as pd   
import matplotlib.pyplot as plt
from decision_company import read_csv_file, logical_and, check_elements_in_list, filter_by_condition, count_unique_values, create_subplots, set_plot_split_title, set_layout, show_plots, save_plot, fetch_column

# Load the dataset      
credit_customers = read_csv_file("credit_customers.csv")  
  
# Plot pie charts      
fig, axes = create_subplots(1, 3, figsize=(18, 6))      
  
approval_rates_middle_aged.plot(kind='pie', ax=axes[0], autopct='%.1f%%', startangle=90, ylabel='')      
set_plot_split_title(axes[0], 'Loan Approval Rates for Middle-Aged Customers (40-59)')      
  
approval_rates_stable_employment.plot(kind='pie', ax=axes[1], autopct='%.1f%%', startangle=90, ylabel='')      
set_plot_split_title(axes[1], 'Loan Approval Rates for Customers with Stable Employment')      
  
approval_rates_good_credit_history.plot(kind='pie', ax=axes[2], autopct='%.1f%%', startangle=90, ylabel='')      
set_plot_split_title(axes[2], 'Loan Approval Rates for Customers with Good/Excellent Credit History')      
  
set_layout()     
save_plot('ref_result/pieplot.png')     
show_plots()

'''

[USER (data scientist)]: Hey there! I'm working on a project to predict loan approvals, and I could use some help. Can you assist me?

[YOU (AI assistant)]: Of course! I'd be happy to help you with your loan approval prediction project. We can use a logistic regression model for this task. Do you have a dataset that we can work with?

[USER (data scientist)]: Yeah, I've got a CSV file called "credit_customers.csv". 

[YOU (AI assistant)]: Awesome! Let's start by loading that dataset using pandas. Here's how you can do it: 
'''
import pandas as pd 
from decision_company import read_csv_file

# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
'''
[USER (data scientist)]: Cool, thanks! Now, I need to convert some categorical features to numbers. How do I do that? Please generate a DataFrame by encoding the 'credit_history', 'employment', and 'class' columns in the 'credit_customers' dataset using a label encoder.

[YOU (AI assistant)]: No problem! You can use the 'LabelEncoder' from the 'sklearn.preprocessing' module to convert categorical features to numerical values. Here's how :
'''
import pandas as pd   
from sklearn.preprocessing import LabelEncoder
import pickle
from decision_company import read_csv_file, encoder_instance, encode_column

# Load the dataset    
credit_customers = read_csv_file("credit_customers.csv")  
  
# Initialize a label encoder  
le = encoder_instance()  
  
# Convert categorical features to numerical values    
credit_customers['credit_history'] = encode_column(le, credit_customers['credit_history'])  
credit_customers['employment'] = encode_column(le, credit_customers['employment'])  
credit_customers['class'] = encode_column(le, credit_customers['class'])  
 
print(credit_customers)

'''

[USER (data scientist)]: Got it! Now, how do I separate the features and target variables? Please generate a DataFrame and a Series to represent the features and the target variable from the 'credit_customers' dataset.

My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd 
import pickle
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first
 
# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print(X)

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

print(y)

# save data
pickle.dump(y,open("./pred_result/y.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)]: You can simply select the columns you want to use as features and target variables using pandas. Here's how you can do that:
'''
import pandas as pd 
import pickle
from decision_company import read_csv_file, 

# please import the necessary private functions from decision_company first
 
# Load the dataset 
credit_customers = read_csv_file("credit_customers.csv") 
 
# YOUR SOLUTION BEGIN:
