[USER (data scientist)]:Hi Tapilot, I need your help to analyze the credit_customers dataset. What is the distribution of age, employment status, and credit history among our customers in the credit_customers dataset? Please provide mean, median, mode, range and distribution plots for age, employment status, and credit history.

[YOU (AI assistant)]:Sure, let's start by calculating the descriptive statistics for age, employment status, and credit history. Then, we'll create distribution plots for each of these variables.
'''
import pandas as pd  
import numpy as np  
import matplotlib.pyplot as plt  
import seaborn as sns 
import pickle 
from decision_company import read_csv_file, create_histogram, create_countplot, set_current_ticks, create_figure, set_plot_xlabel, create_figure, set_plot_title, set_yaxis_label, show_plots, save_plot, avg, locate_mode, get_max, get_min_value, calculate_median, count_unique_values

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# Descriptive statistics for age  
age_column = credit_customers['age']  
age_mean = avg(age_column)  
age_median = calculate_median(credit_customers, 'age')  
age_mode = locate_mode(age_column)[0]  
age_range = get_max(age_column) - get_min_value(age_column)  
  
print(f"Age - Mean: {age_mean}, Median: {age_median}, Mode: {age_mode}, Range: {age_range}")  
pickle.dump(age_mean,open("./ref_result/age_mean.pkl","wb"))
pickle.dump(age_median,open('./ref_result/age_median.pkl','wb'))
pickle.dump(age_mode,open('./ref_result/age_mode.pkl','wb'))
pickle.dump(age_range,open('./ref_result/age_range.pkl','wb')) 

# Descriptive statistics for employment status  
employment_column = credit_customers['employment'] 
employment_counts = count_unique_values(employment_column)  
  
print("\nEmployment Status Distribution:")  
print(employment_counts)  
pickle.dump(employment_counts,open("./ref_result/employment_counts.pkl","wb"))
  
# Descriptive statistics for credit history  
credit_history_column = credit_customers['credit_history']  
credit_history_counts = count_unique_values(credit_history_column)  
  
print("\nCredit History Distribution:")  
print(credit_history_counts)  
pickle.dump(credit_history_counts,open("./ref_result/credit_history_counts.pkl","wb"))

  
# Distribution plot for age  
create_figure((10, 6))  
create_histogram(data=credit_customers, x='age', kde=True, bins=20)  
set_plot_title("Age Distribution")  
set_plot_xlabel("Age")  
set_yaxis_label("Frequency")  
save_plot('./ref_result/histplot.png')  
show_plots()  
  
# Distribution plot for employment status  
create_figure((10, 6))  
create_countplot(data=credit_customers, x='employment', order=employment_counts.index)  
set_plot_title("Employment Status Distribution")  
set_plot_xlabel("Employment Status")  
set_yaxis_label("Frequency")  
save_plot('./ref_result/countplot.png')  
show_plots()  
  
# Distribution plot for credit history  
create_figure((10, 6))  
create_countplot(data=credit_customers, x='credit_history', order=credit_history_counts.index)  
set_plot_title("Credit History Distribution")  
set_plot_xlabel("Credit History")  
set_yaxis_label("Frequency")  
set_current_ticks(rotation=45)  
save_plot('./ref_result/countplot_2.png')  
show_plots()  
'''

[USER (data scientist)]: How many more customers have the mode age of 27 years compared to the median age of 33 years?
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(f"There are {difference} more customers with the mode age of 27 years than the median age of 33 years.")

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

---END CODE TEMPLATE---

[YOU (AI assistant)]:Sure,  here is the code:
'''
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:
