[USER (data scientist)]:Hi Tapilot, I need your help to analyze the credit_customers dataset. Let's start with the first question: What is the distribution of age, employment status, and credit history among our customers in the credit_customers dataset? Specifically, you can generate DataFrame summaries and visualizations for the 'credit_customers' dataset, including mean, median, mode, and range of 'age', value counts of 'employment' and 'credit_history', and distribution plots for 'age', 'employment status', and 'credit history'.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import numpy as np  
import matplotlib.pyplot as plt  
import seaborn as sns  
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"Age - Mean: {age_mean}, Median: {age_median}, Mode: {age_mode}, Range: {age_range}") 

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

# save data
pickle.dump(age_median,open('./pred_result/age_median.pkl','wb'))

# save data
pickle.dump(age_mode,open('./pred_result/age_mode.pkl','wb'))

# save data
pickle.dump(age_range,open('./pred_result/age_range.pkl','wb'))
  
# YOUR SOLUTION BEGIN:
<code2>
[COMPLETE YOUR CODE]
</code2>
# YOUR SOLUTION END

print("\nEmployment Status Distribution:")  
print(employment_counts)  

# save data
pickle.dump(employment_counts,open("./pred_result/employment_counts.pkl","wb"))
  
# YOUR SOLUTION BEGIN:
<code3>
[COMPLETE YOUR CODE]
</code3>
# YOUR SOLUTION END

print("\nCredit History Distribution:")  
print(credit_history_counts)  

# save data
pickle.dump(credit_history_counts,open("./pred_result/credit_history_counts.pkl","wb"))
  
# Distribution plot for age  
plt.figure(figsize=(10, 6))  

# YOUR SOLUTION BEGIN:
<code4>
[COMPLETE YOUR CODE]
</code4>
# YOUR SOLUTION END

plt.savefig('pred_result/histplot.png')
plt.show()  
  
# Distribution plot for employment status  
plt.figure(figsize=(10, 6))  

# YOUR SOLUTION BEGIN:
<code5>
[COMPLETE YOUR CODE]
</code5>
# YOUR SOLUTION END

plt.savefig('pred_result/countplot.png') 
plt.show()  
  
# Distribution plot for credit history  
plt.figure(figsize=(10, 6)) 
 
# YOUR SOLUTION BEGIN:
<code6>
[COMPLETE YOUR CODE]
</code6>
# YOUR SOLUTION END

plt.savefig('pred_result/countplot_2.png') 
plt.show()

---END CODE TEMPLATE---

[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
  
# Load the dataset  
credit_customers = pd.read_csv("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
