[USER (data scientist)]: Fantastic! Lastly, I'd like to see the credit history distribution of our customers. Please generate the code with the output in series type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd  
import pickle
from decision_company import read_csv_file, create_barplot, create_histogram, create_histogram_subplot, create_countplot, set_current_ticks, create_figure, set_plot_xlabel, create_figure, set_plot_title, set_yaxis_label, get_figure, configure_gridlines, show_plots, save_plot, fetch_column, avg, locate_mode, get_max, get_min_value, calculate_median, count_unique_values

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

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]   
</code1>
# 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"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: No problem! You can use the "value_counts()" function to get the credit history distribution. Here's the code:

# MY SOLUTION BEGIN:
