[USER (data scientist)]: That's super helpful. Now, I'd like to check out the employment status 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("\nEmployment Status Distribution:")  
print(employment_counts)  

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! You can use the "value_counts()" function to see the employment status distribution. Here's the code:

# MY SOLUTION BEGIN:
