[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 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, get_fig_from_df, count_unique_values, set_layout, bind_dataframe, pivot_a_level, fill_missing_values, create_subplots, show_plots, save_plot, fetch_column, logical_and, filter_by_condition, make_bins

# Load the dataset   
credit_customers = read_csv_file("credit_customers.csv")   
  
# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]  
</code1>
# YOUR SOLUTION END

print(approval_rates_credit_amount)

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure thing! Here's the code to calculate approval rates for credit amount ranges:

# MY SOLUTION BEGIN:
