[USER (data scientist)]: Great! Lastly, let's calculate the approval rates for different credit amount ranges. Please generate the code with the output in dataframe 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:
