[USER (data scientist)]: Hey AI, I've got this "stable_employment" dataset, and I'm trying to figure out if there's any connection between credit history, loan duration, and employment status. Can you lend me a hand? 

[YOU (AI assistant)]: Absolutely! First, let's make sure all the columns are numeric. Do you need help with that?  

[USER (data scientist)]: Yeah, how do I go about doing that? Please generate the code with the output in dataframe type.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
from sklearn.preprocessing import LabelEncoder    
import pickle
from decision_company import read_csv_file, df_copy, encoder_instance, encode_column, corr, avg, fetch_column, bind_dataframe

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

print(f"stable_employment_encoded: {stable_employment_encoded}")  

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: No problem! We can use label encoding to convert non-numeric columns to numeric values. Here's how you can do it:

# MY SOLUTION BEGIN:
