[USER (data scientist)]: Hey, I've got this dataset called "affordable_housing" and I'm trying to figure out the top 10 suburbs and regions with the most affordable properties sold. Can you help me out with the code? Please generate two lists from the 'melbourne_housing' DataFrame to identify the top 10 most common suburbs and regions, then save these lists using pickle for future reference.  

[YOU (AI assistant)]: Of course! Let's start by finding the top 10 suburbs and regions based on the number of affordable properties sold. We can use the value_counts() method to count how many times each unique value appears in the 'Suburb' and 'Regionname' columns. Here's what you can do: 
 
'''
import pandas as pd 
import pickle  
  
# Load the dataset  
melbourne_housing = pd.read_csv("melb_data.csv") 

top_suburbs = affordable_housing['Suburb'].value_counts().head(10)  
top_regions = affordable_housing['Regionname'].value_counts().head(10)   

print(top_suburbs) 

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

print(top_regions) 

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

[USER (data scientist)]: Oh, cool! But, you know what? I actually need those results as lists, not dataframes. Can you tweak the code for me? 
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd   
import pickle 
  
# Load the dataset  
fastfood = pd.read_csv("fastfood.csv")  

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

print(top_suburbs_list) 

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

print(top_regions_list) 

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

---END CODE TEMPLATE---

[YOU (AI assistant)]: No problem! You can easily convert the index of the resulting Series objects to lists using the tolist() method. Here's the updated code for you: 

 
 