You are an AI assistant that aids users in performing data analysis using Python and Pandas to find information. 
 
There is the data  
You have the data melbourne_housing contains the following columns: ['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG', 'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude', 'Longtitude', 'Regionname', 'Propertycount'].
--- The description for each column this data is:
Suburb: Name of the suburb where the property is located (Abbotsford, Airport West, Albert Park ...etc)
Address: Street address of the property (85 Turner St, 25 Bloomburg St, 5 Charles St ...etc)
Rooms: The number of rooms in the property
Type: Type of property (h: house, u: unit/apartment, t: townhouse)
Price: Sale price of the property (measured in dollars)
Method: Method of sale (S: Sold (property successfully sold), SP: Sold Prior (sold before auction), PI: Passed In (not sold at auction), VB: Vendor Bid (seller made a bid), and SA: Sold After (sold after auction))
SellerG: Name of the real estate agent or agency that facilitated the sale
Date: Date when the property sale was finalized (day/month/year)
Distance: Distance of the property from a significant central point (measured in kilometers)
Postcode: Postal code area of the property
Bedroom2: The number of bedrooms in the property
Bathroom: The number of bathrooms in the property
Car: The number of car parking spaces available with the property
Landsize: Size of the land on which the property is situated (measured in square meters)
BuildingArea: Size of the building itself (measured in square meters) 
YearBuilt: Year in which the property was constructed
CouncilArea: Local government area in which the property is located (Yarra, Moonee Valley, Port Phillip ...etc)
Latitude: Geographical latitude of the property
Longitude: Geographical longitude of the property
Regionname: Larger region or area that the property is part of (Northern Metropolitan, Western Metropolita, Southern Metropolitan ...etc)
Propertycount: The total number of properties in the suburb
---
Considering contents from the dataset and result types from user, you only need to generate codes and notations. 
Conversation begins:
[USER (data scientist)]: Hey, I'm working on a project about properties in Flemington, Australia, and I could use some help. I want to find out how old the properties are on average and if there are any old buildings that could be renovated for sustainability. Can you help me with that? 
 
[YOU (AI assistant)]: Of course! I'd be happy to help. Do you have a dataset with the property information, like when each property was built? 
 
[USER (data scientist)]: Yeah, I've got a pandas DataFrame called "melbourne_housing" with a column named "YearBuilt" that has the year each property was built. 
 
[USER (data scientist)]: Let's start by looking at just the properties in Flemington.  Please generate a DataFrame to filter and display properties located in Flemington from the 'melbourne_housing' dataset, and then serialize this filtered DataFrame for later use.

[YOU (AI assistant)]: Sure thing! Here's the code to filter the dataset to only include properties in Flemington :
'''
import pandas as pd   
import pickle

# Load the dataset
melbourne_housing = pd.read_csv("melb_data.csv")

# Filter the dataset to only include properties in Flemington
flemington_housing = melbourne_housing[melbourne_housing['Suburb'] == 'Flemington']

print(flemington_housing)

'''

[USER (data scientist)]: Cool, now let's figure out the mean, median, and quartiles of the property ages. Please generate values (mean, median, first quartile, and third quartile) for property ages in the 'flemington_housing' dataframe and serialize them as pickle files.

My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import pickle

melbourne_housing = pd.read_csv("melb_data.csv") 

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

print(mean_age)

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

print(median_age)

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

print(first_quartile )

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

print(third_quartile)

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

---END CODE TEMPLATE---

Please note that you have to generate the WHOLE python code instead of code segments based on the code snippet using Pandas library 2.0.3 version and Matplotlib library 3.7.4 version. You must keep all comments in code snippet unchanged.

[YOU (AI assistant)]: Alright, here's the code to calculate the mean, median, and quartiles of property ages in Flemington: 
