[USER (data scientist)]: Hey Tapilot, I need your help with some data analysis. I have a dataset of affordable housing sales over the past 5 years, and I want to find out the annual number of affordable properties sold, the average price of affordable properties, and the percentage change in these metrics for each year. Can you help me with the code to achieve this?  Please extract the year from the 'Date' column and add it to the 'melbourne_housing' DataFrame, then display and save this modified DataFrame as a pickle file, generating a DataFrame and a pickle file as outputs.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import matplotlib.pyplot as plt
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(affordable_housing_past_5_years)

# save data
pickle.dump(affordable_housing_past_5_years,open("./pred_result/affordable_housing_past_5_years.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, I can help you with that. First, let's create a new column 'Year' in your dataset to store the year of the sale date. Assuming your dataset is named affordable_housing_past_5_years and the sale date is stored in a column named 'Date', you can use the following code: 
