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)]:Can you filter the melbourne_housing dataset to only include properties in the suburb of Flemington, Australia? The result should be dataframe with columns ['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG', 'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude', 'Longtitude', 'Regionname', 'Propertycount']. Please generate a box plot visualization (matplotlib figure) to explore the distribution of land sizes in Flemington, Australia, in the 'melbourne_housing' dataframe, and calculate the average land size, saving it as a pickle file.

[YOU (AI assistant)]: Sure, I can help you with that. :
'''
import pandas as pd   
import pickle
import numpy as np
import matplotlib.pyplot as plt

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

flemington_housing = melbourne_housing[melbourne_housing['Suburb'] == 'Flemington']
flemington_land_sizes = flemington_housing['Landsize']
average_land_size = np.mean(flemington_land_sizes)
plt.boxplot(flemington_land_sizes)
plt.ylabel('Land Size')
plt.title('Distribution of Land Sizes in Flemington, Australia')

plt.savefig('./ref_result/box_chart.png')
plt.show()

print(f"Average land size in Flemington, Australia: {average_land_size:.2f} square meters")

'''

[USER (data scientist)]: What is the distribution of property prices in Flemington, Australia? Show me histogram plot of property prices to visualize the distribution. Please generate a histogram to visualize the distribution of property prices in Flemington, Australia, using the 'melbourne_housing' dataset.

[YOU (AI assistant)]: Sure, I can help you with that :
'''
import pandas as pd   
import pickle
import matplotlib.pyplot as plt

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

flemington_prices = flemington_housing['Price']
plt.hist(flemington_prices, bins='auto', edgecolor='black')
plt.xlabel('Property Prices')
plt.ylabel('Frequency')
plt.title('Distribution of Property Prices in Flemington, Australia')

plt.savefig('./ref_result/bar_chart.png')
plt.show()

'''

[USER (data scientist)]: What is the distribution of property prices in Flemington, Australia? Show me histogram plot of property prices to visualize the distribution. Please generate a box plot visualization to display the distribution of land sizes ('Landsize') for the Flemington area in the Melbourne housing dataset, and calculate the average land size, saving both the plot as a PNG file and the average land size as a pickle file.

[YOU (AI assistant)]: Sure, I can help you with that. :
'''
import pandas as pd   
import pickle
import numpy as np

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

flemington_land_sizes = flemington_housing['Landsize']
average_land_size = np.mean(flemington_land_sizes)
plt.boxplot(flemington_land_sizes)
plt.ylabel('Land Size')
plt.title('Distribution of Land Sizes in Flemington, Australia')

plt.savefig('./ref_result/box_chart.png')
plt.show()

print(f"Average land size in Flemington, Australia: {average_land_size:.2f} square meters")

'''

[USER (data scientist)]: What is the relationship between property prices, distance to the CBD, and access to public transportation in Flemington, Australia? Show me a table of correlation coefficients (Price vs. Distance, Price vs. Public Transportation Access). Please generate a DataFrame to analyze the correlation between 'Price' and 'Distance' in the 'melbourne_housing' dataset.

[YOU (AI assistant)]: Sure, I can help you with that. :
'''
import pandas as pd
import pickle

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

correlation_price_distance = flemington_housing[['Price', 'Distance']].corr()

print(correlation_price_distance)

'''

[USER (data scientist)]: Could you provide a histogram illustrating the range of property prices in Flemington, Australia? Once we've examined this, could you then determine the year in which the highest number of buildings required renovation?

A. 1970
B. 1976
C. 1973
D. 1979
E. None of above

[YOU (AI assistant)]: 