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)]: Could you provide a histogram to illustrate the range of property prices in Flemington, Australia? Once we've examined this, could you explain what the Interquartile Range (IQR - Box Width) signifies in relation to land sizes in this data, and provide an estimate of its span?

A. Top 25%, 500 to 800 square meters
B. Middle 50%, 100 to 400 square meters
C. Bottom 25%, 50 to 150 square meters
D. Middle 75%, 200 to 600 square meters
E. None of above

[YOU (AI assistant)]: 