Question 3 Objectives: Perform simple exploratory data analysis Design computation logic and routines in Python Assess use of Python only and Python data structures to perform extract, load, transformation, and calculation operations Assess use of Pandas dataframes to perform extract, load, transformation and calculation operations Conduct visualization in an appropriate way (a) Load the CSV file obtained from Q1(c) to a Pandas dataframe and derive the answers for the same three questions in Q2(b) (10 marks) (b) Suppose you are a researcher who would like to discover data patterns in HDB resale transaction after 2017: (i) Design a function to find the top three towns of each month with the greatest number of transactions. (6 marks) (ii) Draw ONE (1) figure to show the median resale price in 2020 of each town. (6 marks) (iii) For the town with the greatest number of transactions in 2020, draw ONE (1) figure to visualize the median resale price per flat type or each month. (6 marks) (iv) Is there any correlation between the storey range and resale price? Draw ONE (1) figure to visualize the correlation. (6 marks) (v) Is there any correlation between the remaining lease in years and resale price? Draw ONE (1) figure to visualize the correlation. (6 marks) (vi) For Yishun, draw ONE (1) figure to visualize how was the median resale price being changed over time per flat type? (6 marks)