Highlights
Task
Task I: First, load the first dataset kfuisairr.esv" and create groups based on 'Firm name' using the groupie method. Tabulate the number of funds offered by each of the KiwiSavcr provider. the mean Tund_size . the median 'Fund rating' and the minimum and maximum 'Fee'. Report your findings in a table and discuss them.
In addition, sort the group based on 'Fee' from lowest to highest. Report the •Fund_id'. •Fund_name' and 'Fee' for the Iwo cheapest funds.
Task 2: In recent years, sustainability rating has been a big selling point for funds. We are interested to see which funds offer the most sustainable investments. To do this, load the second dataset "Suswinabilinirating.csv" and merge it with the lint tinsel Use the common column 'Sustainability_rating' to do this. Using the newly merged dataset. count how many funds have 'Above Average' for their 'Sustainability_calegory'. Who are the providers of these funds? Report your findings in a table and discuss them.
Task 3: Next, we are interested to see the growth of KiwiSaver funds (based on net asset value) over time. Load the third dataset "Nee asset and use dropna to remove observations where information on 'NAV' is not available. Left merge this dataset with the first dataset based on Convert the 'Date column to Datetime object and set this as the index for the merged dataframe. Using the newly merged dataframe. create a pivot table of
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