Ex.15: Import the used_cars_small_prices.csv dataset in your notebook, structure it as a Pandas DataFrame and show the first 20 rows. In [ ] : \#your code goes here Ex.16: Merge the two datasets together so that the cars DataFrame now also captures the price for each car: In [ ] : Ex.17: Are there any cars with a price tag below 100 or above 300K ? If yes, remove these values as the law does not permit used vehicles to be sold above 300K. In [ ] : \#your code goes here Ex.18: Group the cars by manufacturer and produce the mean, median, std, min and max as price as insights for each manufacturer: In [ ] : Ex.19: Extend the previous to include in the analysis for each manufacturer the stats for also the different fuel types: In [ ]: \#your code goes here Ex.20: The above seems messy, can you improve visibility with a pivot table? In [ ] : \#your code goes here