Q:$4($Answer$)$

import pandas as pd

def compute$\_$profit$\_$per$\_$dollar$\_$sold$($df$)$:

# Calculate profit per dollar sold

df$[\text{'}$profit$\_$per$\_$dollar$\_$sold'$]=$ df$[\text{'}$Profit$\text{'}]/$ df$[\text{'}$Sales$\text{'}]$

# Return the dataframe with the new column

return df

# Example of usage:

# Assuming df is your pandas dataframe with columns "Profit" and "Sales"

new$\_$df $=$ compute$\_$profit$\_$per$\_$dollar$\_$sold$($df$)$

Q$6$:

Objective: Filter data over relevant columns To understand how profit has evolved over time, create a function compute$\_$time$\_$average. This function receives as input a pandas dataframe and list of integers representing years, e$.$g$.,[2014,2015],$ and computes the mean profit$\_$per$\_$dollar$\_$sold for each year in the list. Repeat the same process to compute the mean Discount for each year. Return the result as tuple with two lists: $-$ the first with the mean profit$\_$per$\_$dollar$\_$sold per year, $-$ the second with the mean Discount per year Round the values to two decimal places. Note: the lists must be aligned so that mean$\_$profit$\_$per$\_$dollar$\_$sold$[1]$ and mean$\_$discount$[1]$ have the average for the year in list$\_$years$[1].$ Hint: There are different ways to compute this. A possible one requires you to filter the data in the dataframe for each year. You can use other methods also covered in Programming Inception. Example of usage: mean$\_$profit$\_$per$\_$dollar$\_$sold, mean$\_$values$\_$discount $=$ compute$\_$time$\_$average$($new$\_$df$,[2014,2015,2016,2017,2018]),-$ new$\_$df the pandas dataframe from question $4$; $-$ mean$\_$profit$\_$per$\_$dollar$\_$sold is a list with floats with the average profit$\_$per$\_$dollar$\_$sold for each year; $-$ mean$\_$discount is a list with floats with the average discount for each year in the list$\_$years;

Answer question $6$ with the help of questions $4$ Answer given above.